ArticleJournal of the American Medical Informatics Association : JAMIA2016
Classification of radiology reports for falls in an HIV study cohort.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 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
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2023Pooled it
- The reporting quality of natural language processing studies: systematic review of studies of radiology reports.BMC medical imaging · 2021Pooled it
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Article
- Gerontologic Biostatistics and Data Science: Aging Research in the Era of Big Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness.Journal of biomedical informatics · 2024Article
- Assessing the contributions of modifiable risk factors to serious falls and fragility fractures among older persons living with HIV.Journal of the American Geriatrics Society · 2023Article
- Predicting future falls in older people using natural language processing of general practitioners' clinical notes.Age and ageing · 2023Article
- Multilingual RECIST classification of radiology reports using supervised learning.Frontiers in digital health · 2023Article
- Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports.Frontiers in digital health · 2023Article
- The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis.JMIR medical informatics · 2021Review
- Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data.Journal of cardiovascular pharmacology and therapeutics · 2021Article
- Serious Falls in Middle-Aged Veterans: Development and Validation of a Predictive Risk Model.Journal of the American Geriatrics Society · 2020Article
- Natural Language Processing for Surveillance of Cervical and Anal Cancer and Precancer: Algorithm Development and Split-Validation Study.JMIR medical informatics · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Polypharmacy, Hazardous Alcohol and Illicit Substance Use, and Serious Falls Among PLWH and Uninfected Comparators.Journal of acquired immune deficiency syndromes (1999) · 2019Article
- Robust clinical marker identification for diabetic kidney disease with ensemble feature selection.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- A Framework of Rebalancing Imbalanced Healthcare Data for Rare Events' Classification: A Case of Look-Alike Sound-Alike Mix-Up Incident Detection.Journal of healthcare engineering · 2018Article
- Towards the Inference of Social and Behavioral Determinants of Sexual Health: Development of a Gold-Standard Corpus with Semi-Supervised Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.PloS one · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveTo identify patients in a human immunodeficiency virus (HIV) study cohort who have fallen by applying supervised machine learning methods to radiology reports of the cohort.
methodsWe used the Veterans Aging Cohort Study Virtual Cohort (VACS-VC), an electronic health record-based cohort of 146 530 veterans for whom radiology reports were available (N=2 977 739). We created a reference standard of radiology reports, represented each report by a feature set of words and Unified Medical Language System concepts, and then developed several support vector machine (SVM) classifiers for falls. We compared mutual information (MI) ranking and embedded feature selection approaches. The SVM classifier with MI feature selection was chosen to classify all radiology reports in VACS-VC.
resultsOur SVM classifier with MI feature selection achieved an area under the curve score of 97.04 on the test set. When applied to all the radiology reports in VACS-VC, 80 416 of these reports were classified as positive for a fall. Of these, 11 484 were associated with a fall-related external cause of injury code (E-code) and 68 932 were not, corresponding to 29 280 patients with potential fall-related injuries who could not have been found using E-codes. DISCUSSION: Feature selection was crucial to improving the classifier's performance. Feature selection with MI allowed us to select the number of discriminative features to use for classification, in contrast to the embedded feature selection method, in which the number of features is chosen automatically.
conclusionMachine learning is an effective method of identifying patients who have suffered a fall. The development of this classifier supplements the clinical researcher's toolkit and reduces dependence on under-coded structured electronic health record data.
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.