{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T04:51:41Z","timestamp":1791435101381,"version":"4.3.3"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82330108"],"award-info":[{"award-number":["82330108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Province Key Research and Development Program","award":["2021SFGC0504"],"award-info":[{"award-number":["2021SFGC0504"]}]},{"name":"Shandong Province Natural Science Foundation Youth Branch","award":["ZR2024QH532"],"award-info":[{"award-number":["ZR2024QH532"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Antipsychotic treatment response in schizophrenia shows substantial interindividual variability. While pharmacogenomics (PGx) holds potential for personalized prescribing, current models fall short in integrating multimodal factors and meeting real-time clinical decision-making needs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Hypothesis<\/jats:title>\n                    <jats:p>The precision medicine platform integrating multi-dimensional factors can provide guidance for selecting antipsychotic medications for patients with schizophrenia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>This study employed a retrospective cohort design for model development (2019-2021, N\u2009=\u2009735) and external validation (2021-2022, N\u2009=\u200990). We integrated PGx profiles, clinical characteristics, and medication data, selected features via random forest recursive feature elimination (RF-RFE), and built a Super Learner ensemble model (incorporating 5 machine learning algorithms) to predict Positive and Negative Syndrome Scale (PANSS) reduction. For 13 prioritized single-nucleotide polymorphisms (SNPs), we developed a kompetitive allele-specific PCR-based microfluidic chip and validated its accuracy against Sanger sequencing in 24 clinical samples. A clinical decision support (CDS) tool integrating genotyping and predictive analytics was deployed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Results<\/jats:title>\n                    <jats:p>The Super Learner model achieved a cross-validated RMSE of 7.08 (R2\u2009=\u20090.89) and an external validation RMSE of 9.02 (R2\u2009=\u20090.76). The microfluidic chip showed 100% concordance with Sanger sequencing across all 13 SNPs. The integrated CDS system demonstrated sample-to-report feasibility within 3\u00a0h.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Microfluidic genotyping and Super Learner platform enables rapid, real-time prediction of antipsychotic treatment response in schizophrenia, providing a scalable tool for early personalized treatment selection during hospitalization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/schbul\/sbag044","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T12:32:32Z","timestamp":1772973152000},"source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Precision Psychiatry for Schizophrenia: A Microfluidic Genotyping and Super-Learner Platform for Treatment Response 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Laboratory of Shandong Mental Health Center, Shandong University , Jinan 250012 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University , Jinan 250012 ,","place":["China"]},{"name":"Healthcare Big Data Research Institute, Cheeloo College of Medicine, Shandong University , Jinan 250012 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Hong","family":"Feng","sequence":"additional","affiliation":[{"name":"Medical Services Section, Pingyi County Psychiatric Hospital , Linyi 276000 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Qu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University , Jinan 250012 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