Method, system and equipment for automatically extracting human disease symptom characteristics

An automatic extraction and disease technology, applied in the field of medical diagnosis, can solve problems such as incompleteness, feature deviation, and extraction errors

Inactive Publication Date: 2019-03-29
XINBO ZHUOCHANG TECH BEIJING
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The disadvantage of this method is that as long as the order of modifiers changes, it may lead to deviations in the features found by CRF, and then there will be extraction errors or incomplete sy...

Method used

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  • Method, system and equipment for automatically extracting human disease symptom characteristics
  • Method, system and equipment for automatically extracting human disease symptom characteristics
  • Method, system and equipment for automatically extracting human disease symptom characteristics

Examples

Experimental program
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Effect test

Embodiment 1

[0052] Such as figure 1 As shown, a method for automatic extraction of human disease symptom features includes the following steps:

[0053]Step 100 is executed to extract patient cases. Step 110 is executed to analyze and summarize the dimension information in the medical records. The dimension information includes at least one of gender, age, symptom, part, symptom modifier, part modifier, secretion, excretion, action, special period, affected part, emotion, smell, sound, verb, size and shape. Execute step 120, summarize the basic medical knowledge according to the dimensional information, and generate knowledge atomic units of the above multiple dimensions according to the dimensional information. Execute step 130, perform word segmentation and semantic analysis on the disease characteristic sentences in the medical records, generate disease characteristic sentence dependencies, and perform entity labeling and identification on corresponding words, and obtain disease know...

Embodiment 2

[0055] Such as figure 2 As shown, an automatic feature extraction system for human disease symptoms includes an information extraction module 200 , an information analysis module 210 , a summary module 220 , a feature analysis module 230 , an information generation module 240 , and a knowledge map 250 .

[0056] The information extraction module 200 is used to extract patient cases.

[0057] The information analysis module 210 is used to analyze and summarize the dimensional information in the medical records. The dimensional information includes gender, age, symptoms, parts, symptom modifiers, part modifiers, secretions, excretions, actions, special periods, affected areas, emotions, smells, sounds , verb, size and shape at least one.

[0058] The summary module 220 is used for summarizing basic medical knowledge according to the dimensional information, and for generating knowledge atomic units of the above multiple dimensions according to the dimensional information.

[...

Embodiment 3

[0063] The present invention combines the outpatient clinical medical record texts to carry out the research on the extraction method of symptom and sign information. By establishing dimensions for the symptom and sign information, a model of the symptom and sign is formed, and then using NLP and feature learning methods to realize the extraction from the clinical medical record to the current medical history text. Requirements for extracting symptom phenotype entities in .

[0064] The present invention designs a method for extracting symptom features, using natural language processing and knowledge map technology, can extract disease symptom features from massive data, and at the same time quantify to specified dimensions, finally generate knowledge map, and finally realize flexible query of knowledge Effect.

[0065] The process of extracting disease features is the process of natural language processing (NLP). First of all, the basic vocabulary should be organized accordi...

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Abstract

The invention provides a method and system for automatically extracting human disease symptom characteristics. The method comprises a step of extracting a medical record of a patient, and further comprises the following steps: analyzing and summarizing dimension information in the medical record; summarizing the basic medical knowledge according to the dimension information; performing segmentation and semantic analysis on a disease characteristic sentence in the medical record; corresponding the disease knowledge information to disease and dimension information to generate json-format entityinformation; repeating the steps, and updating an iterative knowledge map. The invention provides the method and the system for automatically extracting the human disease symptom characteristics, through which a method for extracting symptom and sign information is researched on the basis of an outpatient clinical medical record text, the dimension is established through the symptom and sign information to form a symptom and sign model, and then a requirement on extraction of symptom phenotype entities from a current clinical medical history text is met by NLP, feature learning and other methods.

Description

technical field [0001] The invention relates to the technical field of medical diagnosis, in particular to a method and system for automatically extracting human disease symptom features. Background technique [0002] With the continuous improvement of people's health awareness, more and more people begin to pay attention to their physical condition. It is difficult for ordinary people to understand the inspection and laboratory report as the standard of health judgment. Due to the serious imbalance in the ratio of doctors to patients in China, it is impossible for doctors to explain the detailed information of the report to each patient in detail. Although with the popularization of the Internet, people have more and more sources of knowledge, but the above are so mixed that it is impossible for ordinary people to distinguish true from false. [0003] Symptom phenotype (symptom and sign) is an important substantive information in clinical data and medical bibliography dat...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/70
CPCG16H50/20G16H50/70
Inventor 吕军震胥洪锋于国方李长松王林武佳
Owner XINBO ZHUOCHANG TECH BEIJING
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