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001480714 019__ $$a1396140626
001480714 020__ $$a9783031369384$$q(electronic bk.)
001480714 020__ $$a3031369386$$q(electronic bk.)
001480714 020__ $$z9783031369377
001480714 020__ $$z3031369378
001480714 0247_ $$a10.1007/978-3-031-36938-4$$2doi
001480714 035__ $$aSP(OCoLC)1396186096
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001480714 050_4 $$aR859.7.A78$$bA78 2023
001480714 08204 $$a610.285/63$$223/eng/20230906
001480714 24500 $$aArtificial intelligence for personalized medicine :$$bpromoting healthy living and longevity /$$cArash Shaban-Nejad, Martin Michalowski, Simone Bianco, editors.
001480714 264_1 $$aCham :$$bSpringer,$$c2023.
001480714 300__ $$a1 online resource (xviii, 299 pages) :$$billustrations (some color).
001480714 336__ $$atext$$btxt$$2rdacontent
001480714 337__ $$acomputer$$bc$$2rdamedia
001480714 338__ $$aonline resource$$bcr$$2rdacarrier
001480714 4901_ $$aStudies in Computational Intelligence,$$x1860-9503 ;$$vvolume 1106
001480714 5050_ $$aIntro -- Preface -- Contents -- Contributors -- Abbreviations -- Artificial Intelligence for Personalized Care, Wellness, and Longevity Research -- 1 Introduction -- 2 The Role of AI and Data Science in Ageing and Longevity Research -- 3 Advances in AI Technologies and Data Analytics in Healthcare -- References -- Towards Trust of Explainable AI in Thyroid Nodule Diagnosis -- 1 Introduction -- 1.1 Dataset -- 2 Related Work -- 2.1 Backpropagation-Based Methods -- 2.2 CAM-Based Methods -- 2.3 Perturbation-Based Methods -- 2.4 Statistic-Based Methods -- 2.5 XAI in the Medical Diagnosis System
001480714 5058_ $$a3 Methodology -- 3.1 Object Detector and XAI Categorization -- 3.2 XAI Methods for the Localization Task -- 4 Results -- 4.1 Qualitative Evaluation -- 4.2 Quantitative Evaluation -- 5 Conclusion -- References -- Federated Learning over Harmonized Data Silos -- 1 Introduction -- 2 Background -- 3 Federated Learning and Integration -- 3.1 Federated Learning Programming Model -- 3.2 Data Harmonization and Imputation -- 4 Discussion -- References -- Investigation of Drift Detection for Clinical Text Classification -- 1 Introduction -- 2 Two-Sample Statistical Hypothesis Testing
001480714 5058_ $$a2.1 Kernel Maximum Mean Discrepancy -- 2.2 Kolmogorov-Smirnov Test -- 3 The Clinical Case Study -- 3.1 The Drift Monitoring Schema -- 3.2 Drift Data Setup -- 3.3 Sub-sampling -- 4 Analysis -- 4.1 Analysis with Different Drift Ratios -- 4.2 Analysis of Time Performance -- 4.3 Discussion -- 5 Conclusion and Future Work -- References -- Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy -- 1 Introduction -- 2 Problem Formulation -- 3 Proposed Approach -- 3.1 Neural Contextual Bandits -- 3.2 Generating Relevant Available Action Sets -- 3.3 OptimNeuralTS -- 4 Experiments
001480714 5058_ $$a4.1 Synthetic Data Generation -- 4.2 Experimental Setup -- 5 Results -- 5.1 Impact of Ensemble -- 5.2 Generalization -- 6 Related Work -- 7 Conclusion -- References -- Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients -- 1 Introduction and Related Work -- 2 Methods -- 3 Data -- 4 Prediction Tasks -- 5 Results -- 6 Discussion -- 7 Limitations and Future Work -- 8 Summary -- References -- Bayesian-Based Parameter Estimation to Quantify Trust in Medical Devices -- 1 Introduction -- 2 Parameter Estimation of Bayesian Network
001480714 5058_ $$a2.1 Data-Oriented Bayesian Parameter Estimation -- 2.2 Constructing the Trust Network -- 3 Experimental Evaluation -- 3.1 Parameter Estimation -- 3.2 Results and Discussion -- 4 Conclusion and Future Work -- References -- EEG Analysis of Neurodevelopmental Disorders by Integrating Wavelet Transform and Visual Analysis -- 1 Introduction -- 2 Previous Work -- 3 Methods -- 3.1 Data Description -- 3.2 Proposed Approach -- 3.3 Channel-Based Predictive Model Generation -- 3.4 Visual Analysis -- 4 Results -- 5 Conclusions and Future Works -- References
001480714 506__ $$aAccess limited to authorized users.
001480714 520__ $$aThis book aims to highlight the latest achievements in the use of AI in personalized medicine and healthcare delivery. The edited book contains selected papers presented at the 2023 Health Intelligence workshop, co-located with the Thirty-Seven Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI in medicine and public health.
001480714 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 6, 2023).
001480714 650_0 $$aArtificial intelligence$$xMedical applications.$$xMedical applications$$0(DLC)sh 88003000
001480714 650_0 $$aPrecision medicine.
001480714 655_0 $$aElectronic books.
001480714 7001_ $$aShaban-Nejad, Arash,$$eeditor.
001480714 7001_ $$aMichalowski, Martin,$$eeditor.$$1https://orcid.org/0000-0003-2060-5878
001480714 7001_ $$aBianco, Simone,$$eeditor.
001480714 77608 $$iPrint version: $$z3031369378$$z9783031369377$$w(OCoLC)1381545103
001480714 830_0 $$aStudies in computational intelligence ;$$vv. 1106.$$x1860-9503
001480714 852__ $$bebk
001480714 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-36938-4$$zOnline Access$$91397441.1
001480714 909CO $$ooai:library.usi.edu:1480714$$pGLOBAL_SET
001480714 980__ $$aBIB
001480714 980__ $$aEBOOK
001480714 982__ $$aEbook
001480714 983__ $$aOnline
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