New Results
Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease
View ORCID ProfileAndrew J. Steele, S. Aylin Cakiroglu, View ORCID ProfileAnoop D. Shah, View ORCID ProfileSpiros C. Denaxas, View ORCID ProfileHarry Hemingway, View ORCID ProfileNicholas M. Luscombe
doi: https://doi.org/10.1101/256008
Andrew J. Steele
1The Francis Crick Institute, London, United Kingdom
S. Aylin Cakiroglu
1The Francis Crick Institute, London, United Kingdom
Anoop D. Shah
2Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London, United Kingdom
3University College London Hospitals NHS Foundation Trust, London, United Kingdom
Spiros C. Denaxas
2Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London, United Kingdom
Harry Hemingway
2Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London, United Kingdom
Nicholas M. Luscombe
1The Francis Crick Institute, London, United Kingdom
4UCL Genetics Institute, Department of Genetics Evolution and Environment, University College London, London, United Kingdom
5Okinawa Institute of Science & Technology Graduate University, Okinawa, Japan
Article usage
Posted January 30, 2018.
Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease
Andrew J. Steele, S. Aylin Cakiroglu, Anoop D. Shah, Spiros C. Denaxas, Harry Hemingway, Nicholas M. Luscombe
bioRxiv 256008; doi: https://doi.org/10.1101/256008
Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease
Andrew J. Steele, S. Aylin Cakiroglu, Anoop D. Shah, Spiros C. Denaxas, Harry Hemingway, Nicholas M. Luscombe
bioRxiv 256008; doi: https://doi.org/10.1101/256008
Subject Area
Subject Areas
- Biochemistry (11753)
- Bioengineering (8752)
- Bioinformatics (29201)
- Biophysics (14974)
- Cancer Biology (12100)
- Cell Biology (17413)
- Clinical Trials (138)
- Developmental Biology (9422)
- Ecology (14182)
- Epidemiology (2067)
- Evolutionary Biology (18309)
- Genetics (12245)
- Genomics (16804)
- Immunology (11869)
- Microbiology (28098)
- Molecular Biology (11596)
- Neuroscience (60975)
- Paleontology (451)
- Pathology (1871)
- Pharmacology and Toxicology (3238)
- Physiology (4959)
- Plant Biology (10427)
- Synthetic Biology (2886)
- Systems Biology (7340)
- Zoology (1651)