1. A system for assisting in disease reasoning, the system comprising:

a storage unit configured to store a multi-map;

an acquisition unit configured to acquire an initial evidence set including at least user basic information and user symptom information; and

a processing unit, comprising:

a symptom expansion module configured to expand the user symptom information based on the multigraph to obtain an expanded set of evidence and obtain an associated set of diagnoses based on the expanded set of evidence;

a probability calculation module configured to calculate a diagnosis condition probability for each diagnosis in the associated set of diagnoses relative to relevant evidence in the expanded set of evidence;

a completeness calculation module configured to calculate a contribution of each diagnosis in the associated set of diagnoses with respect to each relevant evidence in the expanded set of evidence and a sum of all relevant evidence normalized contributions of each diagnosis, thereby obtaining an information completeness for each diagnosis;

a predetermined relationship value calculation module configured to calculate a predetermined relationship value associated with the diagnostic condition probability and the information integrity; and

a diagnosis determination module configured to rank the calculated predetermined relationship values and output one or more diagnoses ranked top.

2. The system of claim 1, further comprising a human-machine interaction interface for receiving initial information input by a user, including initial evidence sets of user basic information and user symptom information, and a diagnostic interaction interface, further configured to receive the initial information in any form achievable by a user; and wherein the diagnostic interactive interface is for displaying to a user a diagnostic result, which includes one or more diagnoses.

3. The system of claim 1 or 2, wherein the processing unit further comprises:

a cold start clarification module configured to ask the user to add a new user symptom until a valid symptom is obtained when the initial set of evidence does not include any valid symptom.

4. The system of claim 3, wherein the diagnostic determination module can be further configured to compare all of the calculated predetermined relationship values to a certain threshold and activate an inference clarification process if none of the predetermined relationship values is greater than the certain threshold.

5. The system of claim 4, wherein the processing unit further comprises:

a problem domain module configured to obtain a problem domain based on a multi-map all symptoms associated with each diagnosis in the set of associated diagnoses to obtain a problem domain when the diagnosis determination module has determined that there are no predetermined relationship values that are greater than the certain threshold.

6. The system of claim 5, wherein the processing unit further comprises:

a differential diagnosis module configured to determine a query priority for each diagnosis in the problem domain based on the diagnosis condition probability for each diagnosis in the problem domain and its information integrity.

7. The system of claim 6, wherein the processing unit further comprises:

a clarification priority calculation module configured to determine a clarification priority for other effective symptoms associated with each disease in the problem domain based on the diagnostic conditional probability, information integrity, and the conditional probability of an effective symptom relative to the associated disease for each diagnosis.

8. The system of claim 7, wherein the processing unit further comprises:

a reasoning clarification module configured to determine which other effective symptom of which diagnosis to query for based on the query priority and the clarification priority, and feed back to the user to obtain positive evidence and/or negative evidence of the user for the other effective symptom and feed back the positive evidence and/or negative evidence to the symptom extension module.

9. The system of claim 7, wherein the processing unit further comprises:

a counting module configured to count a number of cycles of the inference clarification process and when the number is greater than 12, the diagnosis determination module terminates the activation of the inference clarification process and outputs one or more diagnoses with predetermined relationship values ranked top.

10. A storage medium storing instructions that, when executed, implement at least the following:

obtaining an initial evidence set comprising user symptom information;

expanding the user symptom information based on a multigraph to obtain an expanded set of evidence and obtaining an associated set of diagnoses based on the expanded set of evidence;

calculating a diagnosis condition probability for each diagnosis in the associated set of diagnoses relative to the relevant evidence in the expanded set of evidence;

calculating a contribution of each diagnosis in the set of associated diagnoses relative to each relevant evidence in the expanded set of evidence and a sum of all relevant evidence normalized contributions of each diagnosis, thereby obtaining an information integrity for each diagnosis;

calculating a predetermined relationship value associated with the diagnostic conditional probability and the information integrity; and

the calculated predetermined relationship values are sorted and the top ranked one or more diagnoses are output.

Background

On-line medical treatment is a new state generated by the combination of the current internet and the medical industry, and the research of the on-line medical treatment is gradually paid extensive attention by the management academia. The system predicts the name and probability of the disease of the patient according to the known data through strict and complex calculation, and gives a set of evidences which are expected to be clarified further and treatment suggestions for the patient, including but not limited to examination names (CT, X-ray and the like), doctor departments, critical information of the disease and the like.

At present, most of diseases that patients want to know are registered in hospitals, and then the patients communicate with doctors for many times and check to know the diseases that the patients may suffer from.

It is expected that the direct hospital registration communication has at least the following disadvantages:

1. due to the shortage of the existing medical resources, the problems of difficult registration and difficult medical visit exist;

2. the problem of hanging wrong numbers exists due to unclear understanding of own symptoms and incapability of reaching, so that precious time of patients and doctors is wasted;

3. some systems also make disease reasoning, but most of them use decision tree algorithm as core algorithm, but the disadvantages of decision tree are obvious, such as: ignoring the correlation between attributes, when the number of samples in each category is inconsistent, the information gain is biased to the characteristics of more numerical values, when the category is too many, the error is increased more quickly, and the like. These disadvantages result in great difficulty and poor results in handling of the sample.

