ArticleJMLR workshop and conference proceedings2016
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks.
Article in JMLR workshop and conference proceedings, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06447532 (Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients), which is not on this map. Cited by 280 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
Who cites it
280 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- 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 of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- CURENet: combining unified representations for efficient chronic disease prediction.Health information science and systems · 2026Article
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- Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- A data-driven and interpretable hybrid quantum-classical model for male infertility risk assessment.Frontiers in medical technology · 2026Article
220 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions (one label for each diagnosis or medication category). Based on separate blind test set evaluation, Doctor AI can perform differential diagnosis with up to 79% recall@30, significantly higher than several baselines. Moreover, we demonstrate great generalizability of Doctor AI by adapting the resulting models from one institution to another without losing substantial accuracy.
Identifiers
28286600PMC5341604What 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.