ArticleBioMed research international2015
Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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Who cites it
14 citing papers in PubMed.
- Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.Scientific reports · 2024Article
- Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients with diabetes from routine visits' text.Scientific reports · 2023Article
- Machine learning based study for the classification of Type 2 diabetes mellitus subtypes.BioData mining · 2023Article
- Automated Detection of Substance-Use Status and Related Information from Clinical Text.Sensors (Basel, Switzerland) · 2022Article
- Application of multi-label classification models for the diagnosis of diabetic complications.BMC medical informatics and decision making · 2021Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Big data analytics for preventive medicine.Neural computing & applications · 2020Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- TheFrontiers in public health · 2018Article
- Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients.Journal of biomedical informatics · 2017Article
- Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Yearbook of medical informatics · 2017Review
- Measuring Quality of Healthcare Outcomes in Type 2 Diabetes from Routine Data: a Seven-nation Survey Conducted by the IMIA Primary Health Care Working Group.Yearbook of medical informatics · 2017Article
- Machine Learning and Data Mining Methods in Diabetes Research.Computational and structural biotechnology journal · 2017Review
- Big data analytics to improve cardiovascular care: promise and challenges.Nature reviews. Cardiology · 2016Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.
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Registered trials
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