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2. Using heterogeneous, longitudinal EHR data for risk assessment and early detection of cardiovascular disease
3. Causal machine learning for reliable real-world evidence generation in healthcare
4. Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias
5. Leveraging Electronic Health Record Event Logs to Measure Clinician Documentation Burden in the Emergency Department
6. Phenotyping with Partially Labeled, Partially Observed Data
7. Common and rare genetic effects on the transcriptome and their contribution to human traits
8. Inferring Race and Ethnicity from Clinical Notes: Annotation, Model Auditing, and Ethical Implications
9. Using Real-World Data to Enhance Clinical Trials
10. Computational Framework for the Identification of Neural Circuits Underlying Psychiatric Disorders
11. Data Quality Assessment for the Secondary Use of Person-Generated Wearable Device Data: Assessing Self-Tracking Data for Research Purposes
12. Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence
13. Mechanisms Underlying Phenotypic Heterogeneity in Simplex Autism Spectrum Disorders
14. Post-transcriptional gene expression regulation in developmental disorders
15. The Transparent, Concurrent, and Collaborative Health Record: Methods to Improve Patients' Comprehension of Health Information
16. Towards unified medical evidence computation from literature for evidence-based medicine
17. An Evaluation of Computational Methods to Support the Clinical Management of Chronic Disease Populations
18. Leveraging Social Media for Nutrition Interventions with Young Populations
19. Machine Learning Methods for Causal Inference with Observational Biomedical Data
20. Mosaicism and the genetic architecture of congenital heart disease
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