CN112164460B - Intelligent disease auxiliary diagnosis system based on medical knowledge graph - Google Patents
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Abstract
The invention discloses an intelligent disease auxiliary diagnosis system based on a medical knowledge graph, which comprises a patient data layer, an entity extraction layer, an auxiliary diagnosis layer and a recommended treatment layer, wherein the patient data layer is used for collecting and storing patient data to form an electronic patient record, the entity extraction layer is used for carrying out entity identification and relation extraction according to patient complaints, the auxiliary diagnosis layer is used for carrying out auxiliary diagnosis analysis according to the result of the entity extraction layer to output a suspected disease diagnosis list and a corresponding recommended examination list, and the recommended treatment layer is used for carrying out analysis according to basic information of a patient, the result of an examination report and the diagnosis result of a doctor to output a final treatment scheme. The invention is beneficial to realizing intelligent auxiliary diagnosis of common diseases, providing auxiliary diagnosis with higher credibility for doctors, and helping to improve the diagnosis efficiency, accuracy and accurate medication of doctors.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an intelligent disease auxiliary diagnosis system based on a medical knowledge graph.
Background
With the continuous perfection of medical systems in China, medical resources including medical equipment and medical staff are gradually growing, but the conditions of shortage of medical resources and low operation efficiency of hospitals still exist, such as: the diagnosis and treatment level of the basic layer is low, and the false missing rate is up to 40%; the clinician time is not effectively utilized, and more than 20-50% of the time is used for text report entry; the data islands exist inside the hospital system and among the large medical databases, so that information is difficult to integrate, and a large amount of medical information is wasted.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent disease auxiliary diagnosis system based on a medical knowledge graph, a system and a system.
The invention adopts the following technical scheme:
the intelligent disease auxiliary diagnosis system based on the medical knowledge graph comprises a patient data layer, an entity extraction layer, an auxiliary diagnosis layer and a recommended treatment layer, wherein the patient data layer is used for collecting and storing patient data to form a patient electronic medical record, the entity extraction layer is used for carrying out entity identification and relation extraction according to patient complaints, the auxiliary diagnosis layer is used for carrying out auxiliary diagnosis analysis according to the result of the entity extraction layer so as to output a suspected disease diagnosis list and a corresponding recommended examination list, and the recommended treatment layer is used for carrying out analysis according to basic information of a patient, the result of an examination report and the diagnosis result of a doctor so as to output a final treatment scheme.
Further, the patient electronic medical record contains patient basic information including gender, height and weight, patient complaints, examination reports, diagnostic results and medication records.
Further, the entity extraction layer is composed of a data input module and a neural network module, wherein the data input module adopts manual input or voice input to generate a text by patient complaints, the neural network module is composed of a Bi-LSTM network and a CRF network, and an output result of the entity extraction layer is composed of disease types, disease symptoms and causes.
Further, the entity identification and relation extraction process is specifically as follows:
a1, word embedding is carried out on the text generated by the patient complaint, and word vectors are generated;
a2, carrying out named entity recognition, word segmentation and part of speech tagging on the word vectors by utilizing a Bi-LSTM network and CRF network joint model, and outputting corresponding entity recognition results, wherein the entity recognition results are noun subjects and non-noun words, and the noun subjects are disease symptoms;
and A3, performing label embedding and relation extraction on the entity identification result, and outputting a relation extraction result of the non-noun words, namely incentive.
Further, the auxiliary diagnosis layer comprises an input end, a deep learning model and an output end, wherein the input end is used for extracting the result of the entity extraction layer, the deep learning model is used for carrying out auxiliary diagnosis analysis on the data of the input end, and the output end is used for outputting a suspected disease diagnosis list and a corresponding recommended examination list.
Further, the construction process of the deep learning model specifically comprises the following steps:
b1, constructing a basic network by utilizing the relation between diseases and symptoms in the medical knowledge graph;
b2, performing incremental learning by using priori medical knowledge;
b3, constructing a Bayesian probability model by utilizing a public database, wherein the public database comprises CDC, pubMed and Stanford;
and B4, fusing the basic network and the Bayesian probability model through a linear model fusion technology.
Further, the recommended treatment layer comprises a treatment scheme generating module, a personalized recommendation module and a fusion output module, wherein the treatment scheme generating module is used for generating a preliminary treatment scheme according to the examination report result and the doctor diagnosis result, the personalized recommendation module is used for searching treatment modes of similar patient groups according to the patient basic information and the doctor diagnosis result to generate personalized recommendation, and the fusion output module is used for integrating the treatment scheme and the personalized recommendation by utilizing a linear model fusion technology to obtain a final treatment scheme.
Further, the final treatment regimen includes possible complications, prognosis, medication recommendations, and precautions.
Further, the preliminary treatment scheme generation process specifically includes:
c1, representing the clinical guideline as a decision tree, and translating the clinical guideline into executable rules;
and C2, applying rules to the results of the examination report and the diagnosis results of the doctor by adopting an inference engine to generate a preliminary quality scheme.
