CN112650860A - Intelligent electronic medical record retrieval system based on knowledge graph - Google Patents
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Abstract
The invention discloses an electronic medical record intelligent retrieval system based on a knowledge graph, which comprises: the data layer is used for acquiring medical record information data; the platform layer is used for processing data acquired by the data layer by using a big data platform and constructing a medical record central library; the knowledge map layer is used for crawling required medical data from the medical record central library by utilizing a crawler technology to generate a medical knowledge map; the entity extraction layer is used for carrying out entity identification and relation extraction on the knowledge graph layer according to the patient chief complaints and the patient characteristics; and the application layer is used for integrating diversified information of the patient, inputting the diversified information into the entity extraction layer for matching, and obtaining and outputting a retrieval result. The invention provides an electronic medical record intelligent retrieval system based on a knowledge graph, which has high retrieval intelligence and high speed, accurately obtains required data, filters unnecessary data, reduces the burden of a patient and the operation load of a hospital, and brings efficient and accurate medical experience to the patient.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an electronic medical record intelligent retrieval system based on a knowledge graph.
Background
With the continuous improvement of medical systems in China, medical resources including medical equipment and medical staff are gradually increased, but the situations of shortage of medical resources and low operating efficiency of hospitals still exist. The electronic medical record system adopts electronic equipment to store, manage, transmit and reproduce digitized medical records of patients, and replaces hand-written paper medical records. For hospitals and medical care personnel, the disease course record of patients has quite high scientific research value, is an important medical resource, and can provide the medical care personnel with the material for analyzing the previous cases, thereby further improving the diagnosis and treatment level. Therefore, the development of an intelligent retrieval system which is based on the electronic medical record archive and aims at a certain retrieval word has extremely high practical significance
Disclosure of Invention
The invention provides an electronic medical record intelligent retrieval system based on a knowledge graph, which has high retrieval intelligence and high speed, accurately obtains required data, filters unnecessary data, reduces the burden of a patient and the operation load of a hospital, and brings efficient and accurate medical experience to the patient.
In order to achieve the purpose, the invention adopts the following technical scheme:
an electronic medical record intelligent retrieval system based on knowledge graph comprises:
the data layer is used for acquiring medical record information data;
the platform layer is used for processing data acquired by the data layer by using a big data platform and constructing a medical record central library;
the knowledge map layer is used for crawling required medical data from the medical record central library by utilizing a crawler technology to generate a medical knowledge map;
the entity extraction layer is used for carrying out entity identification and relation extraction on the knowledge graph layer according to the patient chief complaints and the patient characteristics;
and the application layer is used for integrating diversified information of the patient, inputting the diversified information into the entity extraction layer for matching, and obtaining and outputting a retrieval result.
Preferably, the data sources of the data layer include HIS, EMR, LIS, and ACS.
Preferably, the medical record information data includes structured data, unstructured data and semi-structured data, the structured data includes outpatient medical records, diagnoses and medical orders, the unstructured data includes examination data, image data and voice data, and the semi-structured data includes hospitalized medical records.
Preferably, the big data platform comprises a data integration module, a data calculation module, a data analysis module, a data storage module and a platform support module.
Preferably, the medical record central library comprises a disease knowledge base, an examination and examination knowledge base, a symptom knowledge base, a medicine knowledge base, a body part knowledge base and an operation knowledge base.
Preferably, the step of the knowledge-graph layer crawling the required medical data from the medical record central library comprises the following steps:
a1, defining a crawler task through a definition module;
a2, a scheduling module reads a crawler task and acquires crawler resources based on a ZooKeeper module and a Redis module;
a3, processing a crawler task and crawler resources by a scheduling module, decomposing the task and sending the task to a crawler engine to directionally crawl information data of a medical record central library;
a4, crawling specific data from a medical record central library by a knowledge graph layer, and performing visual analysis on data of different dimensions based on a web analysis module to generate a knowledge graph.
Preferably, the entity extraction layer comprises a data input module and a neural network module, the data input module adopts manual input or voice input of patient characteristics and patient complaints to generate texts, the neural network module comprises a Bi-LSTM network and a CRF network, and the output result of the entity extraction layer comprises disease types, disease symptoms and causes.
