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Leveraging medical context to recommend semantically similar terms for chart reviews | BMC Medical Informatics and Decision Making

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Information retrieval (IR) help clinicians answer questions posed to large collections of electronic medical records (EMRs), such as how best to identify a patient’s cancer stage. One of the more promising approaches to IR for EMRs is to expand a keyword query with similar terms (e.g., augmenting cancer with mets). However, there is a large range of clinical chart review tasks, such that fixed sets of similar terms is insufficient. Current language models, such as Bidirectional Encoder Representations from Transformers (BERT) embeddings, do not capture the full non-textual context of a task. In this study, we present new methods that provide similar terms dynamically by adjusting with the context of the chart review task. We introduce a vector space for medical-context in which each word is represented by a vector that captures the word’s usage in different medical contexts (e.g., how frequently cancer is used when ordering a prescription versus describing family history) beyond the co

Background In a clinical chart review task [1], a clinically knowledgeable person (e.g., physician, medical student, or nurse) combs through electronic medical records (EMRs) [2,3,4] for specific data of interest. Chart reviews are time-consuming and costly because a patient’s chart may be composed of hundreds of clinical notes. Various automated approaches have been developed to improve the efficiency of chart reviews. A particularly promising information retrieval (IR) method to assist with chart reviews is query expansion [5,6,7,8]. This method expands the original search terms into a…

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