Karthik Ramesh
A5076829264- SARS-CoV-2 and COVID-19 Research
- COVID-19 epidemiological studies
- COVID-19 diagnosis using AI
- SARS-CoV-2 detection and testing
- Machine Learning in Healthcare
- Emergency and Acute Care Studies
- Healthcare cost, quality, practices
- Bacterial Identification and Susceptibility Testing
- Disaster Response and Management
- Global Security and Public Health
- Sepsis Diagnosis and Treatment
- Healthcare Policy and Management
- Nursing Roles and Practices
- Child Nutrition and Feeding Issues
- Quality and Safety in Healthcare
- Animal Disease Management and Epidemiology
- Cardiac Arrest and Resuscitation
- Vaccine Coverage and Hesitancy
- Long-Term Effects of COVID-19
- Photoacoustic and Ultrasonic Imaging
- Health Systems, Economic Evaluations, Quality of Life
- Optical Coherence Tomography Applications
- Infant Health and Development
- Climate Change and Health Impacts
- Diphtheria, Corynebacterium, and Tetanus
University of California, San Diego
2022-2024
University of San Diego
2024
Scripps (United States)
2023
Scripps Institution of Oceanography
2023
Scripps Research Institute
2021-2022
Howard Hughes Medical Institute
2019
University of California, San Francisco
2019
Abstract As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing infeasible at scale, especially in areas with limited resources, participation, or and/or sequencing capacity, which can also introduce biases 1–3 . RNA concentration wastewater successfully tracks regional infection dynamics provides less biased abundance estimates than 4,5 Tracking virus genomic sequences...
Purpose: To evaluate the performance of various approaches processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate geographic atrophy (GA) lesions caused by age-related macular degeneration (AMD). Methods: The study used OCT volumes GA patients/eyes from lampalizumab clinical trials (NCT02247479, NCT02247531, NCT02479386); 1219 442 eyes model development holdout evaluation, respectively. Four were...
As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. RNA concentration wastewater successfully tracks regional infection dynamics provides less biased abundance estimates than testing. Tracking virus genomic sequences would...
Abstract Regional connectivity and land travel have been identified as important drivers of SARS-CoV-2 transmission. However, the generalizability this finding is understudied outside well-sampled, highly connected regions. In study, we investigated relative contributions regional intercontinental to source-sink dynamics for Jordan Middle East. By integrating genomic, epidemiological data show that source introductions into was dynamic across 2020, shifting from seeding in early pandemic...
Many patients infected with the SARS-CoV-2 virus (COVID-19) continue to experience symptoms for weeks years as sequelae of initial infection, referred "Long COVID". Although many studies have described incidence and symptomatology Long COVID, there are little data reporting potential burden COVID on surgical departments. A previously constructed database survey respondents who tested positive COVID-19 was queried, identifying experiencing consistent COVID. Additional chart review determined...
<sec> <title>BACKGROUND</title> Sepsis is a major cause of morbidity and mortality for which early intervention improves patient outcomes. However, many patients experience delays in appropriate diagnosis treatment. Predictive modeling artificial intelligence may aid recognition sepsis but there remains considerable disconnect between the development predictive algorithms their use clinical care. Despite importance user adoption efficacy models, are relatively few studies focused on provider...
Abstract Objectives Traditional methods for medical device post-market surveillance often fail to accurately account operator learning effects, leading biased assessments of safety. These struggle with non-linearity, complex curves, and time-varying covariates, such as physician experience. To address these limitations, we sought develop a machine (ML) framework detect adjust effects. Materials Methods A gradient-boosted decision tree ML method was used analyze synthetic datasets that...
Summary The maturation of genomic surveillance in the past decade has enabled tracking emergence and spread epidemics at an unprecedented level. During COVID-19 pandemic, for example, data revealed that local varied considerably frequency SARS-CoV-2 lineage importation persistence, likely due to a combination restrictions changing connectivity. Here, we show are driven by regional transmission, including across international boundaries, but can become increasingly connected distant locations...
Sepsis is a major cause of morbidity and mortality worldwide, caused by bacterial infection in majority cases. However, fungal sepsis often carries higher rate both due to its prevalence immunocompromised patients as well delayed recognition. Using chest x-rays, associated radiology reports, structured patient data from the MIMIC-IV clinical dataset, authors present machine learning methodology differentiate between bacterial, fungal, viral sepsis. Model performance shows AUCs 0.81, 0.83,...
Summary Regional connectivity and land-based travel have been identified as important drivers of SARS-CoV-2 transmission. However, the generalizability this finding is understudied outside well-sampled, highly connected regions such Europe. In study, we investigated relative contributions regional intercontinental to source-sink dynamics for Jordan wider Middle East. By integrating genomic, epidemiological data show that source introductions into was dynamic across 2020, shifting from...
<sec> <title>BACKGROUND</title> Sepsis is a major cause of morbidity and mortality for which early intervention improves patient outcomes. However, many patients experience delays in appropriate diagnosis treatment. Predictive modeling machine learning may aid recognition sepsis but there remains considerable disconnect between the development predictive algorithms their use clinical care. Despite importance user adoption efficacy models, are relatively few studies focused on provider...