ArticleHealth informatics journal2020
Natural language processing of lifestyle modification documentation.
Article in Health informatics journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Feasibility Assessment of Extracting Social Determinants of Health From Electronic Medical Records: A Multicenter Study Across Chinese Healthcare Institutions.Health care science · 2026Article
- Management of Patients With Comorbid Asthma and Obesity: A Large Language Model Evaluation of Clinical Documentation.The journal of allergy and clinical immunology. In practice · 2026Article
- Phenotyping to predict 12-month health outcomes of older general medicine patients.Aging clinical and experimental research · 2025Article
- Electronic health record data extraction: Physical therapists' documentation of physical activity assessments and prescriptions for patients with chronic low back pain.Physiotherapy theory and practice · 2024Article
- Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Extracting social determinants of health events with transformer-based multitask, multilabel named entity recognition.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Automatic extraction of social determinants of health from medical notes of chronic lower back pain patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Design considerations for a hierarchical semantic compositional framework for medical natural language understanding.PloS one · 2023Article
- Leveraging electronic health record data for endometriosis research.Frontiers in digital health · 2023Review
- Play the Pain: A Digital Strategy for Play-Oriented Research and Action.Frontiers in psychiatry · 2021Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
- Identifying patterns and predictors of lifestyle modification in electronic health record documentation using statistical and machine learning methods.Preventive medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Lifestyle modification, including diet, exercise, and tobacco cessation, is the first-line treatment of many disorders including hypertension, obesity, and diabetes. Lifestyle modification data are not easily retrieved or used in research due to their textual nature. This study addresses this knowledge gap using natural language processing to automatically identify lifestyle modification documentation from electronic health records. Electronic health record notes from hypertension patients were analyzed using an open-source natural language processing tool to retrieve assessment and advice regarding lifestyle modification. These data were classified as lifestyle modification assessment or advice and mapped to a coded standard ontology. Combined lifestyle modification (advice and assessment) recall was 99.27 percent, precision 94.44 percent, and correct classification 88.15 percent. Through extraction and transformation of narrative lifestyle modification data to coded data, this critical information can be used in research, metric development, and quality improvement efforts regarding care delivery for multiple medical conditions that benefit from lifestyle modification.
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.