MedGPT: Medical Concept Prediction from Clinical Narratives

• URL: http://arxiv.org/abs/2107.03134v1
• Date: Wed, 7 Jul 2021 10:36:28 GMT
• Title: MedGPT: Medical Concept Prediction from Clinical Narratives
• Authors: Zeljko Kraljevic, Anthony Shek, Daniel Bean, Rebecca Bendayan, James Teo, Richard Dobson
• Abstract summary: Temporal modelling of a patient's medical history can be used to predict future events. We present MedGPT, a novel transformer-based pipeline that uses Named Entity Recognition and Linking tools. We show that our model captures medical knowledge by testing it on an experimental medical multiple choice question answering task.
• Score: 0.23488056916440858
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The data available in Electronic Health Records (EHRs) provides the opportunity to transform care, and the best way to provide better care for one patient is through learning from the data available on all other patients. Temporal modelling of a patient's medical history, which takes into account the sequence of past events, can be used to predict future events such as a diagnosis of a new disorder or complication of a previous or existing disorder. While most prediction approaches use mostly the structured data in EHRs or a subset of single-domain predictions and outcomes, we present MedGPT a novel transformer-based pipeline that uses Named Entity Recognition and Linking tools (i.e. MedCAT) to structure and organize the free text portion of EHRs and anticipate a range of future medical events (initially disorders). Since a large portion of EHR data is in text form, such an approach benefits from a granular and detailed view of a patient while introducing modest additional noise. MedGPT effectively deals with the noise and the added granularity, and achieves a precision of 0.344, 0.552 and 0.640 (vs LSTM 0.329, 0.538 and 0.633) when predicting the top 1, 3 and 5 candidate future disorders on real world hospital data from King's College Hospital, London, UK (\textasciitilde600k patients). We also show that our model captures medical knowledge by testing it on an experimental medical multiple choice question answering task, and by examining the attentional focus of the model using gradient-based saliency methods.

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