What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature

Abstract

Ideally decisions regarding medical treatments would be informed by the totality of the available evidence. The best evidence we currently have is in published natural language articles describing the conduct and results of clinical trials. Because these are unstructured, it is difficult for domain experts (e.g., physicians) to sort through and appraise the evidence pertaining to a given clinical question. Natural language technologies have the potential to improve access to the evidence via semi-automated processing of the biomedical literature. In this brief paper I highlight work on developing tasks, corpora, and models to support semi-automated evidence retrieval and extraction. The aim is to design models that can consume articles describing clinical trials and automatically extract from these key clinical variables and findings, and estimate their reliability. Completely automating 'machine reading' of evidence remains a distant aim given current technologies; the more immediate hope is to use such technologies to help domain experts access and make sense of unstructured biomedical evidence more efficiently, with the ultimate aim of improving patient care. Aside from their practical importance, these tasks pose core NLP challenges that directly motivate methodological innovation.

Figure 1:

Schematic of a model that can map from an unstructured article describing an RCT to structured data codifying the evidence that it reports. We envision such models primarily being used to help, not replace, domain experts.

Figure 2:

High-level overview of an envisioned semi-automated system for evidence retrieval and extraction. (A) An interactively trained classification/retrieval model facilitates rapid identification of trials relevant to a given clinical question, specified as a PICO frame. (B) Models next extract clinically salient information from relevant articles. (C) Domain experts then interact with the extracted evidence extracted by models and the underlying documents to find the data they are after; the idea is to make this process faster and less tedious.