Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
Open accessAbstract
Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We present a deep learning pipeline that uses all uncurated chart, lab, and output events for prediction of in-hospital mortality without variable selection. Over 21,000 ICU patients and tens of thousands of variables derived from the MIMIC-III database were used to train and validate our model. Recordings in the first few hours of a patient’s stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores, AUROC 0.72 and 0.76 at 24 h respectively, within just 12 h of ICU admission. Our model achieves a very strong predictive performance of AUROC 0.85 (95% CI 0.83–0.86) after 48 h. Predictive performance increases over the first 48 h, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.
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- Authors
- Deasy, J., Liò, P., & Ercole, A.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Deasy, Jacob
- ORCID
- Ercole, Ari
- Venue
- Scientific Reports
- Published
- 2020-12-17
- Volume
- 10
- Issue
- 1
- Pages
- 22129-22129
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7747558
- OpenAlex
- W3111842226
- Cited by
- 52
- References
- 41
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 8 · 2023: 9 · 2022: 9 · 2021: 3
- FWCI
- 2.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Medical Research Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- in-hospital mortality prediction, intensive care unit, heterogeneous time series, electronic health records, deep learning, MIMIC-III, early mortality prediction, clinical decision-making, feature selection, SAPS II
- MeSH terms
- Algorithms, Critical Care, Humans, Intensive Care Units, Models, Theoretical, Patient Admission, ROC Curve, Time Factors, United States, Reproducibility of Results, Hospital Mortality, Health Care Surveys, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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