An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing.

An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing. AMIA Annu Symp Proc. 2018;2018:288-294 Authors: Bozkurt S, Park JI, Kan KM, Ferrari M, Rubin DL, Brooks JD, Hernandez-Boussard T Abstract Digital rectal examination (DRE) is considered a quality metric for prostate cancer care. However, much of the DRE related rich information is documented as free-text in clinical narratives. Therefore, we aimed to develop a natural language processing (NLP) pipeline for automatic documentation of DRE in clinical notes using a domain-specific dictionary created by clinical experts and an extended version of the same dictionary learned by clinical notes using distributional semantics algorithms. The proposed pipeline was compared to a baseline NLP algorithm and the results of the proposed pipeline were found superior in terms of precision (0.95) and recall (0.90) for documentation of DRE. We believe the rule-based NLP pipeline enriched with terms learned from the whole corpus can provide accurate and efficient identification of this quality metric. PMID: 30815067 [PubMed - indexed for MEDLINE]
Source: AMIA Annual Symposium Proceedings - Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research