ArticleBMC medical informatics and decision making2020
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 3 of them syntheses that pooled it.
What it found
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Who cites it
51 citing papers in PubMed, 3 syntheses or guidelines pooled it, 106 citations in OpenAlex.
- Impact of artificial intelligence on electronic health record-related burnouts among healthcare professionals: systematic review.Frontiers in public health · 2025Pooled it
- Interventions and contextual factors to improve retention in care for patients with hypertension in primary care: Hermeneutic systematic review.Preventive medicine · 2024Pooled it
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Early Detection of Clinical Deterioration in ICU Patients With Respiratory Failure Receiving Vasoactive Support: A Machine Learning Approach to Identifying High-Risk Individuals.Health science reports · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Pediatric diabetes prediction using machine learning.Scientific reports · 2026Article
- Enhancing diabetes risk stratification through natural language processing: a multimodal data integration approach.Frontiers in public health · 2026Article
- Comparison of rule- and large language model-based phenotype extraction from clinical notes for neurofibromatosis type 1.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Article
- Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes.PloS one · 2025Article
- Not Fully Synthetic: LLM-based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Using shared clinical decision support to reduce adverse drug events and improve patient safety.Frontiers in digital health · 2025Review
- Predicting survival time for critically ill patients with heart failure using conformalized survival analysis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction.Frontiers in digital health · 2025Article
- Machine learning-based mortality prediction in critically ill patients with hypertension: comparative analysis, fairness, and interpretability.Frontiers in artificial intelligence · 2025Article
- Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models.Frontiers in cardiovascular medicine · 2025Article
- De-identification is not enough: a comparison between de-identified and synthetic clinical notes.Scientific reports · 2024Article
- Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- Lightweight transformers for clinical natural language processing.Natural language engineering · 2024Article
- Transforming and facilitating health care delivery through social networking platforms: evidences and implications from WeChat.JAMIA open · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundDiabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality.
methodsWe conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations.
resultsThe best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients.
conclusionUMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.
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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.