Abstract
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).
Summary
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.
Summary
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.
Section Clinical Natural Language Processing
Althoff, T, Clark K, Leskovec, J. Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health. Trans Assoc Comput Linguist 2016(4):463-76
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361062/
Kilicoglu, H, Demner-Fushman, D. Bio-SCoRes: A Smorgasbord Architecture for Coreference Resolution in Biomedical Text. PLoS One. 2016 Mar 2;11(3):e0148538
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0148538
Morid, MA, Fiszman, M, Raja, K, Jonnalagadda, SR, Del Fiol, G. Classification of clinically useful sentences in clinical evidence resources. J Biomed Inform. 2016 Apr;60:14-22
http://www.sciencedirect.com/science/article/pii/S1532046416000046?via%3Dihub
Shivade C, de Marneffe MC, Fosler-Lussier E, Lai AM. Identification, characterization, and grounding of gradable terms in clinical text. Proceedings of the 15th Workshop on Biomedical Natural Language Processing. 2016:17-26
https://www.semanticscholar.org/paper/Identification-characterization-and-grounding-of-g-Shivade-Marneffe/c00ba120de1964b444807255030741d199ba6e04
Wu, Y, Denny, JC, Rosenbloom, ST, Miller, RA, Giuse, DA, Wang, L, Blanquicett, C, Soysal, E, Xu, J, Xu, H. A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD). J Am Med Inform Assoc 2017 Apr 1;24(e1):e79-e86
https://academic.oup.com/jamia/article-abstract/24/e1/e79/2631496/A-long-journey-to-short-abbreviations-developing?redirectedFrom=fulltext
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.
This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with ...access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.
The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE
®, the largest bibliographic database of ...biomedical citations. Indexers at the US National Library of Medicine (NLM) need efficient tools to help them accommodate the ensuing workload. After reviewing issues in the automatic assignment of Medical Subject Headings (MeSH
® terms) to biomedical text, we focus more specifically on the new subheading attachment feature for NLM’s Medical Text Indexer (MTI). Natural Language Processing, statistical, and machine learning methods of producing automatic MeSH main heading/subheading pair recommendations were assessed independently and combined. The best combination achieves 48% precision and 30% recall. After validation by NLM indexers, a suitable combination of the methods presented in this paper was integrated into MTI as a subheading attachment feature producing MeSH indexing recommendations compliant with current state-of-the-art indexing practice.
The profusion of online resources calls for tools and methods to help Internet users find precisely what they are looking for. Quality controlled gateway CISMeF provides such services for health ...resources. However, the human cost of maintaining and updating the catalogue are increasingly high. This paper presents the automatic indexing system currently developed in the CISMeF team to be used as such for preliminary indexing, or after human reviewing for the final indexing. The system architecture, using the INTEX platform for MeSH term extraction is detailed. The results of a first evaluation tend to indicate that the automatic indexing strategy is relevant, as it achieves a precision comparable to that of other existing operational systems. Moreover, the system presented in this paper retrieves keyword/qualifier pairs as opposed to single terms, therefore providing a significantly more precise indexing. Further development and tests will be carried out in order to improve the coverage of the dictionaries, and validate the efficiency of the system in the indexers’ everyday work.
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.
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.
Introduction: CISMeF is a Quality Controlled Health Gateway using a terminology based on the Medical Subject Headings (MeSH) thesaurus that displays medical specialties (metaterms) and the ...relationships existing between them and MeSH terms.
Objective: The need to classify the resources within the catalogue has led us to combine this type of semantic information with domain expert knowledge for health resources categorization purposes.
Material and methods: A two-step categorization process consisting of mapping resource keywords to CISMeF metaterms and ranking metaterms by decreasing coverage in the resource has been developed. We evaluate this algorithm on a random set of 123 resources extracted from the CISMeF catalogue. Our gold standard for this evaluation is the manual classification provided by a domain expert, viz. a librarian of the team.
Results: The CISMeF algorithm shows 81% precision and 93% recall, and 62% of the resources were assigned a “fully relevant” or “fairly relevant” categorization according to strict standards.
Discussion: A thorough analysis of the results has enabled us to find gaps in the knowledge modeling of the CISMeF terminology. The necessary adjustments having been made, the algorithm is currently used in CISMeF for resource categorization.
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