Therefore, there is a need for a system for assisting disease reasoning that can reduce the medical resource shortage while providing high accuracy so that information of possible diseases can be accurately acquired in advance without going to a hospital and communicating with a doctor directly.

Disclosure of Invention

According to one aspect of the disclosure, the disclosure relates to a system for assisting in disease reasoning, the system comprising: a storage unit configured to store a multi-map; an acquisition unit configured to acquire an initial evidence set including at least user basic information and user symptom information; and a processing unit, the processing unit comprising: a symptom expansion module configured to expand the user symptom information based on a multi-graph to obtain an expanded set of evidence and obtain an associated set of diagnoses based on the expanded set of evidence; a probability calculation module configured to calculate a diagnosis condition probability for each diagnosis in the associated set of diagnoses relative to relevant evidence in the expanded set of evidence; a completeness calculation module configured to calculate a contribution degree of each diagnosis in the associated set of diagnoses with respect to each relevant evidence in the expanded set of evidence and a sum of all relevant evidence normalized contribution degrees of each diagnosis, thereby obtaining an information completeness for each diagnosis; a predetermined relationship value calculation module configured to calculate a predetermined relationship value associated with the diagnostic condition probability and the information integrity; and a diagnosis determination module configured to rank the calculated predetermined relationship values and output one or more top ranked diagnoses.

Preferably, the system further comprises a human-machine interaction interface and a diagnosis interaction interface, wherein the human-machine interaction interface is used for receiving initial information input by a user, the initial information comprises user basic information and an initial evidence set of user symptom information, and further the human-machine interaction interface is constructed to receive the initial information in any form capable of being realized by the user; and wherein the diagnostic interactive interface is for displaying to the user a diagnostic result, which includes one or more diagnoses.

Preferably, the processing unit further comprises a cold start clarification module configured to ask the user to add a new user symptom until a valid symptom is obtained when the initial set of evidence does not include any valid symptom.

Preferably, the diagnosis determination module can be further configured to compare all of the calculated predetermined relationship values with a certain threshold and to activate an inference clarification process in case none of the predetermined relationship values is greater than said certain threshold.

Preferably, the processing unit further comprises a problem domain module configured to obtain all symptoms associated with each diagnosis of the set of associated diagnoses to obtain a problem domain based on the multiple map when the diagnosis determination module has determined that there are no predetermined relationship values greater than the certain threshold.

Preferably, the processing unit further comprises a differential diagnosis module configured to determine a query priority for each diagnosis in the problem domain based on the diagnosis condition probability for each diagnosis in the problem domain and its information integrity.

Preferably, the processing unit further comprises a clarification priority calculation module configured to determine a clarification priority for other effective symptoms associated with each disease in the problem domain based on the diagnostic conditional probability, the information integrity and the conditional probability of the effective symptom relative to the associated disease for each diagnosis.

Preferably, the processing unit further comprises a reasoning clarification module configured to determine which other effective symptom of which diagnosis to query for based on the query priority and the clarification priority, and feed back it to the user to obtain positive evidence and/or negative evidence of the user for the other effective symptom and feed back the positive evidence and/or negative evidence to the symptom extension module and implement the loop calculation.

Preferably, the processing unit further comprises a counting module configured to count a number of cycles of the inferential clarification process, and when the number is greater than 12, the diagnosis determination module terminates the activation of the inferential clarification process and outputs one or more diagnoses with predetermined relationship values ranked higher.

According to another aspect of the disclosure, the disclosure relates to a storage medium storing computer instructions that, when executed, perform at least the following:

obtaining an initial evidence set comprising user symptom information;

expanding the user symptom information based on the multigraph to obtain an expanded set of evidence and obtaining an associated set of diagnoses based on the expanded set of evidence;

calculating a diagnosis condition probability for each diagnosis in the associated set of diagnoses relative to the relevant evidence in the expanded set of evidence;

calculating the contribution degree of each diagnosis in the associated diagnosis set relative to each relevant evidence in the expanded evidence set and the sum of all relevant evidence normalized contribution degrees of each diagnosis, thereby obtaining the information integrity degree for each diagnosis;

calculating a predetermined relationship value associated with the diagnostic condition probability and the information integrity; and

the calculated predetermined relationship values are ranked and the top ranked one or more diagnoses are output.

Drawings

Other significant features and advantages of the invention result from the following non-limiting description provided for illustrative purposes with reference to the following drawings, in which:

FIG. 1 shows a flow diagram of a flow performed by a system for assisting disease inference according to an embodiment of the invention;

FIG. 2 shows a flow diagram of a flow performed by a system for assisting disease inference according to another embodiment of the invention;

FIG. 3 illustrates a structural block diagram of a system for assisting disease inference according to an embodiment of the present invention; and

fig. 4 shows an overall architecture diagram of a system for assisting in disease reasoning according to an embodiment of the invention.

Detailed Description

The embodiment of the application relates to a technical scheme under the condition of computer application.

