Leveraging Interpretable Feature Representations for Advanced Differential Diagnosis in Computational Medicine.

Diagnostic errors represent a critical issue in clinical diagnosis and treatment. In China, the rate of misdiagnosis in clinical diagnostics is approximately 27.8%.

Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.

Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations. In this paper, we exploit additional evidence by also making use of . We encode the relation between a span of tokens matching a Unified Medical Language System (UMLS) concept and other tokens in the sentence.

MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records.

As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as "symptom-disease", "laboratory examination/imaging examination-disease", and "disease-treatment method". The continuous expansion of large electronic clinical records provides an opportunity to learn medical knowledge by machine learning. In this process, how to extract entities with a medical logic structure and how to make entity extraction more consistent with the logic of the text content in electronic clinical records are two issues that have become key in building a high-quality, medical knowledge graph.