ArticleJournal of biomedical informatics2015
A context-aware approach for progression tracking of medical concepts in electronic medical records.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Utilisation of Electronic Health Records for Public Health in Asia: A Review of Success Factors and Potential Challenges.BioMed research international · 2019Pooled it
- The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature.Journal of medical systems · 2018Pooled it
- From Innovation to Integration: Bridging the Gap Between IoMT Technologies and Real-World Health Management Systems.Sensors (Basel, Switzerland) · 2025Article
- Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records.NPJ digital medicine · 2025Article
- Application of Clinical Department-Specific AI-Assisted Coding Using Taiwan Diagnosis-Related Groups: Retrospective Validation Study.JMIR human factors · 2025Article
- Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.Computational and structural biotechnology journal · 2024Article
- Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.Scientific reports · 2023Article
- Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients.Frontiers in psychiatry · 2020Article
- Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Family member information extraction via neural sequence labeling models with different tag schemes.BMC medical informatics and decision making · 2019Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Data Processing and Text Mining Technologies on Electronic Medical Records: A Review.Journal of healthcare engineering · 2018Review
- Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients.Journal of biomedical informatics · 2017Article
- Automatic prediction of coronary artery disease from clinical narratives.Journal of biomedical informatics · 2017Article
- Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion.Database : the journal of biological databases and curation · 2016Article
- Coronary artery disease risk assessment from unstructured electronic health records using text mining.Journal of biomedical informatics · 2015Article
- Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Journal of biomedical informatics · 2015Article
- Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records.BioMed research international · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk factors such as high blood pressure, cholesterol levels, and smoking status. Discovering the described risk factors and tracking their progression over time may support medical personnel in making clinical decisions, as well as facilitate data modeling and biomedical research. Such highly patient-specific knowledge is essential to driving the advancement of evidence-based practice, and can also help improve personalized medicine and care. One general approach for tracking the progression of diseases and their risk factors described in EMRs is to first recognize all temporal expressions, and then assign each of them to the nearest target medical concept. However, this method may not always provide the correct associations. In light of this, this work introduces a context-aware approach to assign the time attributes of the recognized risk factors by reconstructing contexts that contain more reliable temporal expressions. The evaluation results on the i2b2 test set demonstrate the efficacy of the proposed approach, which achieved an F-score of 0.897. To boost the approach's ability to process unstructured clinical text and to allow for the reproduction of the demonstrated results, a set of developed .NET libraries used to develop the system is available at https://sites.google.com/site/hongjiedai/projects/nttmuclinicalnet.
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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.