Further, the personalized recommendation generation process specifically includes:
d1, carrying out association rule analysis on clinical guidelines, medical knowledge pictures and clinical data to generate a common treatment mode;
and D2, finding out clinically similar patient groups according to the basic information of the patient and the diagnosis result of the doctor, and generating personalized recommendation by combining the treatment modes of the similar patient groups and the common treatment modes.
After the technical scheme is adopted, compared with the background technology, the invention has the following advantages:
the medical data of each large medical database is integrated by utilizing the medical knowledge graph, so that the problem of existing medical data island is solved, and the utilization rate of the medical data is improved; the intelligent auxiliary diagnosis of common diseases is realized, possible complications, prognosis, medication recommendation, attention points and the like are output, the auxiliary diagnosis with higher credibility is provided for doctors, and the diagnosis efficiency, accuracy and accurate medication of the doctors are improved; based on the deep learning technology, key information of patient complaints is automatically identified and extracted, an electronic medical record of the patient is established, the load of inputting the medical record by doctors is reduced, and unified and normative management of medical data is facilitated; in addition, doctor treatment can be standardized, and satisfaction of patients is improved.
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Fig. 1 is a schematic diagram of a system structure according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Examples
The intelligent disease auxiliary diagnosis system based on the medical knowledge graph comprises a patient data layer, an entity extraction layer, an auxiliary diagnosis layer and a recommended treatment layer, wherein the patient data layer is used for collecting and storing patient data to form a patient electronic medical record, the entity extraction layer is used for carrying out entity identification and relation extraction according to patient complaints, the auxiliary diagnosis layer is used for carrying out auxiliary diagnosis analysis according to the result of the entity extraction layer so as to output a suspected disease diagnosis list and a corresponding recommended examination list, and the recommended treatment layer is used for carrying out analysis according to basic information of a patient, the result of an examination report and the diagnosis result of a doctor so as to output a final treatment scheme.
The patient electronic medical record contains patient basic information including gender, height and weight, patient complaints, examination reports, diagnostic results and medication records.
The entity extraction layer is composed of a data input module and a neural network module, wherein the data input module adopts manual input or voice input to generate a text by patient complaints, the neural network module is composed of a Bi-LSTM network and a CRF network, and an output result of the entity extraction layer is composed of disease types, disease symptoms and causes.
The entity identification and relation extraction process comprises the following steps:
a1, word embedding is carried out on the text generated by the patient complaint, and word vectors are generated;
a2, carrying out named entity recognition, word segmentation and part of speech tagging on the word vectors by utilizing a Bi-LSTM network and CRF network joint model, and outputting corresponding entity recognition results, wherein the entity recognition results are noun subjects and non-noun words, and the noun subjects are disease symptoms;
and A3, performing label embedding and relation extraction on the entity identification result, and outputting a relation extraction result of the non-noun words, namely incentive.
The auxiliary diagnosis layer comprises an input end, a deep learning model and an output end, wherein the input end is used for extracting the result of the entity extraction layer, the deep learning model is used for carrying out auxiliary diagnosis analysis on the data of the input end, and the output end is used for outputting a suspected disease diagnosis list and a corresponding recommended examination list.
The construction process of the deep learning model specifically comprises the following steps:
b1, constructing a basic network by utilizing the relation between diseases and symptoms in the medical knowledge graph;
b2, performing incremental learning by using priori medical knowledge;
b3, constructing a Bayesian probability model by utilizing a public database, wherein the public database comprises CDC, pubMed and Stanford;
and B4, fusing the basic network and the Bayesian probability model through a linear model fusion technology.
The recommended treatment layer comprises a treatment scheme generation module, a personalized recommendation module and a fusion output module, wherein the treatment scheme generation module is used for generating a preliminary treatment scheme according to the examination report result and the doctor diagnosis result, the personalized recommendation module is used for searching treatment modes of similar patient groups according to the patient basic information and the doctor diagnosis result to generate personalized recommendation, and the fusion output module is used for integrating the treatment scheme and the personalized recommendation to obtain a final treatment scheme by utilizing a linear model fusion technology.
The final treatment regimen includes possible complications, prognosis, medication recommendations and precautions.
The primary treatment scheme generation process specifically comprises the following steps:
c1, representing the clinical guideline as a decision tree, and translating the clinical guideline into executable rules;
and C2, applying rules to the results of the examination report and the diagnosis results of the doctor by adopting an inference engine to generate a preliminary quality scheme.
The personalized recommendation generation process specifically comprises the following steps:
d1, carrying out association rule analysis on clinical guidelines, medical knowledge pictures and clinical data to generate a common treatment mode;
and D2, finding out clinically similar patient groups according to the basic information of the patient and the diagnosis result of the doctor, and generating personalized recommendation by combining the treatment modes of the similar patient groups and the common treatment modes.
In this embodiment, taking patient complain "recent urination and obesity" as an example, the entity extraction layer identifies "urination and obesity", and the suspected disease diagnosis list is obtained through auxiliary diagnosis layer analysis, for example: "type 1, 2 diabetes; 2. obesity; 3. diabetic retinopathy "and corresponding recommended exam list" 1, blood glucose test; 2. urine is regular; 3. visual acuity test "; and then the doctor gives out a final diagnosis result according to the examination result, and the recommended treatment layer analyzes according to the basic information of the patient, the result of the examination report and the diagnosis result of the doctor and outputs a final treatment scheme.
