{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T06:24:20Z","timestamp":1787293460475,"version":"build-2736575974"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Despite the advent of targeted therapies, cisplatin remains a cornerstone in the management of non-small cell lung cancer Its systemic toxicity, however, often triggers neutropenic sepsis, a rapidly fatal oncological emergency. Traditional intermittent monitoring is inadequate here. Chemotherapy-induced \u201cinflammatory silence\u201d frequently masks the classical febrile response, leaving patients unprotected during outpatient windows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this Perspective, we mined global real-world pharmacovigilance data from the FAERS and Japanese Adverse Drug Event Report databases. We applied four disproportionality-analysis algorithms and Weibull time-to-onset modeling to reconstruct the systemic toxicity spectrum and temporal risk trajectory of cisplatin. We then synthesize the conceptual and technical basis for continuous multidimensional physiological monitoring (cVSM) coupled with explainable artificial intelligence (XAI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We reveal a distinct \u201cearly failure\u201d mode (Weibull shape parameter \u03b2 = 0.81) for severe cisplatin-related adverse events, clustered within the first 30\u00a0days after infusion. We also show that threshold-based alerts tend to induce \u201calarm fatigue\u201d, because the physiological baselines of cancer patients shift dynamically, as in sarcopenia-driven occult overexposure in older patients. To address this, we propose an XAI-driven predictive framework integrated with non-invasive point-of-care testing The framework decodes subtle autonomic changes, such as reduced heart rate variability, hours before overt septic shock.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>cVSM coupled with XAI can shift the management of chemotherapy-induced toxicity from reactive rescue to pre-emptive therapeutics. Realizing this digital safety net will require revised oncology monitoring guidelines, restructured reimbursement models, and the deployment of decentralized clinical trials.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2026.1861637","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T05:37:31Z","timestamp":1787290651000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Continuous multidimensional physiological monitoring redefines early warning and preemptive treatment for neutropenic sepsis following cisplatin chemotherapy in non-small cell lung cancer"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhe","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Thoracic Oncology Surgery II, Jilin Cancer Hospital","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunpeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Oncology Surgery II, Jilin Cancer Hospital","place":["Changchun, China"]},{"name":"School of Clinical Medicine, Changchun University of Chinese Medicine","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Oncology Surgery II, Jilin Cancer Hospital","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Thoracic Oncology Surgery II, Jilin Cancer Hospital","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiyun","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Thoracic Oncology Surgery II, Jilin Cancer Hospital","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglun","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Thoracic Oncology Surgery II, Jilin Cancer Hospital","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Clinical Medicine, Changchun University of Chinese Medicine","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Oncology Surgery II, Jilin Cancer Hospital","place":["Changchun, China"]},{"name":"School of Clinical Medicine, Changchun University of Chinese Medicine","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.3390\/healthcare13172154","article-title":"Explainable AI in clinical decision support systems: a meta-analysis of methods, applications, and usability challenges","volume":"13","author":"Abbas","year":"2025","journal-title":"Healthc. 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