Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU
| dc.contributor.author | Gani, Md Osman | |
| dc.contributor.author | Kethireddy, Shravan | |
| dc.contributor.author | Bikak, Marvi | |
| dc.contributor.author | Griffin, Paul | |
| dc.contributor.author | Adibuzzaman, Mohammad | |
| dc.date.accessioned | 2020-12-09T18:48:43Z | |
| dc.date.available | 2020-12-09T18:48:43Z | |
| dc.date.issued | 2020-10-28 | |
| dc.description.abstract | Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identification of causal effects from observational data in the cases where the causal graph is identifiable, i.e., the data generating mechanism can be recovered from the joint distribution. However, no such studies have been done to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effect from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and important research question, i.e., the effect of oxygen therapy intervention in the intensive care unit (ICU); the result of this project is useful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC III database, a standard database in the machine learning community that contains 58,976 admissions from an ICU in Boston, MA, for estimating the oxygen therapy effect on morality. We also identified the covariate-specific effect to oxygen therapy from the model for more personalized intervention. | en_US |
| dc.description.sponsorship | This study was partially supported by the Regenstrief Center for Healthcare Engineering at Purdue University. We would like to express our sincere gratitude to Professor Elias Bareinboim for his insights on the methodological framework. Part of the analysis (Appendix Appendix B) was achieved with the causalfusion.net software developed by Dr. Bareinboim | en_US |
| dc.description.uri | https://arxiv.org/abs/2010.14774 | en_US |
| dc.format.extent | 46 pages | en_US |
| dc.genre | journal articles preprints | en_US |
| dc.identifier | doi:10.13016/m2tqay-zzzz | |
| dc.identifier.citation | Md Osman Gani, Shravan Kethireddy, Marvi Bikak, Paul Griffin and Mohammad Adibuzzaman, Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU, https://arxiv.org/abs/2010.14774 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/20217 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.title | Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU | en_US |
| dc.type | Text | en_US |