A scoping review of ethics considerations in clinical natural language processing.
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ABSTRACT: Objectives
To review through an ethics lens the state of research in clinical natural language processing (NLP) for the study of bias and fairness, and to identify gaps in research.Methods
We queried PubMed and Google Scholar for articles published between 2015 and 2021 concerning clinical NLP, bias, and fairness. We analyzed articles using a framework that combines the machine learning (ML) development process (ie, design, data, algorithm, and critique) and bioethical concepts of beneficence, nonmaleficence, autonomy, justice, as well as explicability. Our approach further differentiated between biases of clinical text (eg, systemic or personal biases in clinical documentation towards patients) and biases in NLP applications.Results
Out of 1162 articles screened, 22 met criteria for full text review. We categorized articles based on the design (N = 2), data (N = 12), algorithm (N = 14), and critique (N = 17) phases of the ML development process.Discussion
Clinical NLP can be used to study bias in applications reliant on clinical text data as well as explore biases in the healthcare setting. We identify 3 areas of active research that require unique ethical considerations about the potential for clinical NLP to address and/or perpetuate bias: (1) selecting metrics that interrogate bias in models; (2) opportunities and risks of identifying sensitive patient attributes; and (3) best practices in reconciling individual autonomy, leveraging patient data, and inferring and manipulating sensitive information of subgroups. Finally, we address the limitations of current ethical frameworks to fully address concerns of justice. Clinical NLP is a rapidly advancing field, and assessing current approaches against ethical considerations can help the discipline use clinical NLP to explore both healthcare biases and equitable NLP applications.
SUBMITTER: Bear Don't Walk OJ
PROVIDER: S-EPMC9154253 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. MATERIALS AND METHODS:We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. RESULTS:DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a "long tail" of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. DISCUSSION:Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). CONCLUSION:Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.
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Project description:BackgroundThe COVID-19 pandemic has highlighted the growing need for digital learning tools in postgraduate family medicine training. Family medicine departments must understand and recognize the use and effectiveness of digital tools in order to integrate them into curricula and develop effective learning tools that fill gaps and meet the learning needs of trainees.ObjectiveThis scoping review will aim to explore and organize the breadth of knowledge regarding digital learning tools in family medicine training.MethodsThis scoping review follows the 6 stages of the methodological framework outlined first by Arksey and O'Malley, then refined by Levac et al, including a search of published academic literature in 6 databases (MEDLINE, ERIC, Education Source, Embase, Scopus, and Web of Science) and gray literature. Following title and abstract and full text screening, characteristics and main findings of the included studies and resources will be tabulated and summarized. Thematic analysis and natural language processing (NLP) will be conducted in parallel using a 9-step approach to identify common themes and synthesize the literature. Additionally, NLP will be employed for bibliometric and scientometric analysis of the identified literature.ResultsThe search strategy has been developed and launched. As of October 2021, we have completed stages 1, 2, and 3 of the scoping review. We identified 132 studies for inclusion through the academic literature search and 127 relevant studies in the gray literature search. Further refinement of the eligibility criteria and data extraction has been ongoing since September 2021.ConclusionsIn this scoping review, we will identify and consolidate information and evidence related to the use and effectiveness of existing digital learning tools in postgraduate family medicine training. Our findings will improve the understanding of the current landscape of digital learning tools, which will be of great value to educators and trainees interested in using existing tools, innovators looking to design digital learning tools that meet current needs, and researchers involved in the study of digital tools.Trial registrationOpen Science Framework osf.io/wju4k 10.17605/OSF.IO/WJU4K.International registered report identifier (irrid)DERR1-10.2196/34575.
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