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

• Published in: JMIR medical informatics Vol. 9; no. 9; p. e18471
• Main Authors: Sarker, Abeed, Al-Garadi, Mohammed Ali, Yang, Yuan-Chi, Choi, Jinho, Quyyumi, Arshed A, Martin, Greg S
• Format: Journal Article
• Language: English
• Published: Toronto JMIR Publications 01.09.2021
• Subjects: Artificial intelligence; Automation; Big Data; Business metrics; Decision making; Deep learning; Disease; Electronic health records; Evidence-based medicine; Health informatics; Machine learning; Natural language processing; Neural networks; Patients; R&D; Research & development; Subject specialists; Viewpoint
• DOI: 10.2196/18471
• ISSN: 2291-9694

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.