ArticleBMC medical informatics and decision making2013
Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records.
Article in BMC medical informatics and decision making, 2013. 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.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Extracting information from the text of electronic medical records to improve case detection: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016Pooled it
- Predicting Metabolic Syndrome Using Supervised Machine Learning: A Multivariate Parameter Approach.International journal of molecular sciences · 2025Article
- Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers.PloS one · 2024Article
- Prediction of obstructive coronary artery disease in patients undergoing heart valve surgery: A cross-sectional study in a tertiary care hospital.Journal of cardiovascular and thoracic research · 2023Article
- Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model.Scientific reports · 2022Article
- Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Metabolic Syndrome Prediction Models Using Machine Learning and Sasang Constitution Type.Evidence-based complementary and alternative medicine : eCAM · 2021Article
- Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia.Brain informatics · 2020Review
- An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Use of Machine Learning Techniques for Case-Detection of Varicella Zoster Using Routinely Collected Textual Ambulatory Records: Pilot Observational Study.JMIR medical informatics · 2020Article
- Automated detection of altered mental status in emergency department clinical notes: a deep learning approach.BMC medical informatics and decision making · 2019Article
- Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables.International journal of environmental research and public health · 2019Article
- How to validate a diagnosis recorded in electronic health records.Breathe (Sheffield, England) · 2019Article
- Generating and evaluating a propensity model using textual features from electronic medical records.PloS one · 2019Article
- Symptom Signatures and Diagnostic Timeliness in Cancer Patients: A Review of Current Evidence.Neoplasia (New York, N.Y.) · 2018Review
- Article
- TextHunter--A User Friendly Tool for Extracting Generic Concepts from Free Text in Clinical Research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundDistinguishing cases from non-cases in free-text electronic medical records is an important initial step in observational epidemiological studies, but manual record validation is time-consuming and cumbersome. We compared different approaches to develop an automatic case identification system with high sensitivity to assist manual annotators.
methodsWe used four different machine-learning algorithms to build case identification systems for two data sets, one comprising hepatobiliary disease patients, the other acute renal failure patients. To improve the sensitivity of the systems, we varied the imbalance ratio between positive cases and negative cases using under- and over-sampling techniques, and applied cost-sensitive learning with various misclassification costs.
resultsFor the hepatobiliary data set, we obtained a high sensitivity of 0.95 (on a par with manual annotators, as compared to 0.91 for a baseline classifier) with specificity 0.56. For the acute renal failure data set, sensitivity increased from 0.69 to 0.89, with specificity 0.59. Performance differences between the various machine-learning algorithms were not large. Classifiers performed best when trained on data sets with imbalance ratio below 10.
conclusionsWe were able to achieve high sensitivity with moderate specificity for automatic case identification on two data sets of electronic medical records. Such a high-sensitive case identification system can be used as a pre-filter to significantly reduce the burden of manual record validation.
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.