ArticleJournal of medical Internet research2020
Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Deep representation learning for clustering longitudinal survival data from electronic health records.Nature communications · 2025Article
- Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.bioRxiv : the preprint server for biology · 2024Article
- Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.JCO clinical cancer informatics · 2024Article
- TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records.Nature communications · 2023Article
- Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study.Journal of medical Internet research · 2022Article
- Prediction algorithm for ICU mortality and length of stay using machine learning.Scientific reports · 2022Article
- Using data science to improve outcomes for persons with opioid use disorder.Substance abuse · 2022Article
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.Informatics (MDPI) · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundScalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications).
objectiveThis study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient's mortality using their longitudinal EHR data.
methodsWe built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient's encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians' input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models.
resultsExperiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models (<0.86). In addition, physicians' agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model.
conclusionsOur results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality.
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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.