LEADER 04026nam 22005895 450 001 9910484170603321 005 20251113210931.0 010 $a3-030-53352-2 024 7 $a10.1007/978-3-030-53352-6 035 $a(CKB)4100000011558644 035 $a(MiAaPQ)EBC6383559 035 $a(DE-He213)978-3-030-53352-6 035 $a(PPN)252507312 035 $a(EXLCZ)994100000011558644 100 $a20201102d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aExplainable AI in Healthcare and Medicine $eBuilding a Culture of Transparency and Accountability /$fedited by Arash Shaban-Nejad, Martin Michalowski, David L. Buckeridge 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XXII, 344 p. 110 illus., 84 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v914 311 08$a3-030-53351-4 327 $aExplainability and Interpretability: Keys to Deep Medicine -- Fast Similar Patient Retrieval from Large Scale Healthcare Data: A Deep Learning-based Binary Hashing Approach -- A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs -- Machine learning discrimination of Parkinson's Disease stages from walk-er-mounted sensors data -- Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Rein-forcement Learning -- A Generalizable Method for Automated Quality Control of Functional Neuroimaging Datasets -- Uncertainty Characterization for Predictive Analytics with Clinical Time Series Data -- A Dynamic Deep Neural Network for Multimodal Clinical Data Analysis -- DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data -- A Deep Learning Approach for Classifying Nonalcoholic Steatohepatitis Pa-tients from Nonalcoholic Fatty Liver Disease Patients using Electronic Medical Records. 330 $aThis book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v914 606 $aComputational intelligence 606 $aBiomedical engineering 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aBiomedical Engineering and Bioengineering 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aBiomedical engineering. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aBiomedical Engineering and Bioengineering. 615 24$aArtificial Intelligence. 676 $a610.28563 702 $aShaban-Nejad$b Arash 702 $aBuckeridge$b David Llewellyn$f1970- 702 $aMichalowski$b Martin 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484170603321 996 $aExplainable AI in healthcare and medicine$92851553 997 $aUNINA