ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012
Risk stratification of ICU patients using topic models inferred from unstructured progress notes.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Natural Language Processing of Nursing Notes: An Integrative Review.Computers, informatics, nursing : CIN · 2023Review
- Classification of neurologic outcomes from medical notes using natural language processing.Expert systems with applications · 2023Article
- Current status and trends in researches based on public intensive care databases: A scientometric investigation.Frontiers in public health · 2022Article
- Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation.JMIR medical informatics · 2021Article
- Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing.JMIR medical informatics · 2021Article
- Keyword extraction and structuralization of medical reports.Health information science and systems · 2020Article
- Cohort selection for clinical trials using hierarchical neural network.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Construct validity of six sentiment analysis methods in the text of encounter notes of patients with critical illness.Journal of biomedical informatics · 2019Article
- Making sense of abbreviations in nursing notes: A case study on mortality prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.JAMA network open · 2018Article
- Big Data and Data Science in Critical Care.Chest · 2018Review
- Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.Critical care medicine · 2018Article
- Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of snomed codes.Journal of biomedical informatics · 2018Article
- Accurate and interpretable intensive care risk adjustment for fused clinical data with generalized additive models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- What's in a Note? Unpacking Predictive Value in Clinical Note Representations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.IEEE journal of biomedical and health informatics · 2017Article
- Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.Journal of cardiovascular translational research · 2017Article
- Phenotyping Hypotensive Patients in Critical Care Using Hospital Discharge Summaries.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2017Article
- Estimating Patient's Health State Using Latent Structure Inferred from Clinical Time Series and Text.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We propose a novel approach for ICU patient risk stratification by combining the learned "topic" structure of clinical concepts (represented by UMLS codes) extracted from the unstructured nursing notes with physiologic data (from SAPS-I) for hospital mortality prediction. We used Hierarchical Dirichlet Processes (HDP), a non-parametric topic modeling technique, to automatically discover "topics" as shared groups of co-occurring UMLS clinical concepts. We evaluated the potential utility of the inferred topic structure in predicting hospital mortality using the nursing notes of 14,739 adult ICU patients (mortality 14.6%) from the MIMIC II database. Our results indicate that learned topic structure from the first 24-hour ICU nursing notes significantly improved the performance of the SAPS-I algorithm for hospital mortality prediction. The AUC for predicting hospital mortality from the first 24 hours of physiologic data and nursing text notes was 0.82. Using the physiologic data alone with the SAPS-I algorithm, an AUC of 0.72 was achieved. Thus, the clinical topics that were extracted and used to augment the SAPS-I algorithm significantly improved the performance of the baseline algorithm.
Indexed as
Identifiers
23304322PMC3540429What 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.