Explainable AI in Healthcare and Medicine : Building a Culture of Transparency and Accountability /
This 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 Conf...
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| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2021.
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| Edition: | 1st ed. 2021. |
| Series: | Studies in Computational Intelligence,
914 |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- 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.