TY - GEN
T1 - Learning Physician's Treatment for Alzheimer's Disease based on Electronic Health Records and Reinforcement Learning
AU - Bhattarai, Kritib
AU - Rajaganapathy, Sivaraman
AU - Das, Trisha
AU - Kim, Yejin
AU - Chen, Yongbin
AU - Dai, Qiying
AU - Li, Xiaoyang
AU - Jiang, Xiaoqian
AU - Zong, Nansu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Alzheimer's Disease (AD) is a progressive neurological disorder that necessitates physicians with sophisticated skills and knowledge to effectively care for AD patients. In this study, we adopted reinforcement learning (RL) to learn a physician's treatment plan for AD by utilizing Electronic Health Records (EHR). By defining states, actions, and rewards, we modeled the data of 1,736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) into an RL problem. We evaluated the RL-based learning model across four patient cohorts: the entire dataset, AD-only data, AD-hypertension data, and AD-hypertension-depression data. The RL learning models demonstrated promising outcomes in generating an optimal physician policy, which represents the treatment plan, in comparison to the clinician policy obtained from transitional probability. For instance, the q-learning-based policy achieved a score of -2.48, whereas the clinician policy scored -3.57. This research highlights the potential of RL-based treatment learning to enhance the management of Alzheimer's Disease.
AB - Alzheimer's Disease (AD) is a progressive neurological disorder that necessitates physicians with sophisticated skills and knowledge to effectively care for AD patients. In this study, we adopted reinforcement learning (RL) to learn a physician's treatment plan for AD by utilizing Electronic Health Records (EHR). By defining states, actions, and rewards, we modeled the data of 1,736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) into an RL problem. We evaluated the RL-based learning model across four patient cohorts: the entire dataset, AD-only data, AD-hypertension data, and AD-hypertension-depression data. The RL learning models demonstrated promising outcomes in generating an optimal physician policy, which represents the treatment plan, in comparison to the clinician policy obtained from transitional probability. For instance, the q-learning-based policy achieved a score of -2.48, whereas the clinician policy scored -3.57. This research highlights the potential of RL-based treatment learning to enhance the management of Alzheimer's Disease.
KW - Alzheimer's disease
KW - learning treatment
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85181578939&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85181578939&partnerID=8YFLogxK
U2 - 10.1109/ICHI57859.2023.00092
DO - 10.1109/ICHI57859.2023.00092
M3 - Conference contribution
AN - SCOPUS:85181578939
T3 - Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023
SP - 525
EP - 526
BT - Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference on Healthcare Informatics, ICHI 2023
Y2 - 26 June 2023 through 29 June 2023
ER -