IS in Healthcare
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Paper Number
1403
Paper Type
short
Description
Observational data, such as electronic medical records in healthcare, have provided new opportunities for clinical discoveries. However, analyses using observational data are challenging due to the absence of a control group and selection bias between treatment groups. We propose a personalized treatment using counterfactual regression based on deep learning, which reduces concerns related to selection bias in a technical way. Using the data on the use of biologics in patients with rheumatoid arthritis, we estimate the individual treatment effect to determine which drugs to prescribe. In addition, we compare the individual treatment effect for each variable to show the relationship between clinical variables and the drug effectiveness. This study contributes to establishing evidence-based guidelines that recommend the optimal drug for each patient, thereby improving treatment responses clinically.
Recommended Citation
Eun, Seongho; Koo, Bon San; Oh, Ji Seon; Kim, Kee-Eung; and Lee, Byungtae, "Personalized Treatment Using Biologics: An Analysis Using Counterfactual Regression Based on Deep Learning" (2021). ICIS 2021 Proceedings. 4.
https://aisel.aisnet.org/icis2021/is_health/is_health/4
Personalized Treatment Using Biologics: An Analysis Using Counterfactual Regression Based on Deep Learning
Observational data, such as electronic medical records in healthcare, have provided new opportunities for clinical discoveries. However, analyses using observational data are challenging due to the absence of a control group and selection bias between treatment groups. We propose a personalized treatment using counterfactual regression based on deep learning, which reduces concerns related to selection bias in a technical way. Using the data on the use of biologics in patients with rheumatoid arthritis, we estimate the individual treatment effect to determine which drugs to prescribe. In addition, we compare the individual treatment effect for each variable to show the relationship between clinical variables and the drug effectiveness. This study contributes to establishing evidence-based guidelines that recommend the optimal drug for each patient, thereby improving treatment responses clinically.
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