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Benchmarking deep learning models on large healthcare datasets
Open accessAbstract
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models.
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“Late Fusion (LF) with simple concatenation was the best-performing model in the evaluations conducted by the previous paper that used the exact same partition as ours [129].”
From MultiBench: Multiscale Benchmarks for Multimodal Representation Learning (2021) · cites this paper as [129]
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Metadata
- Authors
- Purushotham, S., Meng, C., Che, Z., & Liu, Y.
- Affiliations
- University of Southern California (US)
- Tsinghua University (CN)
- Corresponding author
- Liu, Yan
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 83
- Pages
- 112-134
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2806031239
- Cited by
- 406
- References
- 57
- Citations by year
- 2026: 37 · 2025: 57 · 2024: 39 · 2023: 51 · 2022: 66 · 2021: 66 · 2020: 50 · 2019: 36 · 2018: 4
- FWCI
- 21.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Funders
- NSF (IIS-1134990, IIS-1254206)
- Samsung GRO Grant
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Keywords
- deep learning benchmarking, clinical prediction, mortality prediction, length of stay prediction, MIMIC-III, intensive care unit patients, super learner, SAPS II, SOFA score, clinical time series, prognostic score
- MeSH terms
- Deep Learning, Aged, Aged, 80 and over, Algorithms, Female, Forecasting, Humans, Intensive Care Units, Length of Stay, Male, Middle Aged, Databases, Factual, Neural Networks, Computer, Hospital Mortality, Benchmarking, International Classification of Diseases
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