The probability model of the embodiment covers more than 500 diseases, and the diagnosis scheme of more than 30 diseases is common in the whole world, and the accuracy of diagnosis is 95%.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (6)
1. An intelligent disease auxiliary diagnosis system based on medical knowledge graph is characterized in that: the system comprises a patient data layer, an entity extraction layer, an auxiliary diagnosis layer and a recommended treatment layer, wherein the patient data layer is used for collecting and storing patient data to form a patient electronic medical record, the entity extraction layer is used for carrying out entity identification and relation extraction according to patient complaints, the auxiliary diagnosis layer is used for carrying out auxiliary diagnosis analysis according to the result of the entity extraction layer to output a suspected disease diagnosis list and a corresponding recommended examination list, and the recommended treatment layer is used for carrying out analysis according to basic patient information, the result of an examination report and the diagnosis result of a doctor to output a final treatment scheme;
the entity identification and relation extraction process comprises the following steps:
a1, word embedding is carried out on the text generated by the patient complaint, and word vectors are generated;
a2, carrying out named entity recognition, word segmentation and part of speech tagging on the word vectors by utilizing a Bi-LSTM network and CRF network joint model, and outputting corresponding entity recognition results, wherein the entity recognition results are noun subjects and non-noun words, and the noun subjects are disease symptoms;
a3, performing label embedding and relation extraction on the entity identification result, and outputting a relation extraction result of the non-noun words, namely incentive;
the entity extraction layer consists of a data input module and a neural network module, wherein the data input module adopts manual input or voice input to generate a text by inputting patient complaints, the neural network module consists of a Bi-LSTM network and a CRF network, and an output result of the entity extraction layer consists of disease types, disease symptoms and causes;
the auxiliary diagnosis layer comprises an input end, a deep learning model and an output end, wherein the input end is used for extracting the result of the entity extraction layer, the deep learning model is used for carrying out auxiliary diagnosis analysis on the data of the input end, and the output end is used for outputting a suspected disease diagnosis list and a corresponding recommended examination list;
the recommended treatment layer comprises a treatment scheme generation module, a personalized recommendation module and a fusion output module, wherein the treatment scheme generation module is used for generating a preliminary treatment scheme according to the examination report result and the doctor diagnosis result, the personalized recommendation module is used for searching treatment modes of similar patient groups according to the patient basic information and the doctor diagnosis result to generate personalized recommendation, and the fusion output module is used for integrating the treatment scheme and the personalized recommendation to obtain a final treatment scheme by utilizing a linear model fusion technology.
2. The medical knowledge graph-based intelligent disease auxiliary diagnosis system according to claim 1, wherein: the patient electronic medical record contains patient basic information including gender, height and weight, patient complaints, examination reports, diagnostic results and medication records.
3. The medical knowledge graph-based intelligent disease auxiliary diagnosis system according to claim 1, wherein: the construction process of the deep learning model specifically comprises the following steps:
b1, constructing a basic network by utilizing the relation between diseases and symptoms in the medical knowledge graph;
b2, performing incremental learning by using priori medical knowledge;
b3, constructing a Bayesian probability model by utilizing a public database, wherein the public database comprises CDC, pubMed and Stanford;
and B4, fusing the basic network and the Bayesian probability model through a linear model fusion technology.
4. The medical knowledge graph-based intelligent disease auxiliary diagnosis system according to claim 1, wherein: the final treatment regimen includes complications, prognosis, medication recommendations and precautions.
5. The medical knowledge graph-based intelligent disease auxiliary diagnosis system according to claim 1, wherein: the primary treatment scheme generation process specifically comprises the following steps:
c1, representing the clinical guideline as a decision tree, and translating the clinical guideline into executable rules;
and C2, applying rules to the examination report result and the doctor diagnosis result by adopting an inference engine to generate a preliminary treatment scheme.
6. The medical knowledge graph-based intelligent disease auxiliary diagnosis system according to claim 1, wherein: the personalized recommendation generation process specifically comprises the following steps:
d1, carrying out association rule analysis on clinical guidelines, medical knowledge pictures and clinical data to generate a common treatment mode;
and D2, finding out clinically similar patient groups according to the basic information of the patient and the diagnosis result of the doctor, and generating personalized recommendation by combining the treatment modes of the similar patient groups and the common treatment modes.
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| CN117012374B (en) * | 2023-10-07 | 2024-01-26 | 之江实验室 | Medical follow-up system and method integrating event map and deep reinforcement learning |
| CN117690604A (en) * | 2023-12-08 | 2024-03-12 | 浙江大学 | Diabetes health ventilating and teaching and medication recommending system based on large language model |
| CN117690549B (en) * | 2024-02-01 | 2024-05-17 | 中国中医科学院中医临床基础医学研究所 | Traditional Chinese medicine individuation intelligent prescription medicine recommendation system based on similar patient matching |
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