The following are external grants and projects I’ve been awarded as PI or Co-PI
Advancing Identification of Late-Talking Children and Mapping their Developmental Trajectories Using Real World Data from Electronic Health Records – R21 NIDCD (9/24 – 8/26)
- Using Natural Language Processing (NLP) to detect under diagnosis of late talking
- Mapping developmental of late talking children
- Collaborators: Lauren Franz (co-PI), Danai Fanin (co-I)
Leveraging routinely collected health data to improve early identification of autism and co-occurring conditions – P50 NICHD (9/22 -8/27)
- Part of Duke’s Autism Center of Excellence (ACE)
- Using EHR and claims data to create automated screening tool for kids at risk of ASD
- Developing machine learning approaches to model rare outcome
- Collaborators: Geraldine Dawson (Duke – Psychiatry, ACE PI), Gary Maslow (Duke – Psychiatry, Project co-lead)
Personalizing Dialysis Treatment Based on Life Expectancy – R01 NIDDK (7/20 -8/24)
- Leveraging EHR data on dialysis patients
- Using deep learning to predict life expectancy and identify patient clinical trajectories
- Administrative supplement on ethics of ML based tools
- Collaborators: Julia Sciala (U Virginia – Nephrology), Ricardo Henao (Duke – B&B), Tariq Shafi (U Mississippi – Nephrology)
Engaging Multidisciplinary Health System Stakeholders to Create a Process for Implementing Machine-Learning Enabled Clinical Decision Support – R21 NLM (7/22 – 6/24)
- Conduct Stakeholder focus groups to understand the process for how Duke’s early warning score was implemented
- Create a guidance for implemented ML-based CDS tools
- Collaborators: Nina Sperber (Duke – Population Health, coPI)
Using simulation modeling and real-world data to monitor the effectiveness of the COVID-19 vaccine – FDA BAA (9/21 – 3/23)
- Develop analytic approaches for assessing COVID vaccine safety and effectiveness with EHR data
- Compare what is observed directly from health systems versus what is imported in from state data
- Collaborators: Jillian Hurst (Duke – Pediatric ID), Deverick Anderson (Duke – Adult ID), Emily O’Brien (Duke – Population Health)
Using Electronic Health Records to Identify and Assess Adverse Events for Biologic Therapies: Developing and Validating Methodological Approaches – FDA BAA (9/20 – 3/22)
- Exploring the use of EHR to detect and adverse events and associate them with therapies
- Providing advice and guidance to the FDA biostatistics group tasked with RWD
- Collaborators: Jillian Hurst (Duke – Pediatric ID), JJ Strouse (Duke – Hematology), Haley Hostetler (Duke – Allergy & Immunology)
Multifactorial spatiotemporal analyses to evaluate environmental triggers and patient-level clinical characteristics of severe asthma exacerbations in children – R21 NHLBI (3/19 – 3/21)
- Linking Duke EHR data on kids with asthma with geospatial and temporal factors
- Assessing impact of environmental factors on asthma exacerbations
- Collaborators: Jason Lang (Duke – Pediatric Pulmonology, coPI)
Using Machine Learning and Electronic Health Records to predicting hospitalization among diabetics Sanofi S.A. – (12/16 – 1/21)
- Comparing Duke EHR data and Optum Health Data to develop predictive models
- Apply deep learning methods and assess transportability
- Collaborators: Neha Pagidipati (Duke – Cardiology), Ricardo Henao (Duke – B&B)
Understanding and predicting cardiac events in HD using real-time EHRs – K25 – NIDDK (9/13 – 8/18)
- K Award using EHR data on dialysis patients
- Predictive modelling and assessment of EHR data quality
- Mentors: Wolfgang Winkelmayer (Baylor), Michael Pencina (Duke), Trevor Hastie (Stanford), Tim Assimes (Stanford)
