Biomedical doctoral students' research practices when facing dilemmas: two vignette-based randomized control trials.

Our aim was to describe the research practices of doctoral students facing a dilemma to research integrity and to assess the impact of inappropriate research environments, i.e. exposure to (a) a post-doctoral researcher who committed a Detrimental Research Practice (DRP) in a similar situation and (b) a supervisor who did not oppose the DRP. We conducted two 2-arm, parallel-group randomized controlled trials. We created 10 vignettes describing a realistic dilemma with two alternative courses of action (good practice versus DRP). 630 PhD students were randomized through an online system to a vignette (a) with (n = 151) or without (n = 164) exposure to a post-doctoral researcher; (b) with (n = 155) or without (n = 160) exposure to a supervisor. The primary outcome was a score from - 5 to + 5, where positive scores indicated the choice of DRP and negative scores indicated good practice. Overall, 37% of unexposed participants chose to commit DRP with important variation across vignettes (minimum 10%; maximum 66%). The mean difference [95%CI] was 0.17 [- 0.65 to 0.99;], p = 0.65 when exposed to the post-doctoral researcher, and 0.79 [- 0.38; 1.94], p = 0.16, when exposed to the supervisor. In conclusion, we did not find evidence of an impact of postdoctoral researchers and supervisors on student research practices.Trial registration: NCT04263805, NCT04263506 (registration date 11 February 2020).

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.

Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.

Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest.

OBJECTIVE: To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP). METHOD: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers. RESULTS: The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects. CONCLUSIONS: The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.

Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.

OBJECTIVE: To summarize recent research and present a selection of the best papers published in 2014 in the field of clinical Natural Language Processing (NLP). METHOD: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. RESULTS: The clinical NLP best paper selection shows that the field is tackling text analysis methods of increasing depth. The full review process highlighted five papers addressing foundational methods in clinical NLP using clinically relevant texts from online forums or encyclopedias, clinical texts from Electronic Health Records, and included studies specifically aiming at a practical clinical outcome. The increased access to clinical data that was made possible with the recent progress of de-identification paved the way for the scientific community to address complex NLP problems such as word sense disambiguation, negation, temporal analysis and specific information nugget extraction. These advances in turn allowed for efficient application of NLP to clinical problems such as cancer patient triage. Another line of research investigates online clinically relevant texts and brings interesting insight on communication strategies to convey health-related information. CONCLUSIONS: The field of clinical NLP is thriving through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques for concrete healthcare purposes. Clinical NLP is becoming mature for practical applications with a significant clinical impact.

French Infobutton: an academic and business perspective.

Infobuttons are context-specific links between clinical information systems and other online information resources. The objective of this study is to describe a French Infobutton, which will be sold in the French-speaking Health Information market.