The process performed by the system for assisting in disease reasoning of the present application derives possible "diagnoses" and their probabilities from known "evidence" and gives a set of evidence that further "clarification" is desired. Therein, all evidence of a diagnosisMainly comprising the effective symptoms of the user and the population to which the user belongs, wherein e represents evidence and the subscript i represents the number of the related evidence, wherein 1 ≦ i ≦ r, and r represents the number of evidence elements included in the evidence set. All diagnoses related to some evidenceGenerally "disease," also includes disease groups or syndromes, and is within the scope of this applicationWithin, d represents a diagnosis and the subscript j represents the number of the relevant diagnosis, where 1 ≦ j ≦ w, w represents the number of diagnostic elements included in the diagnostic set. Wherein effective symptoms indicate symptoms directly or indirectly associated with the disease. Indirect association refers to evidence e_iAlthough not directly associated with disease in the knowledge map, evidence of increased further restriction conditions e_i' associated with disease. Thus, disease d_jCorresponding effective symptom setIs defined by the following relationship:

therefore, the effective symptom set corresponding to the total disease set D is

In the procedure performed by the system for assisting disease reasoning according to the present application, the following known information needs to be utilized and may be contained in a multiple graph as will be described and defined in detail below:

"disease-symptom" association information, i.e. "sensitivity" and "specificity" of the effective symptom to the disease, which respectively represent the conditional probability P (e) of the effective symptom to the disease_i|d_j) And the conditional probability P (d) of the disease for the effective symptom_j|e_i) (ii) a And

"disease-population" association information, i.e. the demographic characteristics of the disease or simply the incidence, which includes:

(a) incidence of disease in the general population, i.e. the prior probability of disease P (d)_i) And an

(b) Incidence of disease P (d) in a given population_j|e_i) Or the population distribution P (e) of patients_i|d_j)。

Albeit a priori probability of evidenceP(e_i) It is also applied in the course of disease reasoning, however, as will be appreciated, in practice the prior probabilities of a population may be obtained from information sources such as the national statistical bureau, while the prior probabilities of symptoms are generally unknown.

A bayesian network is a suitable expression for expressing the relationship between symptoms and corresponding diseases, wherein the nodes in the bayesian network represent random variables (events), which in the scope of the present invention are evidence (valid symptoms or belonging population e)_i) Or diagnosis (associated diseases d_j) And edges between nodes in the network represent associations between the nodes. It should be noted that in the context of disease rationale diagnosis, it is difficult to establish a complete bayesian network, since the correlation between certain effective symptoms is unknown, such as headache and back pain.

Although bayesian networks describe events and associations between events, in the context of disease reasoning, human-computer interactions are often passed on by concepts, while events tend to be compounded by multiple concepts, e.g., the symptom "low back pain" is compounded by two concepts "low back" and "pain". Therefore, a knowledge graph depicting concepts and relationships between concepts is also needed to enable efficient interaction. Concepts may have various relationships between them, for example, in the context of disease reasoning, such as defining a compound relationship that expresses between concepts/events, such as a contained-of, for example:

composite relationship (1): and

composite relationship (2):

in the composite relationship (1), there are both the term "lumbar pain" which is an event in the Bayesian network and the non-event concepts "lumbar" and "pain", and therefore, the term "lumbar pain" is used_{Location of a body part}"and" pain_{Kind of symptom}"is" waist pain_{Symptoms and signs}"is a constituent element of the above formula.

Whereas in compound relationship (2), it is actually a logical combination of two events, which may be referred to as a "combined event".

It should be appreciated that in addition to composite relationships, there are also probabilistic relationships among these events in a bayesian network. It should be understood that the probabilistic relationship of "population" to "diagnosis" should be included in the bayesian network.

P (lumbar pain-and-fever) 1, P (fever-lumbar pain-and-fever) 1

Further, in the bayesian network, when the probability of the conceptual probability relationship is 1, it is equivalent to the derivation relationship, that is,

to more intuitively represent the use of bayesian networks and knowledge-graphs in the present application, a multiple graph G ═ (V, R, I) is employed to represent both bayesian networks and knowledge-graphs, where:

-V＝{v_kthe vertex set is represented, and comprises evidence events such as effective symptoms and crowds, diagnosis events such as diseases, disease groups or syndromes, and non-event concepts such as parts, symptom types and symptom limits;

-R represents a set of relationships comprising:

(i) the probabilistic relationship between events may be, for example,when P (numbness-lumbar disc herniation of lower limbs) ═ 0.7, it is abbreviated as Further, when the relationship type is not specifically labeled between events, then there is a probabilistic relationship between events by default, i.e., the relationship is not labeled between events<v₁→v₂>；

(ii) Part-whole relationships (part-of) between concepts of the same kind, e.g.

(iii) Composite-of the compound relationship between concepts, as described previously;

(iv) the superior-inferior relation (kinof) between concepts of the same kind, e.g. When the concepts are all event types, then the context also means the derivation of the relationship, i.e.Or

From the above description, the relationship set R can be represented as a conceptual relationship set R_KSet of relationships with probability R_pOr a union of (i), i.e. R ═ R_K∪R_P。

-I represents a correlation function between concepts or events. For example, for two events v₁，v₂If the relation R ∈ R therebetween_PThe association of these two events with the corresponding relationship r can be denoted as i (r) ═ v₁，v₂) And the weight of the relation r is defined as the conditional probability P (upsilon)₂|υ₁)。

A combined evidence consists of two or more evidences in a "and" or "relationship. It is envisaged that the majority of specific symptoms are "evidence-of-harmony", for example, "lumbar pain and numbness of the lower extremities" are specific symptoms of "lumbar disc herniation".

