{"doi":"10.1093/jamia/ocad135","title":"Using artificial intelligence to learn optimal regimen plan for Alzheimer’s disease","abstract":"BACKGROUND: Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population. OBJECTIVE: Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records. METHODS: In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards. RESULTS: Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician's policy (mean -3.03 and -2.93 vs. -2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean -4.68 and -2.82 vs. -4.57, respectively). CONCLUSIONS: Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities.","journal":"Journal of the American Medical Informatics Association","year":2023,"id":357914,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.954,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":950958,"name":"Sivaraman Rajaganapathy","orcid":"0000-0003-4582-7274","position":1,"is_corresponding":false},{"id":1107719,"name":"Trisha Das","orcid":"0009-0008-4770-0297","position":2,"is_corresponding":false},{"id":285041,"name":"Yejin Kim","orcid":"0000-0001-7815-6310","position":3,"is_corresponding":false},{"id":966680,"name":"Yongbin Chen","orcid":"0000-0001-9359-6525","position":4,"is_corresponding":false},{"id":265450,"name":"the Alzheimer’s Disease Neuroimaging Initiative","orcid":null,"position":5,"is_corresponding":false},{"id":558150,"name":"the Australian Imaging Biomarkers and Lifestyle flagship study of ageing","orcid":null,"position":6,"is_corresponding":false},{"id":1010611,"name":"Qiying Dai","orcid":"0009-0007-3237-4954","position":7,"is_corresponding":false},{"id":1107720,"name":"Xiaoyang Li","orcid":"0009-0003-1935-6656","position":8,"is_corresponding":false},{"id":66208,"name":"Xiaoqian Jiang","orcid":"0000-0001-9933-2205","position":9,"is_corresponding":false},{"id":18261,"name":"Nansu Zong","orcid":"0000-0003-0066-9524","position":10,"is_corresponding":false},{"id":1108037,"name":"Kritib Bhattarai","orcid":null,"position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T01:13:39.186262Z","pmid":"37463858","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}