Medical Natural Language Processing

• Chinese Word Segmentation

  A process in which medical texts are rearranged into a word sequence according to certain standards, which is the foundation for further text analysis.

• Entity Recognition

  Identify medical terms with particular meanings in medical texts, such as “diagnosis names”, “drug names” and “operation names”, and some attribute information having clear semantic meanings such as “time” and “degree”, and build an important preconditions for semantic analysis of medical texts.

• Entity Relationship Classification

  Identify the relationship between entities in texts, for example, in an chemotherapy instance, identify such items like “time”, “curative effect” of the “chemotherapy plan”, which build an important basis for extraction of structured information.

• Text Structuralization

  Design models as required by a task, and extract structured task information from the results of entity identification and entity relationship identification.

• Entity Normalization

  Imitating human normalization, use entity linking technology based on knowledge atlas to normalize clinical data to standard names, for example, to normalize a surgery operation to ICD9, and a diagnosis name to ICD10, thus facilitating analysis of future tasks such as screening and statistics.

• Knowledge Graph

  Integrate data from multiple Construction sources such as clinical data, medical literature guide, CFDA data and Internet data to establish a large medical knowledge base supporting such tasks of medical statistics, search, analysis and reasoning.