Objective To provide a scoping review of papers on clinical natural language processing (NLP) shared tasks that use publicly available electronic health record data from a cohort of patients. Materials and Methods We searched 6 databases, including biomedical research and computer science literature databases. A round of title/abstract screening and full-text screening were conducted by 2 reviewers. Our method followed the PRISMA-ScR guidelines. Results A total of 35 papers with 48 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including named entity recognition, summarization, and other NLP tasks. Some tasks were introduced as potential clinical decision support applications, such as substance abuse detection, and phenotyping. We summarized the tasks by publication venue and dataset type. Discussion The breadth of clinical NLP tasks continues to grow as the field of NLP evolves with advancements in language systems. However, gaps exist with divergent interests between the general domain NLP community and the clinical informatics community for task motivation and design, and in generalizability of the data sources. We also identified issues in data preparation. Conclusion The existing clinical NLP tasks cover a wide range of topics and the field is expected to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multidisciplinary collaboration, reporting transparency, and standardization in data preparation. We provide a listing of all the shared task papers and datasets from this review in a GitLab repository.
Contribution: Demonstrates how to use experiential learning (EL) to improve argumentative writing. Presents the design and development of a natural language processing (NLP) application for aiding instructors in providing feedback on student essays. Discusses how EL combined with automated support provides an analytical approach to improving written-communication skills.Background: High-quality, timely, feedback is an effective way to improve students' writing. However, large class sizes and limited instructor backgrounds often make formative feedback impossible. Recent trends, including lowering entry requirements, have added to these challenges. Assistive technologies for implementing inclusive education provide viable solutions.Research Questions: 1) How and why can EL be used to develop argumentative writing skills in university STEM students? 2) How can technologies be developed to support using EL in teaching writing? and 3) How might the holistic impact of using such analytic techniques be evaluated?Methodology: Participants in an EL project were assigned two essays in sequence. They were given instructions on making good arguments and shown how to use an analytic rubric to maximize their scores. The essays were hand scored by tutors who provided scores for each dimension of the rubric. Subsequently, the content and argumentation of the essays were analyzed using NLP techniques to obtain independent scores. Qualitative data were also collected.Findings: The project produced transformative writing experiences for the participants. It showed how analytical techniques help improve writing skills and how relevant automated instructor assistance can be developed using NLP technologies.
<p>This article demonstrates how experiential learning could be used to develop argumentative essay writing skills in STEM students. Written feedback, when delivered in a timely manner, is an effective way of advancing students’ understanding of the writing process. Unfortunately, large class sizes and the limited backgrounds of instructors do not always make formative feedback possible. STEM students are especially disadvantaged since approaches to teaching written communication tend to differ from the trial-and-error strategies compatible with many STEM areas. An experiential learning approach to writing instruction can have a positive impact on developing writing skills in STEM learners. Implementing algorithms for providing STEM students with immediate, dependable, formative feedback is expected to improve their performance in writing. This paper discusses an experiential learning project for teaching argumentative writing was delivered to computer science and engineering freshmen. Also discussed are automated analysis of content and argumentation in the essays, using NLP methods.</p>
<p>This article demonstrates how experiential learning could be used to develop argumentative essay writing skills in STEM students. Written feedback, when delivered in a timely manner, is an effective way of advancing students’ understanding of the writing process. Unfortunately, large class sizes and the limited backgrounds of instructors do not always make formative feedback possible. STEM students are especially disadvantaged since approaches to teaching written communication tend to differ from the trial-and-error strategies compatible with many STEM areas. An experiential learning approach to writing instruction can have a positive impact on developing writing skills in STEM learners. Implementing algorithms for providing STEM students with immediate, dependable, formative feedback is expected to improve their performance in writing. This paper discusses an experiential learning project for teaching argumentative writing was delivered to computer science and engineering freshmen. Also discussed are automated analysis of content and argumentation in the essays, using NLP methods.</p>
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