Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile
Background. Although many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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Oxford University Press
2016
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| Online Access: | http://hdl.handle.net/1721.1/100700 https://orcid.org/0000-0003-0992-0906 |
| _version_ | 1826211132063350784 |
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| author | Wiens, Jenna Campbell, Wayne N. Franklin, Ella S. Guttag, John V. Horvitz, Eric |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Wiens, Jenna Campbell, Wayne N. Franklin, Ella S. Guttag, John V. Horvitz, Eric |
| author_sort | Wiens, Jenna |
| collection | MIT |
| description | Background. Although many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability that an inpatient will test positive for C difficile.
Methods. We consider electronic medical record (EMR) data from patients admitted for ≥24 hours to a large urban hospital in the U.S. between April 2011 and April 2013. Predictive models were constructed using L2-regularized logistic regression and data from the first year. The number of observational variables considered varied from a small set of well known risk factors readily available to a physician to over 10 000 variables automatically extracted from the EMR. Each model was evaluated on holdout admission data from the following year. A total of 34 846 admissions with 372 cases of CDI was used to train the model.
Results. Applied to the separate validation set of 34 722 admissions with 355 cases of CDI, the model that made use of the additional EMR data yielded an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% confidence interval [CI], .79–.83), and it significantly outperformed the model that considered only the small set of known clinical risk factors, AUROC of 0.71 (95% CI, .69–.75).
Conclusions. Automated risk stratification of patients based on the contents of their EMRs can be used to accurately identify a high-risk population of patients. The proposed method holds promise for enabling the selective allocation of interventions aimed at reducing the rate of CDI. |
| first_indexed | 2024-09-23T15:01:05Z |
| format | Article |
| id | mit-1721.1/100700 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:01:05Z |
| publishDate | 2016 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/1007002022-10-02T00:01:26Z Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile Wiens, Jenna Campbell, Wayne N. Franklin, Ella S. Guttag, John V. Horvitz, Eric Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Wiens, Jenna Guttag, John V. Background. Although many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability that an inpatient will test positive for C difficile. Methods. We consider electronic medical record (EMR) data from patients admitted for ≥24 hours to a large urban hospital in the U.S. between April 2011 and April 2013. Predictive models were constructed using L2-regularized logistic regression and data from the first year. The number of observational variables considered varied from a small set of well known risk factors readily available to a physician to over 10 000 variables automatically extracted from the EMR. Each model was evaluated on holdout admission data from the following year. A total of 34 846 admissions with 372 cases of CDI was used to train the model. Results. Applied to the separate validation set of 34 722 admissions with 355 cases of CDI, the model that made use of the additional EMR data yielded an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% confidence interval [CI], .79–.83), and it significantly outperformed the model that considered only the small set of known clinical risk factors, AUROC of 0.71 (95% CI, .69–.75). Conclusions. Automated risk stratification of patients based on the contents of their EMRs can be used to accurately identify a high-risk population of patients. The proposed method holds promise for enabling the selective allocation of interventions aimed at reducing the rate of CDI. National Science Foundation (U.S.) Quanta Computer (Firm) Natural Sciences and Engineering Research Council of Canada 2016-01-05T18:54:24Z 2016-01-05T18:54:24Z 2014-06 2014-04 Article http://purl.org/eprint/type/JournalArticle 2328-8957 http://hdl.handle.net/1721.1/100700 Wiens, J., W. N. Campbell, E. S. Franklin, J. V. Guttag, and E. Horvitz. “Learning Data-Driven Patient Risk Stratification Models for Clostridium Difficile.” Open Forum Infectious Diseases 1, no. 2 (June 18, 2014): ofu045–ofu045. https://orcid.org/0000-0003-0992-0906 en_US http://dx.doi.org/10.1093/ofid/ofu045 Open Forum Infectious Diseases Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Oxford University Press Oxford University Press |
| spellingShingle | Wiens, Jenna Campbell, Wayne N. Franklin, Ella S. Guttag, John V. Horvitz, Eric Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile |
| title | Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile |
| title_full | Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile |
| title_fullStr | Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile |
| title_full_unstemmed | Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile |
| title_short | Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile |
| title_sort | learning data driven patient risk stratification models for clostridium difficile |
| url | http://hdl.handle.net/1721.1/100700 https://orcid.org/0000-0003-0992-0906 |
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