CN102110192A - Auxiliary disease judgment method based on diagnostic element data association - Google Patents
Auxiliary disease judgment method based on diagnostic element data association Download PDFInfo
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Abstract
The invention discloses an auxiliary disease judgment method based on diagnostic element data association, belonging to the field of information analysis and aid decision making. The auxiliary disease judgment method comprises the following steps: establishing a disease library according to clinical experience and expert data; establishing a symptom library by taking symptoms to which each disease model relates in the disease library to serve as the constitution elements of the symptom library; according to the symptom information of a patient, selecting the symptoms in the symptom library to serve as the symptom set of the patient; according to the selected symptoms, finding all diseases with the symptoms from the preset disease library; and listing as a disease list according to the decreasing correlation degree or suspected probability. In the method, limited known information can be furthest utilized based on the practical requirement on diagnosis and treatment on patients by doctors according to the step of diagnosing patients for doctors in the practical work, and the information can be added and revised to furthest simulate the real situation. The obtained information (contents, such as symptoms and the like) is fully simulated by utilizing a self-forming rule, thus the method is suitable for the complex situation of the randomness and dynamics of the information resource of the disease.
Description
Technical field
The present invention relates to a kind of disease auxiliary judgment method, the disease auxiliary judgment method of particularly utilizing data association to analyze belongs to information analysis and aid decision making field, can be used for the clinical diagnosis back-up system.
Background technology
Clinical diagnosis is one of the most basic medical profession behavior of medical institutions and doctor.If the medical worker can analyze patient's cause of disease in the very first time, determine possible therapeutic scheme, just can win golden hour, to rescuing patient's life, ensureing that the healthy aspect of the masses has very large positive role.Because clinical diagnosis process complexity, require the doctor to possess the comparison abundant clinical experience, some difficult and complicated cases particularly, present this clinical diagnosis experience all depends on doctor's personal story, also lacks actual assisting in diagnosis and treatment support.Therefore the auxiliary guide system that makes up clinic diagnosis increases the science of diagnosis to improving doctor's professional skill, and the cost that saves time accumulates various diagnosis and treatment experiences and has important practical significance.
Clinical assisting in diagnosis and treatment system be a kind of based on the information processing technology, incorporate the clinical diagnosis back-up system of artificial intelligence.Assisting in diagnosis and treatment flow process by native system, the clinician can obtain and every relevant information of diagnosis and content (doubtful disease, prescription, experience and lessons, relevant picture, video, text and document thereof etc.), seek the relevant knowledge to individual card clinical problem faster and more easily.
For example, establish certain disease F and 3 kinds of clinical manifestation E1～E3 may occur, in actual patient, just have following 7 kinds of situations: 1. E1, E2, E3; 2. E1, E2; 3. E1, E3; 4. E2, E3; 5. E1; 6. E2; 7. E3.Be that its complexity has:
Planting may.If by the principle of general diagnosis and treatment expert system create-rule, must describe with 7 rules, could guarantee can not omit information processing in the actual operation.But as j kind disease, when a n clinical manifestation occurs, and when j, n are enough big (as: n 〉=1000, j 〉=1000), approach infinity is big for its number of combinations sum, be that its complexity has unlimited kind possibility, general create-rule can not be described it comprehensively.
Summary of the invention
As the auxiliary support to medical worker's clinical diagnosis, purpose of the present invention mainly contains two, and the one, utilize the clinical disease knowledge and experience of long-term accumulation to make up clinic diagnosis disease knowledge storehouse; The 2nd, utilize the knowledge base that makes up, search algorithm by the foundation analysis and provide assistant service for doctor's clinical diagnosis.