Preferably, the process of entity identification and relationship extraction is specifically as follows:
b1, performing data cleaning on the patient characteristics and the original corpus data of the patient complaints to generate text data;
b2, embedding words into the generated text data to generate word vectors;
b3, carrying out named entity recognition, word segmentation and part of speech tagging on the word vector by using a Bi-LSTM network and CRF network combined model, and outputting a corresponding entity recognition result, wherein the entity recognition result is a part of speech subject and a non-part of speech word, and the part of speech subject comprises a disease category and a disease symptom;
and B4, performing label embedding and relation extraction on the entity recognition result, and outputting a relation extraction result of the non-nominal words, namely the incentive.
After adopting the technical scheme, compared with the background technology, the invention has the following advantages:
the invention provides an electronic medical record intelligent retrieval system based on a knowledge graph, which integrates medical record data of each data source through a big data platform to form a medical record central library, a knowledge graph layer crawls required data from the medical record central library through a crawler means to generate a medical knowledge graph, and finally an application layer performs entity identification and relation extraction on the knowledge graph layer through an entity extraction layer according to patient chief complaints and patient characteristics and outputs the required medical record data. The intelligent degree of retrieval is high, and is fast, and accurate required data that obtain filters unnecessary data, alleviates patient's burden and hospital's operational load, brings high-efficient accurate experience of seeking medical advice for the patient.
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FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the present invention, it should be noted that the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are all based on the orientation or positional relationship shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the apparatus or element of the present invention must have a specific orientation, and thus, should not be construed as limiting the present invention.
Examples
As shown in fig. 1, the invention discloses an intelligent electronic medical record retrieval system based on a knowledge graph, which comprises:
the data layer is used for acquiring medical record information data;
the platform layer is used for processing data acquired by the data layer by using a big data platform and constructing a medical record central library;
the knowledge map layer is used for crawling required medical data from the medical record central library by utilizing a crawler technology to generate a medical knowledge map;
the entity extraction layer is used for carrying out entity identification and relation extraction on the knowledge graph layer according to the patient chief complaints and the patient characteristics;
and the application layer is used for integrating diversified information of the patient, inputting the diversified information into the entity extraction layer for matching, and obtaining and outputting a retrieval result.
Data sources for the data layer include HIS, EMR, LIS, and ACS.
The medical record information data comprises structured data, unstructured data and semi-structured data, the structured data comprises outpatient medical records, diagnoses and medical advice, the unstructured data comprises inspection data, image data and voice data, and the semi-structured data comprises hospitalized medical records.
The big data platform comprises a data integration module, a data calculation module, a data analysis module, a data storage module and a platform support module.
The data integration module is used for integrating structured data, unstructured data and semi-structured data of each data source and then inputting the integrated data into the data analysis module for data analysis, wherein the data calculation module is matched with the data for analysis and calculation, the data storage module is used for storing the data, and the platform knowledge module comprises a data operation and data processing engine and plays a role of a bridge for connecting the data sources and data application.
The medical record central library comprises a disease knowledge base, an examination and inspection knowledge base, a symptom knowledge base, a medicine knowledge base, a body part knowledge base and an operation knowledge base.
The step of crawling the required medical data from the medical record central library by the knowledge map layer comprises the following steps:
a1, defining a crawler task through a definition module;
a2, a scheduling module reads a crawler task and acquires crawler resources based on a ZooKeeper module and a Redis module;
a3, processing a crawler task and crawler resources by a scheduling module, decomposing the task and sending the task to a crawler engine to directionally crawl information data of a medical record central library;
a4, crawling specific data from a medical record central library by a knowledge graph layer, and performing visual analysis on data of different dimensions based on a web analysis module to generate a knowledge graph.
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 patient characteristics and patient complaints, the neural network module consists of a Bi-LSTM network and a CRF network, and the output result of the entity extraction layer consists of disease types, disease symptoms and causes.
The process of entity identification and relationship extraction is as follows:
b1, carrying out data cleaning on the original corpus data of the patient characteristics and the patient complaints to generate text data;
b2, embedding words into the generated text data to generate word vectors;
b3, carrying out named entity recognition, word segmentation and part of speech tagging on the word vector by utilizing a Bi-LSTM network and CRF network combined model, and outputting a corresponding entity recognition result, wherein the entity recognition result is a part of speech subject and a non-part of speech word, and the part of speech subject comprises disease types and disease symptoms;
and B4, performing label embedding and relation extraction on the entity recognition result, and outputting a relation extraction result of the non-nominal words, namely the incentive.