As understood, the symptoms are classified into two categories of "specific symptoms" and "non-specific symptoms", in which specific symptoms, which means strong directivity to a single disease, are specific to P (d)_j|e_i) Typically greater than 0.5. However, it should be noted that the specific symptoms also have sensitivity, and that the sensitivity is not correlated with the specificity. In the clinic, symptoms of low sensitivity are less preferred than symptoms of high sensitivity in the actual interrogation process because they are not common. In fact, in actual clinics, symptoms with high sensitivity are often used for differential diagnosis, i.e., diseases with similar symptoms that are likely to be misdiagnosed are excluded.

In contrast, nonspecific symptoms are usually given in the form of sensitivity.

In order to reduce the introduced redundant information as much as possible, the conceptual relationship set R can be utilized in combination_KSet of probabilistic relationships R_pTo equivalently represent combined evidence without recording logical operations in node attributes.

As mentioned earlier, the AND relationship e between evidence₃＝e₁∧e₂Can be expressed asCorrespondingly, the OR relation e between the evidences₃＝e₁∨e₂Can be expressed asFor this AND relationship, when all child nodes are true, the derivation can be reversed, i.e., ifThenHowever, it is anticipated that the derivation between evidence may be ambiguous, for example, if e₁＝e₃∨e₄，e₂＝e₃∨e₅Can be easily determinedHowever, it is not limited toTherefore, to avoid ambiguity, it is necessary to introduce the concept relationship set RK for distinguishing to prevent ambiguity, i.e. when the concept relationship set RK is used for distinguishing

Then e can be determined₃＝e₁∧e₂Can further determine

To further illustrate the relationships between nodes in the multiple graph, some examples of node relationships within the scope of the present application are shown herein:

example one: symptom + limitation

Among them, the "lumbar pain" is composed of the following elements:

example two: symptom combination (and relationship)

Example three: symptom combination (or relationship)

Example four: disease group

It should be understood by those of ordinary skill in the art that the above description of the relationship between nodes in the multi-graph is merely exemplary and should not be construed as limiting the relationship of all nodes in the multi-graph of the present application.

Fig. 1 shows a flow chart of a procedure performed by a system for assisting disease inference according to an embodiment of the present invention. As shown in fig. 1, the flow executed by the system for assisting disease inference described in the present invention includes at least the steps described in detail below.

At step S100, an initial evidence set is obtained

The initial set of evidence includes at least user basic information and user symptoms. The user basic information includes at least basic group information such as sex and age of the user. The user symptoms include at least initial complaint symptoms.

At step S102, a user symptom is expanded based on the multigraph to obtain an expanded set of evidence and an associated set of diagnoses is obtained based on the expanded set of evidence.

It is envisioned that in this step, the user's symptoms, including the initial complaint symptoms, are expanded based on the multiple graph. Since there may be a complex-of relationship between symptoms, such as: and isTherefore, when the combined symptom of the waist pain and the fever is obtained, the simultaneous obtaining of the waist pain and the fever can be deduced according to the multiple graph theory, and therefore, the condition of the user can be expanded, and the condition that the reasoning result is not accurate enough due to the loss of evidence information can be prevented. After the user's symptoms are expanded based on the multigraph, the initial evidence set is updated to an expanded evidence set. Further, from the expanded set of evidence, a set of related diagnoses associated with the expanded set of evidence can be obtained from the multigraph. For example, when the symptom of lumbar pain and fever is expanded to lumbar pain and fever, a set of diagnoses (diseases) associated with lumbar pain and accordingly a set of diagnoses (diagnoses) associated with fever may be acquired based on the multiplogram, and therefore, based on the expansion of the symptom of lumbar pain and fever of the user, a set of diagnoses (diseases) associated with lumbar pain and a set of diagnoses (diseases) associated with fever may be actually acquiredThe union of the diagnoses (sets) is such that all diseases associated with the user's symptoms of low back pain and fever are well-accounted for without causing omissions.

Note that if there are two such evidences in the expanded set of evidenceThen e is removed from the expanded evidence set₂. The reason for this is that e is described as an event₁A certain ratio e₂Contains more information (complex relationships) and, thus, d for any diagnosis_jIf both existThen p is₁A certain ratio p₂More accurately expresses the probability relation of relevant evidence diagnosis (please note, p)₁Not necessarily greater than p₂) E.g. of The diagnosis using the former must be more accurate than the latter.

Further, note that the association with the corresponding disease has been added in advance for all the indications in the multiplet such thatIf not present<e_i→d_j>Then add a special probability relationship without weightAnd does not participate in subsequent diagnosis probability calculation and information integrity calculation.

At step S104, a diagnostic conditional probability is calculated for each diagnosis in the associated set of diagnoses relative to the relevant evidence in the expanded set of evidence.

As is apparent from the above description, symptoms can be classified into "specific symptoms" and "non-specific symptoms", and both have sensitivity regardless of specificity. Typically, non-specific symptoms are given and known in the form of sensitivity. However, in the scope of the system for assisting disease inference and the procedure performed thereby, since the inference direction is evidence → diagnosis, the sensitivity cannot be directly used in the process of assisting disease inference, and needs to be converted into quasi-specificity as will be described in detail below to calculate the diagnosis condition probability of each diagnosis with respect to the relevant evidence.

