Transform medical text expressed in natural language into retrievable, analyzable and computable structured data
Cooperation consulting
Scheme introduction
Characteristic advantages
Application scenarios
Scheme architecture
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Scheme introduction
Medical record data desensitization
It supports desensitization of more than 100 fields of sensitive information. Using NLP technology, it can desensitize the sensitive information inside complex long text.
Medical record text cleaning
Xml parsing;The unstructured long text is divided into paragraphs according to the standard format of medical documents.
Deep structure
Automatically identify medical entities and attributes in the text, such as positive/negative symptoms, signs, diseases, examinations, treatment plans and other entities in admission records and discharge records, and extract structured information from medical records according to the preset schema according to specific research needs.
Structured service platform
It supports user-defined term set, data annotation, customized management and training set annotation for customized entities and attributes, with more intuitive effect and easier operation.
Structured effect
Standard medical entities and attributes
Characteristic advantages
Multilevel extraction scheme
A complete set of medical text data processing schemes from coarse to fine, from general to customized, to meet the information extraction requirements of different granularity
Refine feature extraction
Fine grained analysis and extraction of medical entities and attributes, and accurate understanding of medical concepts expressed in text
Normalized mapping
Mapping the international terminology system to facilitate cross system interoperability
Customizable extraction field
Predefine the schema according to specific requirements, and automatically extract large segments of medical records into fine grained structured information tables
Intelligent technology framework
Introducing multiple deep learning algorithms to solve the problem of vocabulary generalization
Diversified corpus support
Electronic medical records, search queries, doctor-patient dialogues, medical literature, professional terms and colloquial expressions can be effectively identified
Application scenarios
Medical big data platform
Clinical research
Diagnosis and treatment auxiliary system
Intelligent management of hospital data
Medical insurance data analysis
Medical big data platform
Solve the structural and standardized problems faced in the construction of regional health care big data platform.
Cooperation cases
Scheme architecture
Scheme introduction and product composition
The original medical record text data is processed through collection, cleaning, semantic recognition, etc. to achieve multi-level, different granularity structured information extraction.
Structured medical record
The original text medical records are cleaned, desensitized, and denoised to form structured medical record data with unified form and chapter granularity.
Analysis of medical text elements
Analyze and extract medical entities and related attributes in medical query, medical record text, and medical literature.
Special disease depth structure
Customize the schema according to the characteristics of special diseases and research needs, and implement fine granularity information for text medical records according to the predefined schema.
Cooperation consulting
We also provide more complete privatization solutions such as data governance, structure, and data visualization.To get in touch with us quickly, please click the cooperation consultation below.
