Using natural language processing to analyze unstructured patient-reported outcomes data derived from Electronic Health Records for cancer populations: a systematic review

This study aimed to systematically review the published studies that used NLP techniques to extract and analyze PROs in clinical narratives from EHRs for cancer populations. We examined the types of NLP (with and without ML) techniques and platforms for data processing, analysis, and clinical applications.EXPERT OPINION: Utilizing NLP methods offers a valuable approach for processing and analyzing unstructured PROs among cancer patients and survivors. These techniques encompass a broad range of applications, such as extracting or recognizing PROs, categorizing, characterizing, or grouping PROs, predicting or stratifying risk for unfavorable clinical results, and evaluating connections between PROs and adverse clinical outcomes. The employment of NLP techniques is advantageous in converting substantial volumes of unstructured PRO data within EHRs into practical clinical utilities for individuals with cancer.PMID:38383308 | DOI:10.1080/14737167.2024.2322664
Source: Expert Review of Pharmacoeconomics and Outcomes Research - Category: Health Management Authors: Source Type: research