If evidence e is known_iA priori probability P (e) of_i) Then the evidence e_iFor diagnosis d_jThe specificity of (A) can be calculated according to a Bayesian formula as:

however, as previously mentioned, e for most symptoms (evidence)_iIn other words, its prior probability P (e)_i) Cannot be obtained in advance, and therefore can only be determined according to the symptom e_iThe quasi-specificity was calculated approximately from the set of all diseases associated:

wherein the normalization factor

In a fully modeled bayesian network, evidence synthesis is achieved through conditional probability tables. In the case where the joint probability distribution of each evidence cannot be obtained, the approximation calculation is performed as follows.

Evidence can be divided into two categories, one being positive evidence and one being negative evidence. Positive evidence means evidence that the posterior probability of diagnosis (disease) is greater than the prior probabilityThe set of which is denoted herein as E⁺(d_j)＝{e_i|P(d_j|e_i)＞P(d_i) }, abbreviated as E⁺. According to Bayesian formula, P (d)_j|e_i)＞P(d_i) Equivalent to P (e)_i|d_j)＞P(e_i)。

If so, theMeans diagnosis "not d_j". When the evidence is positive evidence e_i∈E⁺Timely diagnosis ofThe possibility ofIs a factor reduction. Further, assuming independence between the evidences, all of the evidences are usedThe probability drops to:

thus:

indeed, within the scope of the system for assisting in disease reasoning and the procedures performed thereby of the present application, positive evidence indicates that the user's symptoms are explicitly indicated to be present by the user.

Negative evidence (abbreviated as E) as opposed to positive evidence^-) Represents the following set of evidence:

E^-(d_j)＝{e_i|P(e_i|d_j)＜P(e_i)}＝{e_i|P(d_j|e_i)＜P(d_j)}

wherein for negative evidence e_i∈E^-In other words, it can be determined that the negative evidence is diagnosticThe penalty can be expressed as:

indeed, in the context of the system for assisting the reasoning on diseases and the procedures performed by it of the present application, negative evidence mainly represents several cases:

1. low prevalence populations, e.g., certain diseases (for) have low morbidity to young populations; and

2. a relatively low diseased gender, e.g., a male user is less likely to have osteoporosis; one particular case is a non-diseased gender, for example, a male user is unlikely to have a gynecological disease.

Within the scope of the present application, both positive evidence and negative evidence belong to positive evidence (which indicates the presence of relevant evidence).

In particular, among the user's symptoms, there is negative evidence of the user for certain symptomsFor example "no low back pain" can be used in the diagnostic conditional probability calculation by equating the negative evidence to negative evidence:

thus, for each diagnosis in the associated set of diagnoses, the diagnostic conditional probability relative to the relevant evidence in the expanded set of evidence can be expressed as:

wherein E ═ E⁺∪E^-A positive evidence set is represented and E' a negative evidence set.

Further, e is due to the majority of symptoms (evidence)_iIn other words, the lack of a priori probability P (e) thereof_i) Thus, the above formula can be further approximated as:

or:

whereinIs specific or quasi-specific as described above.

At step S106, the contribution of each diagnosis in the associated set of diagnoses with respect to each relevant evidence in the extended set of evidence and the sum of all extended evidence normalized contributions of each diagnosis are calculated, thereby obtaining the information integrity for each diagnosis.

The degree of contribution of each diagnosis with respect to the relevant evidence in the extended evidence set, i.e. the relevant evidence e in the extended evidence set_i(i.e., the current known relevant evidence in the expanded evidence set) for a diagnosis d in the correlated diagnosis set_jThe contribution to the integrity of information collection is:

C(e_i，d_j)＝|P(d_j|e_i)-P(d_j)|

the diagnosis d_jThe overall information integrity of (a) is the sum of the normalized information contribution of each relevant evidence in the extended evidence set:

wherein the normalization factorRepresenting d for a certain diagnosis in the set of associated diagnoses_jThe sum of the contribution of the diagnosis with respect to all evidence related to the diagnosis (all evidence related to the diagnosis obtained in the multiple graph) (i.e., the sum of the contribution of all relevant evidence related to the diagnosis in the multiple graph to the diagnosis), wherein the set of all evidence related to the diagnosisIncluding evidence of all "effective symptoms" and the "population" to which it pertains.

At step S108, a predetermined relationship value associated with the diagnosis condition probability and the information integrity is calculated. The predetermined relationship may be flexibly determined according to the different degrees of dependence on the diagnosis condition probability and the information integrity by the implementer of the system for assisting disease inference or the process thereof, for example, the predetermined relationship value may be determined as the weight sum of the diagnosis condition probability and the information integrity, and the like, but is not limited thereto. Further, it is to be noted that the predetermined relationship value may also be appropriately adjusted and determined in accordance with the following basic relationship: the higher the information integrity is, the higher the reliability of the current evidence for determining the diagnosis is proved to be, and the weight of the current diagnosis condition probability is correspondingly enhanced, so that the larger the predetermined relation value tends to be, and an exact conclusion is obtained.

At S110, the calculated predetermined relationship values are sorted and the top ranked one or more diagnoses are output.

