Overview of the TREC 2014 Clinical Decision Support Track

By Kirk Roberts, Dina Demner-Fushman, Ellen M. Voorhees, William R. Hersh. 2014

193 citations recorded by the source corpus.

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “Motivated by the Text REtrieval Conference-Clinical Decision Support (TREC-CDS) track dataset [192], diagnosis inferencing from unstructured clinical text has gained much attention among AI researchers recently.”
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning (2017, methodology): “…TREC CDS tracks, clinical diagnosis inferencing from free text clinical narratives has been showcased as a signiﬁcant milestone in clinical question answering and a path to improving the accuracy of relevant biomedical article retrieval (Roberts et al., 2015, 2016b; Goodwin and Harabagiu, 2016).”
Overview of the TREC 2020 Precision Medicine Track (2017, unknown): “The third series was the 2014-2016 TREC Clinical Decision Support (CDS) track (Roberts et al., 2015, 2016a,b), which targeted clinicians seeking evidencebased literature to test, diagnose, or treat patients.”
Medical Question Answering for Clinical Decision Support (2016, methodology): “In these evaluations, we used the set of 30 topics (numbered 31-60) used for the 2015 TREC-CDS evaluation [25].”
TREC-COVID (2020, background): “mation in future global biomedical crises. Based on the TREC4 framework, TREC-COVID is building a pandemic test collection through a series of rounds. It builds on a history of biomedical TREC tracks [1, 7, 3, 2], and specically on the Genomics, Clinical Decision Support, and Precision Medicine trac”
CliCR: a Dataset of Clinical Case Reports for Machine Reading Comprehension (2018, background): “The current gap between the best machine and the best human performance is nearly We did not include remotely related datasets which concern a different task (e.g. information retrieval) (Roberts et al., 2015; Voorhees and Tice, 2000).”
A Comparison of Word Embeddings for the Biomedical Natural Language Processing (2018, background): “TREC 2016 CDS track focuses on biomedical literature retrieval that helps physicians find the precise literature information and make the best clinical decision at the point of care [53].”
Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study (2017, methodology): “…(CDS) tracks1, clinical diagnosis inferencing from free text clinical narratives has been showcased as a significant milestone in clinical question answering and a path to improving the accuracy of relevant biomedical article retrieval (Roberts et al., 2015, 2016; Goodwin and Harabagiu, 2016).”
TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19 (2020, background): “Relevance: As is typically done in TREC, including its medical tracks [5,11,12,6,13,14], each assessed document is judged as relevant, partially relevant, or not relevant to the topic.”