Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts

Abeed Sarker; Mohammed Ali Al-Garadi; Yuan-Chi Yang; Jinho Choi; Arshed A Quyyumi; Greg S Martin

doi:10.2196/18471

Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts

JMIR Med Inform. 2021 Sep 28;9(9):e18471. doi: 10.2196/18471.

Authors

Abeed Sarker¹, Mohammed Ali Al-Garadi¹, Yuan-Chi Yang¹, Jinho Choi², Arshed A Quyyumi³, Greg S Martin⁴

Affiliations

¹ Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
² Department of Computer Science, College of Arts and Sciences, Emory University, Atlanta, GA, United States.
³ Emory Clinical Cardiovascular Institute, Division of Cardiology, Department of Medicine, School of Medicine, Emory University, Atlanta, GA, United States.
⁴ Predictive Health Institute and Center for Health Discovery and Well Being, Department of Medicine, School of Medicine, Emory University, Atlanta, GA, United States.

PMID: 34581670
PMCID: PMC8512184
DOI: 10.2196/18471

Abstract

The capabilities of natural language processing (NLP) methods have expanded significantly in recent years, and progress has been particularly driven by advances in data science and machine learning. However, NLP is still largely underused in patient-oriented clinical research and care (POCRC). A key reason behind this is that clinical NLP methods are typically developed, optimized, and evaluated with narrowly focused data sets and tasks (eg, those for the detection of specific symptoms in free texts). Such research and development (R&D) approaches may be described as problem oriented, and the developed systems perform specialized tasks well. As standalone systems, however, they generally do not comprehensively meet the needs of POCRC. Thus, there is often a gap between the capabilities of clinical NLP methods and the needs of patient-facing medical experts. We believe that to increase the practical use of biomedical NLP, future R&D efforts need to be broadened to a new research paradigm-one that explicitly incorporates characteristics that are crucial for POCRC. We present our viewpoint about 4 such interrelated characteristics that can increase NLP systems' suitability for POCRC (3 that represent NLP system properties and 1 associated with the R&D process)-(1) interpretability (the ability to explain system decisions), (2) patient centeredness (the capability to characterize diverse patients), (3) customizability (the flexibility for adapting to distinct settings, problems, and cohorts), and (4) multitask evaluation (the validation of system performance based on multiple tasks involving heterogeneous data sets). By using the NLP task of clinical concept detection as an example, we detail these characteristics and discuss how they may result in the increased uptake of NLP systems for POCRC.

Keywords: evidence-based medicine; medical informatics; natural language processing; patient-centered care; text mining.

©Abeed Sarker, Mohammed Ali Al-Garadi, Yuan-Chi Yang, Jinho Choi, Arshed A Quyyumi, Greg S Martin. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 28.09.2021.