Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions
We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient’s possible future medical conditions given the patient’s current and past history of reported conditions. The core of our technique is a Bayesian hierarchical model for selecti...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Mathematical Statistics
2012
|
| Online Access: | http://hdl.handle.net/1721.1/75394 |
| _version_ | 1811083472299622400 |
|---|---|
| author | McCormick, Tyler H. Rudin, Cynthia Madigan, David |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management McCormick, Tyler H. Rudin, Cynthia Madigan, David |
| author_sort | McCormick, Tyler H. |
| collection | MIT |
| description | We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient’s possible future medical conditions given the patient’s current and past history of reported conditions. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as “condition 1 and condition 2 → condition 3”) from a large set of candidate rules. Because this method “borrows strength” using the conditions of many similar patients, it is able to provide predictions specialized to any given patient, even when little information about the patient’s history of conditions is available. |
| first_indexed | 2024-09-23T12:33:37Z |
| format | Article |
| id | mit-1721.1/75394 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:33:37Z |
| publishDate | 2012 |
| publisher | Institute of Mathematical Statistics |
| record_format | dspace |
| spelling | mit-1721.1/753942022-09-28T08:36:55Z Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions McCormick, Tyler H. Rudin, Cynthia Madigan, David Sloan School of Management Rudin, Cynthia We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient’s possible future medical conditions given the patient’s current and past history of reported conditions. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as “condition 1 and condition 2 → condition 3”) from a large set of candidate rules. Because this method “borrows strength” using the conditions of many similar patients, it is able to provide predictions specialized to any given patient, even when little information about the patient’s history of conditions is available. National Science Foundation (U.S.) (NSF Grant IIS-10-53407) Google (Firm) (Ph.D. fellowship in statistics) 2012-12-11T17:21:11Z 2012-12-11T17:21:11Z 2012 2011-10 Article http://purl.org/eprint/type/JournalArticle 1932-6157 Zentralblatt MATH identifier: 06062734 http://hdl.handle.net/1721.1/75394 McCormick, Tyler H., Cynthia Rudin, and David Madigan. “Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions.” The Annals of Applied Statistics 6.2 (2012): 652–668. Web. en_US http://dx.doi.org/10.1214/11-aoas522 Annals of Applied Statistics Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Institute of Mathematical Statistics Other University Web Domain |
| spellingShingle | McCormick, Tyler H. Rudin, Cynthia Madigan, David Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions |
| title | Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions |
| title_full | Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions |
| title_fullStr | Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions |
| title_full_unstemmed | Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions |
| title_short | Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions |
| title_sort | bayesian hierarchical rule modeling for predicting medical conditions |
| url | http://hdl.handle.net/1721.1/75394 |
| work_keys_str_mv | AT mccormicktylerh bayesianhierarchicalrulemodelingforpredictingmedicalconditions AT rudincynthia bayesianhierarchicalrulemodelingforpredictingmedicalconditions AT madigandavid bayesianhierarchicalrulemodelingforpredictingmedicalconditions |