In this embodiment, taking the patient chief complaint of twice a day in two weeks as an example, after the patient chief complaint is cleaned and a text is generated, the entity extraction layer performs word embedding on the patient chief complaint to generate a word vector, then, the entity extraction is realized through a Bi-LSTM network and a CRF network to obtain entities such as a part-of-speech subject such as "B-disease", "B-symptom", "I symptom", "E symptom" and a part-of-speech word O, after the label embedding and the dependency embedding, the relation extraction is performed to obtain a main body of the part-of-speech word O as an incentive, the connection with the medical knowledge map is realized, and a related case of "twice a day in two weeks" is quickly retrieved.
The application layer of the embodiment provides a high-level retrieval function, and the retrieval contents are divided into six categories of time, diagnosis, inspection, examination, medical advice and case history text according to the requirements of doctors, so that a more accurate search function is provided, and the search contents are positioned better and faster. For example, a physician can quickly retrieve all patient medical records for which the pathology report confirms a diagnosis of "breast cancer," or "carcinoembryonic antigen (CEA)" greater than 5.
And subsequently, according to a target medical record, matching medical record samples with the highest similarity from the medical record central library through the knowledge map layer according to needs, so that the scientific research efficiency is greatly improved, and doctors are liberated from heavy data retrieval tasks and are concentrated in clinics and scientific research.
The constructed knowledge graph clearly performs fusion display processing on outpatient service, inpatient medical records and other information which are obtained by dispersing patients in hospital systems such as HIS, EMR, LIS, PACS and the like in a time axis integrated view mode, provides data display of the whole life cycle of the patients, and helps doctors to comprehensively know the physical conditions of the patients; the knowledge map technology is used for calculating and searching similar medical records to provide case reference for doctors, and the missed diagnosis and misdiagnosis rate are reduced.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. An electronic medical record intelligent retrieval system based on knowledge graph is characterized by comprising:
the data layer is used for acquiring medical record information data;
the platform layer is used for processing data acquired by the data layer by using a big data platform and constructing a medical record central library;
the knowledge map layer is used for crawling required medical data from the medical record central library by utilizing a crawler technology to generate a medical knowledge map;
the entity extraction layer is used for carrying out entity identification and relation extraction on the knowledge graph layer according to the patient chief complaints and the patient characteristics;
and the application layer is used for integrating diversified information of the patient, inputting the diversified information into the entity extraction layer for matching, and obtaining and outputting a retrieval result.
2. The system of claim 1, wherein the system comprises: the data sources of the data layer include HIS, EMR, LIS, and ACS.
3. The system of claim 1, wherein the system comprises: the medical record information data comprises structured data, unstructured data and semi-structured data, the structured data comprises outpatient medical records, diagnoses and medical advice, the unstructured data comprises inspection data, image data and voice data, and the semi-structured data comprises hospitalized medical records.
4. The system of claim 1, wherein the system comprises: the big data platform comprises a data integration module, a data calculation module, a data analysis module, a data storage module and a platform support module.
5. The system of claim 1, wherein the system comprises: the medical record central library comprises a disease knowledge base, an examination and inspection knowledge base, a symptom knowledge base, a medicine knowledge base, a body part knowledge base and an operation knowledge base.
6. The system of claim 1, wherein the intellectual property graph based electronic medical record intelligent retrieval system comprises the knowledge graph layer for crawling the medical data from the medical record central repository:
a1, defining a crawler task through a definition module;
a2, a scheduling module reads a crawler task and acquires crawler resources based on a ZooKeeper module and a Redis module;
a3, processing a crawler task and crawler resources by a scheduling module, decomposing the task and sending the task to a crawler engine to directionally crawl information data of a medical record central library;
a4, crawling specific data from a medical record central library by a knowledge graph layer, and performing visual analysis on data of different dimensions based on a web analysis module to generate a knowledge graph.
7. The system of claim 1, wherein the system comprises: the entity extraction layer comprises a data input module and a neural network module, wherein the data input module adopts manual input or voice input to patient characteristics and patient complaints so as to generate texts, the neural network module comprises a Bi-LSTM network and a CRF network, and output results of the entity extraction layer comprise disease types, disease symptoms and inducements.
