PMID- 30473255 OWN - NLM STAT- MEDLINE DCOM- 20200427 LR - 20200427 IS - 1873-2860 (Electronic) IS - 0933-3657 (Linking) VI - 96 DP - 2019 May TI - Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning. PG - 167-186 LID - S0933-3657(17)30521-3 [pii] LID - 10.1016/j.artmed.2018.11.003 [doi] AB - The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized conciliation of multiple guidelines considering additionally patient preferences brings some further difficulties. Recently, several works have explored distinct techniques to come up with an automated process for the conciliation of clinical guidelines for comorbid patients but very little attention has been put in integrating the patient preferences into this process. In this work, a Multi-Agent Planning (MAP) framework that extends previous work on single-disease temporal Hierarchical Task Networks (HTN) is proposed for the automated conciliation of clinical guidelines with patient-centered preferences. Each agent encapsulates a single-disease Computer Interpretable Guideline (CIG) formalized as an HTN domain and conciliates the decision procedures that encode the clinical recommendations of its CIG with the decision procedures of the other agents' CIGs. During conciliation, drug-related interactions, scheduling constraints as well as redundant actions and multiple support interactions are solved by an automated planning process. Moreover, the simultaneous application of the patient preferences in multiple diseases may potentially bring about contradictory clinical decisions and more interactions. As a final step, the most adequate personalized treatment plan according to the patient preferences is selected by a Multi-Criteria Decision Making (MCDM) process. The MAP approach is tested on a case study that builds upon a simplified representation of two real clinical guidelines for Diabetes Mellitus and Arterial Hypertension. CI - Copyright (c) 2018 Elsevier B.V. All rights reserved. FAU - Fdez-Olivares, Juan AU - Fdez-Olivares J AD - Department of Computer Science and AI, Universidad de Granada, Spain. Electronic address: faro@decsai.ugr.es. FAU - Onaindia, Eva AU - Onaindia E AD - Department of Computer Science, Universitat Politecnica de Valencia, Spain. Electronic address: onaindia@dsic.upv.es. FAU - Castillo, Luis AU - Castillo L AD - Department of Computer Science and AI, Universidad de Granada, Spain. Electronic address: L.Castillo@decsai.ugr.es. FAU - Jordan, Jaume AU - Jordan J AD - Department of Computer Science, Universitat Politecnica de Valencia, Spain. Electronic address: jjordan@dsic.upv.es. FAU - Cozar, Juan AU - Cozar J AD - Hospital San Agustin, Linares, Spain. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20181123 PL - Netherlands TA - Artif Intell Med JT - Artificial intelligence in medicine JID - 8915031 SB - IM MH - *Artificial Intelligence MH - *Comorbidity MH - *Decision Making, Computer-Assisted MH - Decision Support Systems, Clinical/*organization & administration/standards MH - Humans MH - Patient Care Planning/organization & administration MH - Patient Participation MH - Patient Preference MH - Practice Guidelines as Topic OTO - NOTNLM OT - Clinical guidelines OT - Comorbidities OT - Conciliation OT - HTN planning OT - Multi-agent planning OT - Patient preferences EDAT- 2018/11/27 06:00 MHDA- 2020/04/28 06:00 CRDT- 2018/11/27 06:00 PHST- 2017/10/10 00:00 [received] PHST- 2018/09/24 00:00 [revised] PHST- 2018/11/06 00:00 [accepted] PHST- 2018/11/27 06:00 [pubmed] PHST- 2020/04/28 06:00 [medline] PHST- 2018/11/27 06:00 [entrez] AID - S0933-3657(17)30521-3 [pii] AID - 10.1016/j.artmed.2018.11.003 [doi] PST - ppublish SO - Artif Intell Med. 2019 May;96:167-186. doi: 10.1016/j.artmed.2018.11.003. Epub 2018 Nov 23.