As shown in fig. 2, in accordance with a preferred embodiment of the present application, cold start clarification is required given that the initial set of evidence obtained at step S100 may not include valid symptoms directly associated with the disease. Specifically, at optional step S112, it is determined whether the initial set of evidence obtained at step S100 includes directly valid symptoms. If including direct effectSymptom, the flow executed by the system for assisting disease inference proceeds to step S102. If it is determined that no directly effective symptom is included or an indirectly effective symptom is included, the step proceeds to step S114, where the user is asked to add a new user symptom until a directly effective symptom is obtained or the user is asked to add further restrictions on the indirectly effective symptom to obtain a directly effective symptom. In other words, if the initial evidence set entered by the user is determined to be the effective symptom set E of all diseases in the multiplexed graph_DNone intersect, the user is queried at step S114 to expand the initial evidence set until it intersects E_DUntil there is an intersection.

According to a preferred embodiment of the present application, a threshold value may be set for the predetermined relationship value so as to improve the accuracy of diagnosis. Specifically, after step S108, the predetermined relationship values may be compared to a threshold, and if any of the predetermined relationship values exists above a threshold, the diagnosis is deemed to be sufficiently accurate, and then proceed to step S110. However, if there is no predetermined relationship value greater than a certain threshold, the current diagnosis may be considered not to be completely accurate and the most likely diagnosis cannot be inferred, in which case further extensive clarification of the initial set of evidence currently acquired is required to enhance the accuracy of the diagnosis. Specifically, in the absence of any predetermined relationship value being greater than a certain threshold, the flow performed by the system for assisting in disease inference of the present application proceeds to steps S116-S122, which may also be referred to generally as inference clarification process steps.

At step S116, all symptoms associated with each diagnosis in the set of associated diagnoses, and all symptoms associated with each diagnosis, are obtained based on the multiple map to obtain a problem domain, wherein all diagnoses in the set of associated diagnoses and all symptoms associated with each diagnosis are referred to as the problem domain.

From the above definitions of problem domain and valid symptoms, it can be known that all the lower symptoms of one valid symptom are also in the problem domain, and thus the problem domain is just a markov blanket of valid symptoms. When there are multiple valid symptoms, the problem domain is the union of the Markov blankets for each valid symptom.

However, it should be noted that in acquiring symptoms associated with each diagnosis in the associated diagnosis set, only sensitive symptoms are acquired, and specific symptoms are not expanded.

At step S118, a query priority for each diagnosis in the associated diagnosis set is determined based on the diagnosis condition probability and the information integrity for each diagnosis in the associated diagnosis set, wherein the query priority is calculated according to the following relationship:

Y(d_j)＝α×P(d_j|E)+β×C(d_j)

wherein alpha and beta are preset parameters and satisfy alpha + beta as 1.

Sorting the current associated diagnosis set D by Y (o) from high to low

At step S120, a clarification priority is calculated for all symptoms in the problem domain, with evidence e being addressed_iThe clarification priority of (c) is calculated as follows:

at step S122, it is determined which other effective symptom of which diagnosis to query for based on the query priority and the clarification priority and the query is fed back to the user to obtain positive evidence and/or negative evidence of the user for the other effective symptom, and the positive evidence and/or negative evidence is fed back to step S102 to loop. It is envisioned that the diagnostic query with the highest query priority is preferentially directed to other effective symptoms with the highest clarification priority associated therewith.

It is noted that, preferably, at step S118, it is determined whether or not there is anySpecifically, where θ is a preset threshold, m is a tableAnd displaying the number of diagnoses greater than a preset threshold value theta to be selected, wherein m is greater than or equal to 1 and is less than or equal to z (z is any natural number). It is foreseen that z may be chosen to be 1. For example, when z is selected to be 1, then a determination is made as to whether there is oneWhen z is selected to be 2, then it is determined whether there are twoIn these cases where z is a certain number, only for d in step S122_jz(the z is a preset maximum) followed by the other diagnostic query with the greatest query priority for the other valid symptom with the highest clarification priority associated with that diagnosis. For example, when z is 1, then the other diagnostic with the second largest query priority is queried for the other valid symptom with the highest clarification priority associated with that diagnostic; when z is 2, then the other diagnostic with the third largest query priority is queried for the other valid symptoms associated with that diagnostic with the highest clarification priority, and so on. Of course, z may also represent an indeterminate number of occurrences. That is, z represents determinationAll diagnoses ofThe total number of (c). In this case, if a certain diagnosis is aimed atExist ofThe other diagnosis having the maximum question priority lower than the preset threshold value theta is asked only for the other effective symptoms having the highest clarification priority associated with the diagnosis in step S122.

In a preferred embodiment of the present application, an optional counting step may also be provided at a suitable location, which determines the number of times the above-mentioned cycle has been passed, and outputs one or more diagnoses with a predetermined relationship value ranked top after the number of cycles has exceeded a certain number, for example 12. It is conceivable to output a predetermined number of diagnoses, for example five.

Further, it is preferable that only the query efficiency of evidence granularity is actually considered in determining the other valid symptoms associated with the diagnosis with the highest clarification priority among the diagnoses that need to be queried, but the natural logic of human conversation may also be actually considered in translating into natural language employed by the flow performed by the system for assisting disease reasoning.

For example: (1) merging elements of the same type; for example, "lumbar pain" and "lumbar numbness" should be asked to "ask you whether your lumbar region has the following symptoms: A. pain, B, numbness; and (2) sequentially inquiring according to the chief complaint symptoms of the user; for example, if the patient enters "my low back pain and leg numbness", then the patient should ask questions about "low back pain", including the definition, degree, etc.; then, the question about "numbness of lower limbs" is asked.

