ReviewYearbook of medical informatics2016
Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.
Review in Yearbook of medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- The evaluation illusion of large language models in medicine.NPJ digital medicine · 2025Article
- Clinical Sentiment Analysis by Large Language Models Enhances Prediction of Hepatorenal Syndrome.Gastro hep advances · 2025Article
- Combining Topic Modeling, Sentiment Analysis, and Corpus Linguistics to Analyze Unstructured Web-Based Patient Experience Data: Case Study of Modafinil Experiences.Journal of medical Internet research · 2024Article
- Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.JMIR neurotechnology · 2024Review
- Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis.Reviews in cardiovascular medicine · 2024Review
- The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.Drug design, development and therapy · 2023Article
- Artificial Intelligence and Medical Humanities.The Journal of medical humanities · 2022Article
- Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review.BMC medical research methodology · 2022Article
- LexExp: a system for automatically expanding concept lexicons for noisy biomedical texts.Bioinformatics (Oxford, England) · 2021Article
- Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis.Frontiers in digital health · 2021Article
- Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells.Proceedings of machine learning research · 2020Article
- Cross-lingual Unified Medical Language System entity linking in online health communities.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Review
- Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data.Applied clinical informatics · 2020Article
- Automatic trial eligibility surveillance based on unstructured clinical data.International journal of medical informatics · 2019Article
- Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications.Yearbook of medical informatics · 2019Review
- Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
objectivesThis paper reviews work over the past two years in Natural Language Processing (NLP) applied to clinical and consumer-generated texts.
methodsWe included any application or methodological publication that leverages text to facilitate healthcare and address the health-related needs of consumers and populations.
resultsMany important developments in clinical text processing, both foundational and task-oriented, were addressed in community- wide evaluations and discussed in corresponding special issues that are referenced in this review. These focused issues and in-depth reviews of several other active research areas, such as pharmacovigilance and summarization, allowed us to discuss in greater depth disease modeling and predictive analytics using clinical texts, and text analysis in social media for healthcare quality assessment, trends towards online interventions based on rapid analysis of health-related posts, and consumer health question answering, among other issues.
conclusionsOur analysis shows that although clinical NLP continues to advance towards practical applications and more NLP methods are used in large-scale live health information applications, more needs to be done to make NLP use in clinical applications a routine widespread reality. Progress in clinical NLP is mirrored by developments in social media text analysis: the research is moving from capturing trends to addressing individual health-related posts, thus showing potential to become a tool for precision medicine and a valuable addition to the standard healthcare quality evaluation tools.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.