A5023482160- Scientific Computing and Data Management
- Machine Learning in Healthcare
- COVID-19 and healthcare impacts
- COVID-19 Clinical Research Studies
- Vector-borne infectious diseases
- Artificial Intelligence in Healthcare
- Neonatal and fetal brain pathology
- Leptospirosis research and findings
- Long-Term Effects of COVID-19
- Neonatal Respiratory Health Research
- Infant Development and Preterm Care
- Genetic Syndromes and Imprinting
- Blood transfusion and management
- Traumatic Brain Injury and Neurovascular Disturbances
- Prenatal Screening and Diagnostics
- Viral Infections and Vectors
- Speech and dialogue systems
- Digital Imaging for Blood Diseases
- Health, Environment, Cognitive Aging
- Blood groups and transfusion
- Frailty in Older Adults
- Congenital limb and hand anomalies
- Cardiac Arrest and Resuscitation
- Family and Patient Care in Intensive Care Units
- Neonatal Health and Biochemistry
Yale University
2019-2021
Yale New Haven Hospital
2021
Eastern Virginia Medical School
2015-2018
Children's Hospital of The King's Daughters
2017-2018
Health care data are increasing in volume and complexity. Storing analyzing these to implement precision medicine initiatives data-driven research has exceeded the capabilities of traditional computer systems. Modern big platforms must be adapted specific demands health designed for scalability growth.The objectives our study were (1) demonstrate implementation a science platform built on open source technology within large, academic system (2) describe 2 computational applications such...
Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients SARS-CoV-2.
Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients SARS-CoV-2. This an observational, retrospective study based on real-world data for 7,995 SARS-CoV-2 from a clinical repository. Yale New Haven Health (YNHH) is five-hospital academic health system serving diverse patient population community teaching facilities both urban suburban areas. The included...
With only a small number of cases in the medical literature, mosaic trisomy 15 liveborn infants is very rare. Despite its rarity, similar features among individuals have been described, including intrauterine growth retardation, craniofacial abnormalities and facial dysmorphisms, cardiac disease, other organ anomalies. Very few liveborns survived first year life. We report here on term infant with restriction multiple congenital anomalies who was found to 15. The proband presented some...
Clinical babesiosis is diagnosed, and parasite burden determined, by microscopic inspection of a thick or thin Giemsa-stained peripheral blood smear. However, quantitative analysis manual microscopy subject to error. As such, methods for the automated measurement percent parasitemia in digital images smears could improve clinical accuracy, relative predicate method.
Objective: To (1) demonstrate the implementation of a data science platform built on open-source technology within large, academic healthcare system and (2) describe two computational applications such platform. Materials Methods: A based several open source technologies was deployed to support real-time, big workloads. Data acquisition workflows for Apache Storm NiFi were developed in Java Python capture patient monitoring laboratory downstream analytics. Results: The use emerging...
Abstract Objective As the COVID-19 pandemic has evolved, a key question for health care systems is whether in-hospital mortality changed over time and if so, what factors contributed to these changes. Our goal was leverage real-world data spanning two surges first year of determine temporal trend mortality. Design This an observational, retrospective study based on patients admitted with COVID-19. Generalized additive models (GAM) were used evaluate association covariates composite outcome...
<sec> <title>BACKGROUND</title> Health care data are increasing in volume and complexity. Storing analyzing these to implement precision medicine initiatives data-driven research has exceeded the capabilities of traditional computer systems. Modern big platforms must be adapted specific demands health designed for scalability growth. </sec> <title>OBJECTIVE</title> The objectives our study were (1) demonstrate implementation a science platform built on open source technology within large,...
Background Clinical babesiosis is diagnosed, and parasite burden determined, by microscopic inspection of a thick or thin Giemsa-stained peripheral blood smear. However, quantitative analysis manual microscopy subject to observer bias, slide distribution errors, statistical sampling error, recording inherently burdensome from time management workflow efficiency standpoints. As such, methods for the automated measurement percent parasitemia in digital images smears could improve clinical...