For example, a problem history abstraction can be modeled as

Wherein the content of the first and second substances,the b-th query object indicating the a-th cycle includes, for example, "lumbar (symptom)", "lumbar pain (degree)". When the flow executed by the system for assisting disease inference according to the present application gives a set of evidence to be clarified for the n +1 th cycle, it should be compared with the historical questions, and preferably the same query target should be selected for querying. The comparison sequence is:

that is, the query objects in the same cycle clarify sequentially, and the query objects in different cycles prioritize the query clarification of the most recently queried to approximate the human conversation habit.

Further, preferably, the initial evidence set of the user expanded at step S114 may be directly generated by matching according to the knowledge graph of the user' S input part entering the multiple graph, for example, if the user inputs "waist", and relevant part symptom evidence such as "waist pain", "waist swelling" and the like is found in the matching, then the user is directly asked whether the waist has the above symptoms, and the user is allowed to directly select. Alternatively, the relevant question may be generated directly for the user to answer, for example, if the user enters "waist" and it is determined that there is no directly valid symptom, the user is directly asked "do you have a waist discomfort of 2". Of course, the aforementioned alternative or direct interrogation approaches may be used in combination.

As shown in fig. 3, fig. 3 illustrates a block diagram of a system for assisting disease inference according to an embodiment of the present invention. The system 3 for assisting disease inference according to the present application may comprise a storage unit 302, an acquisition unit 304 and a processing unit 306 as shown.

The storage unit 302 is configured to store a multi-map. The multi-graph simultaneously represents the Bayesian network and the knowledge graph and at least comprises known information such as effective symptoms, diseases associated with the effective symptoms first, disease-symptom associated information, disease-population associated information and the like.

The obtaining unit 304 is arranged to obtain an initial evidence set comprising at least the user basic information and the user symptom information as described before. Further, in an advantageous embodiment, the obtaining unit 304 may also be configured to ask the user for further information based on feedback from other units in the system.

The processing unit 306 is configured to process the information acquired by the acquisition unit 304 as described above. Preferably, the processing unit 306 may include a plurality of sub-modules, such as an evidence expansion module 3060, a probability calculation module 3061, a completeness calculation module 3062, a predetermined relationship value calculation module 3063, and a diagnosis determination module 3064.

The evidence expansion module 3060 is configured to expand the user symptom information acquired by the acquisition unit 304 based on the multigraph stored in the storage unit 302, as described above, thereby obtaining an expanded evidence set and an associated diagnosis set based on the expanded evidence set.

The probability calculation module 3061 is configured to calculate a diagnosis condition probability for each diagnosis in the associated set of diagnoses relative to the relevant evidence in the expanded set of evidence as previously described.

The completeness calculation module 3062 is configured to calculate the contribution of each diagnosis in the associated set of diagnoses with respect to each relevant evidence in the expanded set of evidence and the sum of all expanded normalized contributions of each diagnosis, as described above, thereby obtaining the information completeness for each diagnosis.

The predetermined relationship value calculation module 3063 is configured to calculate predetermined relationship values associated with the diagnostic conditional probabilities and the information integrity as previously described,

the diagnosis determination module 3064 is configured to sort the predetermined relationship values calculated by the predetermined relationship value calculation module 3063 as described above and output one or more diagnoses ranked top, such as five diagnoses, four diagnoses, three diagnoses, one diagnosis, and so on.

Optionally, the diagnosis determination module 3064 may be further configured to compare all of the calculated predetermined relationship values to a threshold, if any of the predetermined relationship values is greater than a threshold, sort the predetermined relationship values calculated by the predetermined relationship value calculation module 3063 and output one or more diagnoses ranked top; and if there are no predetermined relationship values greater than a certain threshold, an inference clarification process will be initiated. It is clearly indicated here that the process of inferential clarification represents the process of obtaining further evidence to further determine a diagnosis. In a preferred embodiment of the present application, the inference clarification process may involve processing by at least the problem domain module 3066, the differential diagnosis module 3067, the clearing priority calculation module 3068, and the inference clarification module 3069.

Optionally, the processing unit 306 may also include a cold start clarification module 3065. The cold start clarification module 3065 determines whether the initial set of evidence acquired by the acquisition unit 304 includes valid symptoms and, in the event that the acquired initial set of evidence does not include any valid symptoms, asks the user via the acquisition unit 304 to add new user symptoms until valid symptoms are acquired.

Optionally, the processing unit 306 may further comprise a problem domain module 3066 configured to obtain all symptoms associated with each diagnosis in the associated set of diagnoses based on the multiplet to obtain a problem domain as previously described when it is determined that there are no predetermined relationship values greater than a certain threshold.

Optionally, the processing unit 306 may further include a differential diagnosis module 3067 configured to determine a query priority for each diagnosis in the problem domain based on the diagnosis condition probability and the information integrity for each diagnosis in the problem domain as previously described.

Optionally, the processing unit 306 may further comprise a clarification priority calculation module 3068 configured to calculate a clarification priority for the symptoms for all symptoms in the problem domain as described previously.

