{"product_id":"9783032291387","title":"Multimodal Temporal Modeling for Clinical AI Data, Method, and Deployment","description":"\u003ch1\u003eMultimodal Temporal Modeling for Clinical AI\u003c\/h1\u003e\u003ch2\u003eData, Method, and Deployment\u003c\/h2\u003e\u003ch3\u003eJinjin Cai | Ruiqi Wang | Lingzhong Meng | Jing Su | Baijian Yang\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Artificial Intelligence \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\u003cp class=\"MsoBodyText\" style=\"margin: 0in 18.85pt .0001pt 0in;\"\u003eThis\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003ebook\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003ediscusses how\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003emultimodal\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003etemporal\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003eAI\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003eis\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003etransforming\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003ehealthcare by combining\u003cspan style=\"letter-spacing: -.05pt;\"\u003e \u003c\/span\u003ediverse medical data and health records over time. With clear explanations,\u003cspan style=\"letter-spacing: -.1pt;\"\u003e \u003c\/span\u003ecutting-edge methods, and real-world case studies, the book provides researchers, clinicians, and innovators the tools they need to turn AI breakthroughs into smarter and personalized care and treatment. Unlike\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003eexisting\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003eliterature\u003cspan style=\"letter-spacing: -.25pt;\"\u003e \u003c\/span\u003ethat\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003efocuses\u003cspan style=\"letter-spacing: -.25pt;\"\u003e \u003c\/span\u003enarrowly\u003cspan style=\"letter-spacing: -.25pt;\"\u003e \u003c\/span\u003eon\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003especific\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003etechniques\u003cspan style=\"letter-spacing: -.25pt;\"\u003e \u003c\/span\u003eor\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003eapplications,\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003ethis\u003cspan style=\"letter-spacing: -.25pt;\"\u003e \u003c\/span\u003ebook\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003eprovides\u003cspan style=\"letter-spacing: -.25pt;\"\u003e \u003c\/span\u003ea comprehensive, big-picture perspective on multimodal temporal modeling in clinical AI. The authors not only explain technical methods, but also explore the core principles, challenges, and future directions that shape the field. Readers will find practical guidance for deploying these models in real healthcare settings, along with actionable strategies that can be applied immediately. Covering the full spectrum of topics, from data to methods to deployment, the book offers a complete roadmap\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003erather\u003cspan style=\"letter-spacing: -.1pt;\"\u003e \u003c\/span\u003ethan\u003cspan style=\"letter-spacing: -.1pt;\"\u003e \u003c\/span\u003efragmented\u003cspan style=\"letter-spacing: -.1pt;\"\u003e \u003c\/span\u003einsights.\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003eAs\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003ea\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003etimely\u003cspan style=\"letter-spacing: -.1pt;\"\u003e \u003c\/span\u003eand\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003eup-to-date\u003cspan style=\"letter-spacing: -.25pt;\"\u003e \u003c\/span\u003eresource,\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003ethe book\u003cspan style=\"letter-spacing: -.2pt;\"\u003e \u003c\/span\u003ecaptures\u003cspan style=\"letter-spacing: -.3pt;\"\u003e \u003c\/span\u003ethe\u003cspan style=\"letter-spacing: -.15pt;\"\u003e \u003c\/span\u003emomentum\u003cspan style=\"letter-spacing: -.1pt;\"\u003e \u003c\/span\u003eof a rapidly evolving field and provides readers a forward-looking guide to the future of AI in healthcare.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003eJinjin Cai is a Ph.D. candidate in the School of Applied and Creative Computing at Purdue University. Her research focuses on developing data-driven, human-centric AI methodologies for healthcare, with particular emphasis on representation learning and disease trajectory modeling to elucidate complex pathological processes. By integrating and extracting meaningful features from diverse multimodal medical data, she aims to enable earlier disease detection, enhance patient monitoring, and tailor interventions to individual needs. Her broader objective is to advance efficient, interpretable, and practical AI solutions that can inform clinical decision-making, streamline healthcare delivery, and improve patient outcomes.\u003c\/p\u003e\r\n\u003cp\u003eRuiqi Wang is a Ph.D. candidate in the School of Applied and Creative Computing at Purdue University. His research interests include multimodal perception and reasoning, human-centered adaptive systems, and human-in-the-loop robot learning. His work has been disseminated through leading journals and conferences in AI and robotics. His long-term goal is to enable the seamless integration of human- centered AI systems into everyday human life.\u003c\/p\u003e\r\n\u003cp\u003eLingzhong “LZ” Meng, M.D., is Professor of Clinical Anesthesia and Vice Chair for Clinical and Outcomes Research at Indiana University School of Medicine, with more than 20 years of experience across leading academic medical centers including Yale, UCSF, Duke, and Mayo Clinic. He is internationally recognized for pioneering research in perioperative cerebral blood flow, tissue perfusion monitoring, and integrating artificial intelligence into clinical decision-making—developing AI-enabled frameworks like HM- TARGET and DynaCEL for personalized hemodynamic management in critical care. An accomplished scholar with over 150 peer-reviewed publications, he has authored groundbreaking work in journals such as \u003cem\u003eBritish Medical Journal, Nature Communications, npj Digital Medicine, Anesthesiology\u003c\/em\u003e, and \u003cem\u003eBritish Journal of Anaesthesia\u003c\/em\u003e. As a committed educator and mentor, Dr. Meng founded the Global Perioperative Case Discussion Forum and has trained over 100 students, residents, and junior faculty worldwide.\u003c\/p\u003e\r\n\u003cp\u003eJing Su, Ph.D, is an Associate Professor in the Department of Biostatistics and Health Data Science at Indiana University School of Medicine. Dr. Su earned his Ph.D. in Biomedical Engineering from the Georgia Institute of Technology and Emory University. His work focuses on biomedical informatics, graph-based machine learning, and data management and the integration of longitudinal complex real- world clinical data. His research advances precision medicine through the development of temporal and multimodal analytical frameworks such as digital twin models and the implementation of graph models in real- world clinical care.\u003c\/p\u003e\r\n\u003cp\u003eBaijian Yang, Ph.D., is the Associate Dean for Research at Purdue Polytechnic Institute and a Professor in the School of Applied and Creative Computing at Purdue University. With a Ph.D. in Computer Science from Michigan State University and degrees in Automation from Tsinghua University, he has authored over 100 peer-reviewed publications and two books on Smartphone programming. His research spans applied machine learning, big data analytics, cybersecurity, and notably, healthcare AI, where his work on multimodal, time-aware modeling (e.g., spatial transcriptomics methods like SpaRx) exemplifies the cutting edge of clinical AI. Dr. Yang also brings industry edge with CISSP, MCSE, and Six Sigma Black Belt certifications, bridging rigorous engineering with health-focused innovation.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e22 July 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032291387\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e305\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":50185178742924,"sku":"9783032291387","price":80.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032291387.jpg?v=1780619666","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032291387","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}