{"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/multi-task-prediction-of-disease-onsets-from","title":"Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests","arxiv_id":"1608.00647","date":"2016-08-02","proceeding":null,"authors":["Narges Razavian","Jake Marcus","David Sontag"],"abstract":"Disparate areas of machine learning have benefited from models that can take\nraw data with little preprocessing as input and learn rich representations of\nthat raw data in order to perform well on a given prediction task. We evaluate\nthis approach in healthcare by using longitudinal measurements of lab tests,\none of the more raw signals of a patient's health state widely available in\nclinical data, to predict disease onsets. In particular, we train a Long\nShort-Term Memory (LSTM) recurrent neural network and two novel convolutional\nneural networks for multi-task prediction of disease onset for 133 conditions\nbased on 18 common lab tests measured over time in a cohort of 298K patients\nderived from 8 years of administrative claims data. We compare the neural\nnetworks to a logistic regression with several hand-engineered, clinically\nrelevant features. We find that the representation-based learning approaches\nsignificantly outperform this baseline. We believe that our work suggests a new\navenue for patient risk stratification based solely on lab results.","url_abs":"http://arxiv.org/abs/1608.00647v3","url_pdf":"http://arxiv.org/pdf/1608.00647v3.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":"multi-task-prediction-of-disease-onsets-from","repo_url":"https://github.com/clinicalml/deepDiagnosis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}