Disease auxiliary judgment method based on the association of diagnosis factor data of the present invention comprises the steps:
Step 0: the set of the symptom clauses and subclauses of every kind of disease association (being the symptom set that every kind of disease shows, the symptom that set element may occur when suffering from this disease) is constituted a disease model α=(a according to clinical experience and expert data
1, a
2..., a
k..., a
m), wherein m is the number of the symptom that this disease showed; With the formation element of each disease model as the disease storehouse; The symptom that each disease model in the disease storehouse is related to is set up the symptom storehouse as the formation element in symptom storehouse; As preferably, the symptom library tape has the classification navigation;
Step 1: according to patient's symptom information, at first in the symptom storehouse, select 1 or 1 above symptom, as patient's symptom set E; As preferably, import patient's cardinal symptom earlier;
Step 2: in default disease storehouse, search the disease that all have these symptoms according to selected symptom, and be arranged as list of diseases from high to low according to the degree of correlation or doubtful probability;
Step 3: list of diseases is listed, will be classified as the symptom tabulation from high to low according to the frequency of occurrences with selected related other symptom of symptom in the step 1 simultaneously, comprise other symptom that the described list of diseases of step 2 relates to;
Step 4: the by-election symptom is added among patient's the symptom set E from the symptom tabulation that step 3 is listed; Perhaps directly selecting a disease model from list of diseases locatees in advance;
Step 5: if conduct new symptom a by-election, then in the list of diseases that step 3 is listed, search the disease that all have symptom among the current symptom set E, and be arranged as new list of diseases from high to low according to the degree of correlation or doubtful probability;
If selected a disease model from list of diseases, then other symptom of all of this disease that will prestore shows, and further selects for the user; If the user increases at this moment or deletes selected symptom, then return step 2, promptly cause the location and the screening of the doubtful disease of a new round; If the user no longer increases or deletes symptom, then proceed step 3;
Step 6: according to the doubtful disease that the user selectes, provide the relevant information of this disease, comprise the introduction of disease basic condition, needed inspection item and check index, conventional treatments, medication information etc.
The method that is arranged as list of diseases from high to low according to the degree of correlation or doubtful probability described in step 2 and the step 5 comprises the steps:
Step a: the symptom set E according to patient's current selected calculates the P that it treats each disease of ordering; Method is that this patient suffers from disease H
iProbability be:
P(H
i|β)＝[P(H
i|b
1)+P(H
i|b
2)+…+P(H
i|b
j)+…+P(h
i|b
n)]/n；
Wherein:
B wherein
jSpecific symptoms among the expression symptom set E, H
iRepresent specified disease to be sorted, P (H
i| b
j) symptom b appears for the patient
jThe time, the disease that takes a disease H
iProbability; P (b
j| H
i) be the patient disease H that takes a disease
iThe time symptom b appears
jProbability; P (H
i) be the patient disease H that takes a disease
iPrior probability, usually all be preset value according to the clinical medicine experience; D is the number of disease model to be sorted;
Step b: the probability that step a is obtained sorts from high to low, thereby obtains list of diseases.
The contrast prior art, beneficial effect of the present invention is, from the actual needs of doctor to the patient diagnosis treatment, step according to diagnosis patient in doctor's real work, maximally utilise limited Given information, can carry out appending and revising of information, the simulating reality situation of maximum possible.Employing has rule of one's own the information that obtains contents such as () symptoms is fully simulated, and is fit to the randomness of information source of disease and the complex situations of dynamic.
Description of drawings
The process flow diagram that Fig. 1 handles for assisting in diagnosis and treatment;
Fig. 2 is the graph of a relation of disease and symptom.
Embodiment
Below in conjunction with drawings and Examples content of the present invention is made an explanation, at first accompanying drawing 1 has provided implementing procedure figure of the present invention.
In this technical scheme, at first make up disease storehouse, symptom storehouse.Symptom and disease are the relations of multi-to-multi, the corresponding a plurality of diseases of each symptom, and the corresponding a plurality of symptoms of each disease, different symptom combinations may be caused by different diseases; Each disease can show one or several symptoms, sees accompanying drawing 2.Symptom is the principal character of doubtful disease.The symptom of being set up is the set of the subjective abnormal sensory of a series of functions, metabolism and the caused patient of morphosis ANOMALOUS VARIATIONS in the body in the lysis, as vomiting, pain, chilly etc.
Use assisting in diagnosis and treatment flow implementation example of the present invention:
Step 1: according to patient's descriptor, first-selected symptom E1, the E2 of selecting is as earlier selected symptom " waist is ached ", " hematuria ";
Step 2: in the disease storehouse, retrieve all and have the disease model of symptom E1, E2;
Step 3: all disease models that have symptom E1, E2 are listed, listed file names with possible other the related symptom of E1, E2 and select for the user;
Step 4: the user can replenish new symptom from the symptom tabulation of listing, to improve disease model; Also can directly select a disease locatees in advance;
Step 5: if the user has increased new symptom such as E3, E4, then can the generation of disease model be exerted an influence this moment, symptom is many more, and the completeness of disease model is high more, and is also many more according to the disease combination that has the rule generation of one's own; The probability that calculates acquisition according to Bayes is high more, and the seniority among brothers and sisters degree of doubtful disease is also high more;
After the selected disease, system also can list all symptoms under this disease, and this moment, the user can also be once more increases and decreases according to self actual conditions, so that make a definite diagnosis;
Step 6: list other medical knowledge and information with this disease association.The relevant information of described disease comprises the introduction of disease basic condition, needed inspection item and checks index, conventional treatments, the combination more than a kind or a kind in the medication information.
