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. 2023 Dec:146:102701.
doi: 10.1016/j.artmed.2023.102701. Epub 2023 Nov 1.

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review

Jin-Ah Sim  1 , Xiaolei Huang  2 , Madeline R Horan  3 , Christopher M Stewart  4 , Leslie L Robison  3 , Melissa M Hudson  5 , Justin N Baker  6 , I-Chan Huang  7
Affiliations

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review

Jin-Ah Sim et al. Artif Intell Med. 2023 Dec.

Abstract

Objective: Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.

Methods: We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs.

Results: Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP.

Conclusions: This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.

Keywords: Electronic health records; Machine learning; Natural language processing; Patient-reported outcomes; Unstructured clinical narrative.

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Conflict of interest statement

Declaration of competing interest All co-authors declare no conflict of interest.

Figures

Figure 1.
Figure 1.
Flow diagram of included articles
Figure 2:
Figure 2:
NLP/ ML pipeline for processing unstructured PRO data in EHRs
See this image and copyright information in PMC

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