{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:54:28Z","timestamp":1771026868243,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Chronic disease management involves frequent administration of invasive lab procedures in order for\n\nclinicians to determine the best course of treatment\n\nregimes for these patients. However, patients are\n\noften put off by these invasive lab procedures and\n\ndo not follow the appointment schedules. This has\n\nresulted in poor management of their chronic conditions leading to unnecessary disease complications. An AI system that is able to personalize the\n\nprediction of individual patient lab test responses\n\nwill enable clinicians to titrate the medications to\n\nachieve the desired therapeutic outcome.  Accurate prediction of lab test response is a challenge because these patients typically have co-morbidities and their treatments might influence the target lab test response. To address this,\n\nwe model the complex interactions among different medications, diseases, lab test response, and\n\nfine-grained dosage information to learn a strong\n\npatient representation. Together with information\n\nfrom similar patients and external knowledge such\n\nas drug-lab interactions and diagnosis-lab interaction, we design a system called KALP to perform\n\npersonalized prediction of patients\u2019 response for a\n\ntarget lab result and identify the top influencing\n\nfactors for the prediction. Experiment results on\n\nreal-world datasets demonstrate the effectiveness of\n\nKALP in reducing prediction errors by a significant\n\nmargin. Case studies show that the identified factors are consistent with clinicians\u2019 understanding.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/699","type":"proceedings-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T22:55:56Z","timestamp":1657925756000},"page":"5038-5044","source":"Crossref","is-referenced-by-count":1,"title":["Chronic Disease Management with Personalized Lab Test Response Prediction"],"prefix":"10.24963","author":[{"given":"Suman","family":"Bhoi","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mong Li","family":"Lee","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wynne","family":"Hsu","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao Sen Andrew","family":"Fang","sequence":"additional","affiliation":[{"name":"SingHealth Polyclinics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ngiap","family":"Chuan Tan","sequence":"additional","affiliation":[{"name":"SingHealth Polyclinics"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T07:11:09Z","timestamp":1658128269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/699"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/699","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}