Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability (Studies in Computational Intelligence (914))

ISBN 10: 3030533514 / ISBN 13: 9783030533519

Edité par Springer, 2020

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N° de réf. du vendeur 382633028

Détails bibliographiques

• Titre: Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability (Studies in Computational Intelligence (914))
• Éditeur: Springer
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 3030533514
• ISBN à 13 chiffres: 9783030533519
• Reliure: Couverture rigide
• État de l'article: New

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Synopsis

Explainability 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.