Titlebook: Explainable AI in Healthcare and Medicine; Building a Culture o Arash Shaban-Nejad,Martin Michalowski,David L. Buc Book 2021 The Editor(s)

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Fast Similar Patient Retrieval from Large Scale Healthcare Data: A Deep Learning-Based Binary Hashithe data size increased. The retrieval efficiency increased as the number of bits in binary hash codes increased. Descriptive analysis revealed distinct profiles between similar patients and the overall patient cohort.

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Uncertainty Characterization for Predictive Analytics with Clinical Time Series Data, for improved performance over SOTA methods, and characterize aleatoric uncertainty in the setting of noisy features. Importantly, we demonstrate how our uncertainty estimates could be used in realistic prediction scenarios to better interpret the reliability of the data and the model predictions, and improve relevance for decision support.

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Visualization of Deep Models on Nursing Notes and Physiological Data for Predicting Health Outcomesns results and it is capable of sending warnings for crashing patients in a more timely manner. Also, to illustrate the different focal points of the models, we identified the top contributing factors each deep model utilizes to make predictions.

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Character-Level Japanese Text Generation with Attention Mechanism for Chest Radiography Diagnosis,-level from chest radiographs. We evaluated the method using a public dataset of Japanese chest radiograph findings. Furthermore, we confirmed via visual inspection that the attention mechanism captures the features and positional information of radiographs.

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1860-949X and applications.Includes revised versions of selected papeThis 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 t

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Renée K. Margolis,Richard U. Margolishealth and medicine depends on the degree to which they support interoperability, to allow consistent integration of different systems and data sources, and explainability, to make their decisions understandable, interpretable, and justifiable by humans.

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,Quantitative Evaluation of Emergency Medicine Resident’s Non-technical Skills Based on Trajectory alts show that the method can create a workflow event database for cardiac arrest. In addition, we evaluated EMRs who are beginners, intermediates, and experts to show that our method can correctly represent the differences in their skill levels.

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Book 2021d 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 ra