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Found 12 papers, 0 papers with code
Automated Compliance Blueprint Optimization with Artificial Intelligence
The critical problem is to establish the mapping between techspecs and regulation controls so that from day one, companies can comply with regulations with minimal effort.
Vulnerability Prioritization: An Offensive Security Approach
Organizations struggle to handle sheer number of vulnerabilities in their cloud environments.
Attack Techniques and Threat Identification for Vulnerabilities
Modern organizations struggle with insurmountable number of vulnerabilities that are discovered and reported by their network and application vulnerability scanners.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
Question-Driven Design Process for Explainable AI User Experiences
A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI.
Phenotypical Ontology Driven Framework for Multi-Task Learning
Despite the large number of patients in Electronic Health Records (EHRs), the subset of usable data for modeling outcomes of specific phenotypes are often imbalanced and of modest size.
A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes.
ODVICE: An Ontology-Driven Visual Analytic Tool for Interactive Cohort Extraction
Increased availability of electronic health records (EHR) has enabled researchers to study various medical questions.
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients
The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari.
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes
Counterfactual prediction is a fundamental task in decision-making.
Explicit-Blurred Memory Network for Analyzing Patient Electronic Health Records
While networks with explicit memory have been proposed recently, the discontinuities imposed by the discrete operations make such networks harder to train and require more supervision.
ATTENTIVE EXPLAINABILITY FOR PATIENT TEMPORAL EMBEDDING
Learning explainable patient temporal embeddings from observational data has mostly ignored the use of RNN architecture that excel in capturing temporal data dependencies but at the expense of explainability.
