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Co-Pilot for Health: Personalized Algorithmic AI Nudging to Improve Health Outcomes
The ability to shape health behaviors of large populations automatically, across wearable types and disease conditions at scale has tremendous potential to improve global health outcomes.
NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients
Recently, deep learning methodologies have been proposed to predict sepsis, but some fail to capture the time of onset (e. g., classifying patients' entire visits as developing sepsis or not) and others are unrealistic for deployment in clinical settings (e. g., creating training instances using a fixed time to onset, where the time of onset needs to be known apriori).
Multi-Subset Approach to Early Sepsis Prediction
However, clinical scores like Sequential Organ Failure Assessment (SOFA) are not applicable for early prediction, while machine learning algorithms can help capture the progressing pattern for early prediction.
Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management
Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature.
Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare
Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems.
Emergency Department Optimization and Load Prediction in Hospitals
Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs).
Survey of explainable machine learning with visual and granular methods beyond quasi-explanations
Next, we present methods of visual discovery of ML models, with the focus on interpretable models, based on the recently introduced concept of General Line Coordinates (GLC).
The Challenge of Imputation in Explainable Artificial Intelligence Models
In this paper, we explore different settings where AI models with imputation can be problematic and describe ways to address such scenarios.
Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach
We first split the problem into various stages, (a) at risk in general (b) risk within 60 days (c) risk within 30 days, and then build suitable classifiers for each stage, thereby increasing the ability to accurately predict the risk using multiple layers of decision.
