{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/benchmark-of-deep-learning-models-on-large","title":"Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets","arxiv_id":"1710.08531","date":"2017-10-23","proceeding":null,"authors":["Sanjay Purushotham","Chuizheng Meng","Zhengping Che","Yan Liu"],"abstract":"Deep learning models (aka Deep Neural Networks) have revolutionized many\nfields including computer vision, natural language processing, speech\nrecognition, and is being increasingly used in clinical healthcare\napplications. However, few works exist which have benchmarked the performance\nof the deep learning models with respect to the state-of-the-art machine\nlearning models and prognostic scoring systems on publicly available healthcare\ndatasets. In this paper, we present the benchmarking results for several\nclinical prediction tasks such as mortality prediction, length of stay\nprediction, and ICD-9 code group prediction using Deep Learning models,\nensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA\nscores. We used the Medical Information Mart for Intensive Care III (MIMIC-III)\n(v1.4) publicly available dataset, which includes all patients admitted to an\nICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the\nbenchmarking tasks. Our results show that deep learning models consistently\noutperform all the other approaches especially when the `raw' clinical time\nseries data is used as input features to the models.","url_abs":"http://arxiv.org/abs/1710.08531v1","url_pdf":"http://arxiv.org/pdf/1710.08531v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"benchmark-of-deep-learning-models-on-large","repo_url":"https://github.com/USC-Melady/Benchmarking_DL_MIMICIII","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"length-of-stay-prediction","task_name":"Length-of-Stay prediction"},{"task_slug":"mortality-prediction","task_name":"Mortality Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.08531","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}