ArticlePloS one2022
Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Automated Derivation of Cerebral Performance Category at Hospital Discharge After Cardiac Arrest Using Natural Language Processing and Machine Learning.Neurocritical care · 2026Article
- Understanding uncertainty in large language model predictions of early death in critically ill patients: a conformal prediction approach.JAMIA open · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models.Journal of medical Internet research · 2025Article
- Understanding Uncertainty in Large Language Model Predictions of Early Death in Critically Ill Patients: A Conformal Prediction Approach.medRxiv : the preprint server for health sciences · 2025Article
- Analyzing Information Disparities across Modalities in Mortality Prediction.medRxiv : the preprint server for health sciences · 2025Article
- Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review.Information (Basel) · 2025Article
- Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes.PloS one · 2025Article
- Lightweight transformers for clinical natural language processing.Natural language engineering · 2024Article
- The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.Japanese journal of radiology · 2024Review
- Question-answering system extracts information on injection drug use from clinical notes.Communications medicine · 2024Article
- Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions.Diagnostic and interventional radiology (Ankara, Turkey) · 2024Review
- Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty.Scientific reports · 2023Article
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
- A comparative study of pretrained language models for long clinical text.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Predicting medical events and ICU requirements using a multimodal multiobjective transformer network.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task.Bioinformatics (Oxford, England) · 2022Article
- Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases.Journal of personalized medicine · 2022Article
- Triptychon: Usability evaluation and implementation of a web-based application for patients' lab and vital parameters.Digital healthArticle
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7 authors.
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Abstract
Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning. However, very few studies rely solely on unstructured clinical notes from the EHR for mortality prediction. In this work, we propose a framework to predict short, mid, and long-term mortality in adult ICU patients using unstructured clinical notes from the MIMIC III database, natural language processing (NLP), and machine learning (ML) models. Depending on the statistical description of the patients' length of stay, we define the short-term as 48-hour and 4-day period, the mid-term as 7-day and 10-day period, and the long-term as 15-day and 30-day period after admission. We found that by only using clinical notes within the 24 hours of admission, our framework can achieve a high area under the receiver operating characteristics (AU-ROC) score for short, mid and long-term mortality prediction tasks. The test AU-ROC scores are 0.87, 0.83, 0.83, 0.82, 0.82, and 0.82 for 48-hour, 4-day, 7-day, 10-day, 15-day, and 30-day period mortality prediction, respectively. We also provide a comparative study among three types of feature extraction techniques from NLP: frequency-based technique, fixed embedding-based technique, and dynamic embedding-based technique. Lastly, we provide an interpretation of the NLP-based predictive models using feature-importance scores.
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