ArticleScientific reports2016
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04589169 (Continuous Non-Invasive Eye Tracking for the Early Detection of Delirium on the Intensive Care Unit), which is not on this map. Cited by 498 papers, 6 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Continuous Non-Invasive Eye Tracking for the Early Detection of Delirium on the Intensive Care Unit
Who cites it
498 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Secondary use of health records for prediction, detection, and treatment planning in the clinical decision support system: a systematic review.BMC medical informatics and decision making · 2025Pooled 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
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Accuracy of deep neural learning models in the imaging prediction of pathological complete response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer: a systematic review.Langenbeck's archives of surgery · 2023Pooled it
- Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.Current neuropharmacology · 2023Pooled it
- Pooled it
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- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
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- Embeddings of clinical codes enable knowledge-grounded AI in medicine.NPJ digital medicine · 2026Article
- Review
438 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name "deep patient". We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.
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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.