8. The system of claim 7, wherein the system comprises: the process of entity identification and relationship extraction is specifically as follows:
b1, performing data cleaning on the patient characteristics and the original corpus data of the patient complaints to generate text data;
b2, embedding words into the generated text data to generate word vectors;
b3, carrying out named entity recognition, word segmentation and part of speech tagging on the word vector by using a Bi-LSTM network and CRF network combined model, and outputting a corresponding entity recognition result, wherein the entity recognition result is a part of speech subject and a non-part of speech word, and the part of speech subject comprises a disease category and a disease symptom;
and B4, performing label embedding and relation extraction on the entity recognition result, and outputting a relation extraction result of the non-nominal words, namely the incentive.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113918732A (en) * | 2021-11-19 | 2022-01-11 | 北京明略软件***有限公司 | Multi-modal knowledge graph construction method and system, storage medium and electronic equipment |
| CN114300083A (en) * | 2021-11-16 | 2022-04-08 | 北京左医科技有限公司 | Medical record construction method and system |
| CN116842142A (en) * | 2023-08-29 | 2023-10-03 | 南通康盛医疗器械有限公司 | Intelligent retrieval system for medical instrument |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180108443A1 (en) * | 2016-04-29 | 2018-04-19 | Boe Technology Group Co., Ltd. | Apparatus and method for analyzing natural language medical text and generating a medical knowledge graph representing the natural language medical text |
| CN109284430A (en) * | 2018-09-07 | 2019-01-29 | 杭州艾塔科技有限公司 | Visualization subject web page content based on distributed structure/architecture crawls system and method |
| CN109346169A (en) * | 2018-10-17 | 2019-02-15 | 长沙瀚云信息科技有限公司 | A kind of artificial intelligence assisting in diagnosis and treatment system and its construction method, equipment and storage medium |
| CN111292821A (en) * | 2020-01-21 | 2020-06-16 | 上海联影智能医疗科技有限公司 | Medical diagnosis and treatment system |
| CN111414393A (en) * | 2020-03-26 | 2020-07-14 | 湖南科创信息技术股份有限公司 | Semantic similar case retrieval method and equipment based on medical knowledge graph |
| CN111613339A (en) * | 2020-05-15 | 2020-09-01 | 山东大学 | Similar medical record searching method and system based on deep learning |
| CN112133424A (en) * | 2020-09-11 | 2020-12-25 | 北京欧应信息技术有限公司 | Learning diagnosis and treatment system for orthopedics department |
| CN112151188A (en) * | 2020-10-19 | 2020-12-29 | 科技谷（厦门）信息技术有限公司 | Intelligent disease prediction system based on medical knowledge map |
-
2021
- 2021-01-15 CN CN202110054968.2A patent/CN112650860A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180108443A1 (en) * | 2016-04-29 | 2018-04-19 | Boe Technology Group Co., Ltd. | Apparatus and method for analyzing natural language medical text and generating a medical knowledge graph representing the natural language medical text |
| CN109284430A (en) * | 2018-09-07 | 2019-01-29 | 杭州艾塔科技有限公司 | Visualization subject web page content based on distributed structure/architecture crawls system and method |
| CN109346169A (en) * | 2018-10-17 | 2019-02-15 | 长沙瀚云信息科技有限公司 | A kind of artificial intelligence assisting in diagnosis and treatment system and its construction method, equipment and storage medium |
| CN111292821A (en) * | 2020-01-21 | 2020-06-16 | 上海联影智能医疗科技有限公司 | Medical diagnosis and treatment system |
| CN111414393A (en) * | 2020-03-26 | 2020-07-14 | 湖南科创信息技术股份有限公司 | Semantic similar case retrieval method and equipment based on medical knowledge graph |
| CN111613339A (en) * | 2020-05-15 | 2020-09-01 | 山东大学 | Similar medical record searching method and system based on deep learning |
| CN112133424A (en) * | 2020-09-11 | 2020-12-25 | 北京欧应信息技术有限公司 | Learning diagnosis and treatment system for orthopedics department |
| CN112151188A (en) * | 2020-10-19 | 2020-12-29 | 科技谷（厦门）信息技术有限公司 | Intelligent disease prediction system based on medical knowledge map |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114300083A (en) * | 2021-11-16 | 2022-04-08 | 北京左医科技有限公司 | Medical record construction method and system |
| CN113918732A (en) * | 2021-11-19 | 2022-01-11 | 北京明略软件***有限公司 | Multi-modal knowledge graph construction method and system, storage medium and electronic equipment |
| CN116842142A (en) * | 2023-08-29 | 2023-10-03 | 南通康盛医疗器械有限公司 | Intelligent retrieval system for medical instrument |
| CN116842142B (en) * | 2023-08-29 | 2023-12-19 | 南通康盛医疗器械有限公司 | Intelligent retrieval system for medical instrument |
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