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  • Publications
    • Explanation Ontology: A General-Purpose, Semantic Representation for Supporting User-Centered Explanations
    • Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles
    • Refining the Definition of Stage 1 Type 1 Diabetes: An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity
    • Informing Clinical Assessment by Contextualizing Post-Hoc Explanations of Risk Prediction Models in Type-2 Diabetes
    • Islet Autoantibody Screening In At-Risk Adolescents To Predict Type 1 Diabetes Until Young Adulthood: A Prospective Cohort Study
    • Interpretable Stratification for Chronic Kidney Disease Progression Based on Time to Event Analysis
    • Data-Driven Disease Progression Modeling
    • Two-Age Islet-Autoantibody Screening for Childhood Type 1 Diabetes: A Prospective Cohort Study
    • Improving the Computational Complexity of the COOL Screening Tool
    • Predicting Pain Now and in the Future Through Personalized Physiologic Mobility Metrics
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    • Finding Precise Causal Multi-Drug-Drug Interaction for Adverse Drug Reactions
    • Carbon Tuner
    • G-Net: a Recurrent Network Approach to G-Computation for Counterfactual Prediction Under a Dynamic Treatment Regime
    • A Comparative Time-to-Event Analysis Across Health Systems
    • Impact of Clinical and Genomic Factors on COVID-19 Disease Severity
    • Disease Progression Modeling Workbench 360
    • Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
    • Transparent and Distributed AI Prediction Modeling: A Case Study on Pediatric Covid
    • A Method For Developing An Ontology Based On Biomarker Information To Improve Clinical Prediction
    • Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes: Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S.
    • Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
    • Phenotypical Ontology Driven Framework For Multi-Task Learning
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    • Finding Causal Mechanistic Drug-Drug Interactions from Observational Data
    • Predicting Type 1 Diabetes Onset using Novel Survival Analysis with Biomarker Ontology
    • A System for Generating Specialized Phenotypical Embedding
    • ODVICE: An Ontology-Driven Visual Analytic Tool for Interactive Cohort Extraction
    • A Multi-Task Learning Approach to Personalized Progression Modeling
    • Leveraging Longitudinal Autoantibody for Phenotyping of Progression Rates to Type 1 Diabetes
    • COOL - A Screening Collaborative Open Outcomes Tool
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    • Analysis of Longitudinal Autoantibody Profiles and the Progression Rates to Type 1 Diabetes
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    • Presymptomatic and Clinical T1D in The US, Sweden, and Finland: Joint Analysis of Four Birth Cohort Studies
    • Sensitivity Analysis of Alternate Definitions of Multiple Islet Autoantibody Positivity
    • Temporal Graph Regression via Structure-Aware Intrinsic Representation Learning
    • Attentive Explainability For Patient Temporal Embedding
    • Estimating Causal Multi-Drug-Drug Interaction for Adverse Drug Reactions
    • Ranking Based Multitask Learning of Scoring Functions
    • Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results
    • Cost Sensitive Time-series Classification
    • Continuous Conditional Dependency Network for Structured Regression
    • Minimum Redundancy Maximum Relevance Feature Selection Approach For Temporal Gene Expression Data
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    • Effectiveness of Multiple Blood-Cleansing Interventions in Sepsis, Characterized in Rats
    • Structured Feature Selection Using Coordinate Descent Optimization
    • Extending the Modelling Capacity of Gaussian Conditional Random Fields while Learning Faster
    • False Alarm Suppression in Early Prediction of Cardiac Arrhythmia
    • Patient--Specific Early Classification of Multivariate Observations
    • Utilizing Temporal Patterns For Estimating Uncertainty In Interpretable Early Decision Making
    • A Data-Driven Model for Optimizing Therapy Duration for Septic Patients
    • Extraction Of Interpretable Multivariate Patterns For Early Diagnostics
    • Early Diagnosis And Its Benefits In Sepsis Blood Purification Treatment
    • D2P2: Database Of Disordered Protein Predictions
    • Interpretable Early Classification Of Multivariate Time Series
    • Early Classification of Multivariate Time Series Using A Hybrid HMM/SVM Model
    • Early Classification of Multivariate Temporal Observations By Extraction of Interpretable Shapelets
    • DNA Methylation Differences at Growth Related Genes Correlate With Birth Weight: A Molecular Signature Linked To Developmental Origins of Adult Disease?
    • Uncertainty Analysis in Protein Disorder Prediction
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Estimating Causal Multi-Drug-Drug Interaction for Adverse Drug Reactions

Nov 7, 2018·
Sanjoy Dey
,
Ping Zhang
Mohamed Ghalwash
Mohamed Ghalwash
,
Zach Shahn
,
Daby Sow
· 0 min read
Type
Conference paper
Publication
AMIA Annual Symposium
publications
Last updated on Nov 7, 2018
Causal Drug
← Attentive Explainability For Patient Temporal Embedding Jan 1, 2019
Ranking Based Multitask Learning of Scoring Functions Dec 30, 2017 →

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