{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:03:25Z","timestamp":1789949005933,"version":"4.0.1"},"reference-count":55,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:00:00Z","timestamp":1787961600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:00:00Z","timestamp":1787961600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Traditional drug\u2010induced liver injury (DILI) surveillance relying on static laboratory thresholds frequently misses early kinetic evolution. To address this, a fundamentally drug\u2010agnostic unsupervised ensemble framework, integrating pharmacokinetic (WCEDR) and polypharmacy (HAMPIS) features, was developed for early DILI detection. Antiepileptic drugs (AEDs) were utilised as a proof\u2010of\u2010concept validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using Thailand's national electronic health record database (2021\u20132024), a multi\u2010view ensemble (Isolation Forest, Local Outlier Factor, One\u2010Class Support Vector Machine) evaluated longitudinal physiological deviations. Feature engineering prioritised acute kinetic volatility to mitigate irregular sampling intervals. Diagnostic performance was benchmarked against standard criteria (ALT\u2009&gt;\u20093\u2009\u00d7\u2009ULN) and validated through blinded expert adjudication of 140 clinical episodes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From 616\u2009425 treatment episodes, the framework isolated 86 high\u2010probability anomalies. Benchmarking revealed the model captured 80.2% of rule\u2010based positives while proactively identifying 17 AI\u2010only sub\u2010threshold episodes (19.8%) exhibiting distinct enzymatic velocity; over half (52.9%) were clinically confirmed as DILI. Blinded adjudication yielded a clinical plausibility rate of 69.8% and a negative predictive value of 92.6%. Operationally, the framework achieved a number needed to review (NNR) of 1.43, substantially optimising triage efficiency. Digital phenotyping stratified alerts into three distinct archetypes: complex polypharmacy, atypical pharmacokinetic burden, and active idiosyncratic injury.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By prioritising kinetic volatility and multidimensional pharmacological context, the unsupervised ensemble functions as a high\u2010precision, complementary surveillance tool. It successfully detects early\u2010onset idiosyncratic reactions frequently overlooked by rigid static thresholds, offering a scalable blueprint for proactive pharmacovigilance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pds.70471","type":"journal-article","created":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T08:00:36Z","timestamp":1787990436000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Ensemble Learning for Active Drug\u2010Induced Liver Injury Surveillance: Integrating Pharmacokinetic Burden With Enzyme Trajectories"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3518-4530","authenticated-orcid":false,"given":"Thawatchai","family":"Nakkaratniyom","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Care, Faculty of Pharmacy Thammasat University  Pathum Thani Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanita","family":"Hirunrassamee","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Care, Faculty of Pharmacy Thammasat University  Pathum Thani Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thapana","family":"Boonchoo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Technology Thammasat University  Pathum Thani Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cha\u2010oncin","family":"Sooksriwong","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Care, Faculty of Pharmacy Thammasat University  Pathum Thani Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,29]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2019.02.014"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2013.02.006"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.2165\/00002018\u2010200629050\u201000003"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ailsci.2021.100005"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40264\u2010022\u201001176\u20101"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3_1"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2500853.2500858"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2932769"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.1211"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pediatrneurol.2017.09.013"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600\u20100404.2008.01009.x"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1528\u20101167.2008.01561.x"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1533\u20102500.2011.00479.x"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2014.00478"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/clpt.2011.58"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcla.24445"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618\u2010007\u20100077\u20107"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3701"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12877\u2010017\u20100621\u20102"},{"key":"e_1_2_13_22_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280220902179"},{"key":"e_1_2_13_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335388"},{"key":"e_1_2_13_25_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"e_1_2_13_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2594473.2594476"},{"key":"e_1_2_13_27_1","first-page":"4768","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Lundberg S. 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