" with sick different disease and the same disease of different disease " all is the complex situations that run in clinical, is at certain concrete patient and the doctor needs diagnosis and treatment.In the process of search disease, can constantly simulate, utilize information that has obtained patient and the dynamic role of bringing into play doctor's thinking fully, improve the accuracy of net result.Utilize the present invention, the doctor can carry out appending and revising of information, and accumulation constantly and the experience of improving oneself also can absorb and inherit other experts' knowledge experience or intelligence system, makes doctor's thinking and logic infer the continuous rational distillation that obtains.Difference on inevitably understanding separately when also having avoided carrying out information interchange between many medical professionals and the computer professional simultaneously, reflect doctor's thoughtcast more truly and have good professional level, guaranteed professional, the reliability and stability of system.
Above-described specific descriptions; purpose, technical scheme and beneficial effect to invention further describe; institute is understood that; the above only is specific embodiments of the invention; and be not intended to limit the scope of the invention; within the spirit and principles in the present invention all, any modification of being made, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (4)
1. the disease auxiliary judgment method based on the association of diagnosis factor data is characterized in that, comprises the steps:
Step 0: the set of the symptom clauses and subclauses of every kind of disease association is constituted a disease model α=(a according to clinical experience and expert data
1, a
2..., a
k..., a
m), wherein m is the number of the symptom that this disease showed; With the formation element of each disease model as the disease storehouse; The symptom that each disease model in the disease storehouse is related to is set up the symptom storehouse as the formation element in symptom storehouse;
Step 1: according to patient's symptom information, at first in the symptom storehouse, select 1 or 1 above symptom, as patient's symptom set E;
Step 2: in default disease storehouse, search the disease that all have these symptoms according to selected symptom, and be arranged as list of diseases from high to low according to the degree of correlation or doubtful probability;
Step 3: list of diseases is listed, will be classified as the symptom tabulation from high to low according to the frequency of occurrences with selected related other symptom of symptom in the step 1 simultaneously, comprise other symptom that the described list of diseases of step 2 relates to;
Step 4: the by-election symptom is added among patient's the symptom set E from the symptom tabulation that step 3 is listed; Perhaps directly selecting a disease model from list of diseases locatees in advance;
Step 5: if conduct new symptom a by-election, then in the list of diseases that step 3 is listed, search the disease that all have symptom among the current symptom set E, and be arranged as new list of diseases from high to low according to the degree of correlation or doubtful probability;
If selected a disease model from list of diseases, then other symptom of all of this disease that will prestore shows, and further selects for the user; If the user increases at this moment or deletes selected symptom, then return step 2, promptly cause the location and the screening of the doubtful disease of a new round; If the user no longer increases or deletes symptom, then proceed step 3;
Step 6:, provide the relevant information of this disease according to the doubtful disease that the user selectes;
Wherein,
The method that is arranged as list of diseases from high to low according to the degree of correlation or doubtful probability described in step 2 and the step 5 comprises the steps:
Step a: the symptom set E according to patient's current selected calculates the P that it treats each disease of ordering; Method is that this patient suffers from disease H
iProbability be:
P(H
i|β)＝[P(H
i|b
1)+P(H
i|b
2)+…+P(H
i|b
j)+…+P(H
i|b
n)]/n；
Wherein:
B wherein
jSpecific symptoms among the expression symptom set E, H
iRepresent specified disease to be sorted, P (H
i| b
j) symptom b appears for the patient
jThe time, the disease that takes a disease H
iProbability; P (b
j| H
i) be the patient disease H that takes a disease
iThe time symptom b appears
jProbability; P (H
i) be the patient disease H that takes a disease
iPrior probability, usually all be preset value according to the clinical medicine experience; D is the number of disease model to be sorted;
Step b: the probability that step a is obtained sorts from high to low, thereby obtains list of diseases.
2. according to the described a kind of disease auxiliary judgment method of claim 1, it is characterized in that described symptom library tape has the classification navigation based on the association of diagnosis factor data.
3. according to the described a kind of disease auxiliary judgment method of claim 1, it is characterized in that, in step 1, import patient's cardinal symptom earlier based on the association of diagnosis factor data.
4. according to described any the disease auxiliary judgment method of claim 1-3 based on the association of diagnosis factor data, it is characterized in that, the relevant information of the described disease of step 6 comprises the introduction of disease basic condition, needed inspection item and checks index, conventional treatments, the combination more than a kind or a kind in the medication information.
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