Optionally, the processing unit 306 may further comprise a reasoning clarification module 3069 configured to determine which other effective symptom of which diagnosis to query for based on the query priority and the clarification priority and feed back the query to the user to obtain positive evidence and/or negative evidence of the user for the other effective symptom and feed back the positive evidence and/or negative evidence to the evidence expansion module 3060 and implement the loop calculation as described above. The inference clarification module 3069 may also be configured to compare the query priority of each diagnosis to a preset threshold and, if there is a diagnosis for which the query priority is greater than the preset threshold, determine other valid symptoms associated with the diagnosis having the highest clarification priority for other diagnosis queries having a maximum query priority below the preset threshold.

Alternatively, the processing unit 306 may comprise a counting module 3070 configured to count the number of cycles and, when the number is larger than 12, the diagnosis determination module 3064 terminates the activation of the inference clarification process and outputs one or more diagnoses, e.g. five diagnoses, four diagnoses, three diagnoses, one diagnosis, etc., with an earlier predetermined relationship value.

Advantageously, the system for assisting in disease reasoning within the scope of the present application further comprises a human-machine interaction interface for receiving initial information input by the user, comprising the user basic information and an initial evidence set of user symptom information. The human-machine interface is configured to receive user-initiated information in any form that a user may implement (e.g., voice input, text input, image recognition). By way of example and not limitation, the human-computer interaction interface may be embodied as a keyboard, a mouse, a touch screen, a joystick, a microphone, or any other hardware or combination thereof that can receive initial information input by a user.

Advantageously, the system for assisting in disease reasoning within the scope of the present application further comprises a diagnosis interactive interface for displaying the diagnosis result output by the system or feeding back to the user other indications of validity of the diagnosis determined by the system to be asked to obtain the user's response to the other symptoms of validity of the system to be clarified. Advantageously, the diagnostic interface is preferably a screen, for example in the form of a liquid crystal display, an organic light emitting diode or the like. It is contemplated that the diagnostic interface may also be output device hardware such as a voice announcement device, a projection device, or a combination thereof, by way of example and not limitation.

More advantageously, the human-machine interface and the diagnostic interface in a system for assisting in disease reasoning within the scope of the present application may be integrated. By way of example and not limitation, a touch screen may be an example of an integrated human-machine interface and diagnostic interface, for example. It is contemplated that other human-machine interface including a screen may be integrated with the diagnostic interface to perform the functions of both, such as a combination of a display and a keyboard (or other physical input device).

Advantageously, the storage unit 302 of the system for assisting disease reasoning in the scope of the present application may for example comprise a Memory, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic or optical disk, etc. or other hardware storage that can store data. Further, the storage unit 302 according to the present invention may include a database, a cloud storage, and the like. Further, the storage unit 302 may include any software program that may also store the procedures executed by the system for assisting disease inference of the present application.

As shown in fig. 4, fig. 4 is a general block diagram of a system for assisting disease inference according to an embodiment of the present invention, wherein the system for assisting disease inference generally includes at least the following components based on the same inventive concept: a processor 401, a memory 402, a communication interface 403, and a bus 404; the processor 401, the memory 402 and the communication interface 403 complete mutual communication through the bus 404; the communication interface 403 is used for implementing information interaction communication of the system for assisting disease reasoning and information transmission with other software or hardware; the processor 401 is adapted to invoke a computer program in the memory 402, which when executed implements the procedures performed by the system for assisting disease inference as described earlier in this application.

Based on the same inventive concept, yet another embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the procedures performed by the system for assisting disease inference as described previously in this application, and will not be described herein again.

In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the procedures executed by the system for assisting disease inference according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.

For the computer-readable storage medium provided by the embodiment of the present invention, the working principle and the beneficial effect of the computer program stored thereon are similar to those of the disease inference system provided by the above embodiment, and the specific contents and descriptions of the above embodiment are referred to, and the embodiment of the present invention will not be described in detail.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in software products, which can be stored in computer-readable storage media such as ROM/RAM, magnetic disks, optical disks, etc., and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute various embodiments or parts of embodiments.

It will also be appreciated that various modifications may be made in accordance with specific requirements. For example, customized hardware might also be used and/or particular elements might be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. For example, some or all of the disclosed systems for assisting in disease reasoning and the procedures performed thereby may be implemented by programming hardware (e.g., programmable logic circuitry including Field Programmable Gate Arrays (FPGAs) and/or Programmable Logic Arrays (PLAs)) in assembly languages or hardware programming languages such as VERILOG, VHDL, C + +, using logic and algorithms in accordance with the present disclosure.

It should also be understood that the processes performed by the aforementioned system for assisting in disease reasoning may be implemented in a server-client mode. For example, a client may receive data input by a user and send the data to a server. The client may receive data input by the user, perform a part of the processing in the flow executed by the system for assisting disease inference, and transmit the data obtained by the processing to the server. The server may receive data from the client and execute another part of the flow performed by the aforementioned system for assisting disease inference or the flow performed by the aforementioned system for assisting disease inference, and return the execution result to the client. The client may receive the execution results of the procedures performed by the system for assisting in disease reasoning from the server and may be presented to the user, for example, through an output device.

It should also be understood that the components of the system for assisting in disease reasoning can be distributed across a network. For example, some processes may be performed using one processor while other processes may be performed by another processor that is remote from the one processor. Other components of the system for assisting in disease reasoning may also be similarly distributed. In this way, the system for assisting in disease reasoning can be interpreted as a distributed computing system that performs processing at multiple locations.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.