TY - GEN
T1 - Consensus Modeling
T2 - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020
AU - Tourani, Roshan
AU - Murphree, Dennis H.
AU - Zhu, Ying
AU - Sheka, Adam
AU - Melton, Genevieve B.
AU - Kor, Daryl J.
AU - Simon, Gyorgy J.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - The adoption of predictive modeling for clinical decision support is accelerating in healthcare, however, the need for large sample sizes puts smaller health systems at a disadvantage. Small health systems have insufficient positive cases to build models are left with three choices. First, they can obtain already trained models, which are often too generic. Second, they can participate in research networks, building a model through a network-wide data set. Since small hospitals can only contribute small amounts of data influencing the resulting shared model minimally, this approach yields only minimal specialization. The third option is transfer learning, where a model previously trained on a large population is refined to the specific population, which carries the danger of over-specializing to the idiosyncrasies of the small data set. In this paper, we present a novel paradigm, consensus modeling, that allows a small health system to collaborate with a larger system to build a model specific to the smaller system without sharing any data instances. The method is similar to transfer learning in that it refines models from the larger system to be specific to the small system, but through iterative refinement, the larger system alleviates the risk of over-specializing to the small system. We evaluated the approach on predicting postoperative complications at two health systems with 9,044 and 38,545 patients. The model obtained from the proposed consensus modeling paradigm achieved a predictive performance on the small system that is as good as the transfer learning approach (AUC 0.71 vs 0.71) but significantly outperformed the transfer learning approach on the large dataset (AUC 0.80 vs 0.65) suggesting significantly reduced over-specializing.
AB - The adoption of predictive modeling for clinical decision support is accelerating in healthcare, however, the need for large sample sizes puts smaller health systems at a disadvantage. Small health systems have insufficient positive cases to build models are left with three choices. First, they can obtain already trained models, which are often too generic. Second, they can participate in research networks, building a model through a network-wide data set. Since small hospitals can only contribute small amounts of data influencing the resulting shared model minimally, this approach yields only minimal specialization. The third option is transfer learning, where a model previously trained on a large population is refined to the specific population, which carries the danger of over-specializing to the idiosyncrasies of the small data set. In this paper, we present a novel paradigm, consensus modeling, that allows a small health system to collaborate with a larger system to build a model specific to the smaller system without sharing any data instances. The method is similar to transfer learning in that it refines models from the larger system to be specific to the small system, but through iterative refinement, the larger system alleviates the risk of over-specializing to the small system. We evaluated the approach on predicting postoperative complications at two health systems with 9,044 and 38,545 patients. The model obtained from the proposed consensus modeling paradigm achieved a predictive performance on the small system that is as good as the transfer learning approach (AUC 0.71 vs 0.71) but significantly outperformed the transfer learning approach on the large dataset (AUC 0.80 vs 0.65) suggesting significantly reduced over-specializing.
KW - Hospital acquired infection
KW - Machine learning
KW - Predictive modeling
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85092257544&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092257544&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59137-3_17
DO - 10.1007/978-3-030-59137-3_17
M3 - Conference contribution
AN - SCOPUS:85092257544
SN - 9783030591366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 181
EP - 191
BT - Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
A2 - Michalowski, Martin
A2 - Moskovitch, Robert
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 25 August 2020 through 28 August 2020
ER -