{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T21:33:01Z","timestamp":1770845581794,"version":"3.50.1"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,15]]},"DOI":"10.1109\/bibm66473.2025.11356287","type":"proceedings-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:19:40Z","timestamp":1769721580000},"page":"5400-5407","source":"Crossref","is-referenced-by-count":0,"title":["BIO-DQNA: Meta-Learning and Contrastive Reinforcement Learning for Personalized Comorbidity Management in Type 1 Diabetes and Hypertension"],"prefix":"10.1109","author":[{"given":"Jamell","family":"Dacon","sequence":"first","affiliation":[{"name":"Morgan State University,Dept. of Computer Science,Maryland,USA"}]},{"given":"Ricky","family":"Gole","sequence":"additional","affiliation":[{"name":"Morgan State University,Dept. of Computer Science,Maryland,USA"}]},{"given":"Anuva","family":"Nuzhat","sequence":"additional","affiliation":[{"name":"North Carolina State University,Dept. of Computer Science,North Carolina,USA"}]},{"given":"Holy","family":"Agyei","sequence":"additional","affiliation":[{"name":"Grambling State University,Dept. of Computer Science,Lousiana,USA"}]},{"given":"Obaloluwa","family":"Wojuade","sequence":"additional","affiliation":[{"name":"Morgan State University,Dept. of Computer Science,Maryland,USA"}]},{"given":"Mikayla","family":"Brown","sequence":"additional","affiliation":[{"name":"Morgan State University,Dept. of Computer Science,Maryland,USA"}]}],"member":"263","reference":[{"key":"ref1","first-page":"161","article-title":"Reinforcement learning with action-derived rewards for chemotherapy and clinical trial dosing regimen selection","volume-title":"Machine Learning for Healthcare Conference","author":"Yauney","year":"2018"},{"key":"ref2","first-page":"194","article-title":"Deep reinforcement learning for closed-loop blood glucose control","volume-title":"Proceedings of the Machine Learning for Healthcare Conference","author":"Fox","year":"2020"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3233\/faia251067"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0274608"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref6","volume-title":"Reinforcement learning: An introduction","author":"Sutton","year":"2018"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3477600"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13193150"},{"key":"ref9","article-title":"Efficient off-policy meta-reinforcement learning via probabilistic context variables","volume-title":"arXiv preprint","author":"Rakelly","year":"2019"},{"key":"ref10","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"International Conference on Machine Learning (ICML)","author":"Chen","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5555\/3016100.3016191"},{"key":"ref12","article-title":"Noisy networks for exploration","volume-title":"International Conference on Learning Representations (ICLR)","author":"Fortunato","year":"2018"},{"key":"ref13","article-title":"Prioritized experience replay","volume-title":"International Conference on Learning Representations (ICLR)","author":"Schaul","year":"2016"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02737-4"},{"issue":"2","key":"ref15","first-page":"1","article-title":"A personalized and adaptive insulin bolus calculator based on double deep q-learning to improve type 1 diabetes management","volume":"27","author":"Author","year":"2023","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/s21093303"},{"key":"ref17","first-page":"1","article-title":"Multi-parameter blood glucose prediction algorithm for type 1 diabetes based on hybrid neural network deep learning technique","volume":"11","author":"Author","year":"2023","journal-title":"IEEE Access"},{"key":"ref18","article-title":"Contrastive learning as goal-conditioned reinforcement learning","volume-title":"Advances in Neural Information Processing Systems (NeurIPS), 36th Conference","author":"Eysenbach","year":"2022"},{"key":"ref19","first-page":"768","article-title":"Neighborhood contrastive learning for sequential recommendation","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM). ACM","author":"Yeche","year":"2021"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3368555.3384450"}],"event":{"name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","location":"Wuhan, China","start":{"date-parts":[[2025,12,15]]},"end":{"date-parts":[[2025,12,18]]}},"container-title":["2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11355913\/11355975\/11356287.pdf?arnumber=11356287","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:51:08Z","timestamp":1770843068000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11356287\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/bibm66473.2025.11356287","relation":{},"subject":[],"published":{"date-parts":[[2025,12,15]]}}}