A5060106740- COVID-19 and healthcare impacts
- COVID-19 Clinical Research Studies
- Economic and Financial Impacts of Cancer
- Biomedical Text Mining and Ontologies
- Cancer Genomics and Diagnostics
- Neutropenia and Cancer Infections
- Machine Learning in Healthcare
- Genomics and Rare Diseases
- Cancer survivorship and care
- Artificial Intelligence in Healthcare
- Semantic Web and Ontologies
- Infection Control and Ventilation
- Ethics in Clinical Research
- Healthcare cost, quality, practices
- Data Quality and Management
- Organ Donation and Transplantation
- Organ Transplantation Techniques and Outcomes
- Health Systems, Economic Evaluations, Quality of Life
- Genetic Syndromes and Imprinting
- Biomedical Ethics and Regulation
- Genetic and Kidney Cyst Diseases
- Lung Cancer Treatments and Mutations
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 and Mental Health
- AI in cancer detection
Memorial Sloan Kettering Cancer Center
2019-2021
Kettering University
2020
The Rogosin Institute
2008-2010
Cornell University
2008
Coronavirus-2019 (COVID-19) mortality is higher in patients with cancer than the general population, yet cancer-associated risk factors for COVID-19 adverse outcomes are not fully characterized.
Genetic testing of PKD1 and PKD2 is useful for diagnosis prognosis autosomal dominant polycystic kidney disease (ADPKD), particularly in asymptomatic individuals or those without a family history. complicated by the large transcript size, complexity gene region, extent variations. A molecular assay was developed using Transgenomic's SURVEYOR Nuclease WAVE Nucleic Acid High Sensitivity Fragment Analysis System to screen variants, followed sequencing variant segments, thereby reducing...
Background. Despite marked improvement in short-term renal allograft survival rates (GSR) recent years, long-term GSR remained elusive.
Background Accurate and rapid clinical decisions based on real-world evidence are essential for patients with cancer. However, the complexity of chemotherapy regimens cancer impedes retrospective research that uses observational health databases. Objective The aim this study is to compare anticancer treatment trajectories patterns events according regimen type using episodes determined by an algorithm. Methods We developed algorithm extract regimen-level abstracted from medication records in...
Observational research in cancer requires substantially more detail than most other therapeutic areas. Cancer conditions are defined through histology, affected anatomical structures, staging and grading, biomarkers, treated with complex therapies. Here, we show a new module as part of the OMOP CDM, allowing manual automated abstraction standardized analytics. We tested model EHR registry data against number typical use cases.
Abstract Introduction: The need to rapidly collect, integrate, and share data on COVID-19 patients with cancer at scale has given rise multiple internal cross-institutional research registries. These registries support use cases that require different levels of granularity are built using mixed standards. Ensuring semantic interoperability quality this is critical for generating reliable reproducible evidence. At MSK, we created a framework enabled the rapid development semantically...
Abstract Introduction: Granular cancer patient treatment data collection, and subsequent mapping to standard regimen definitions, are vital next steps in advancement of observational studies oncology. However, the identification details, including dose schedule, is a prerequisite for both collection mapping. At level, claims databases useful but limited resource. Most registries, such as National Cancer Institute Surveillance, Epidemiology, End Results (SEER) program Commission on Database,...
<sec> <title>BACKGROUND</title> Accurate and rapid clinical decisions based on real-world evidence are essential for patients with cancer. However, the complexity of chemotherapy regimens cancer impedes retrospective research that uses observational health databases. </sec> <title>OBJECTIVE</title> The aim this study is to compare anticancer treatment trajectories patterns events according regimen type using episodes determined by an algorithm. <title>METHODS</title> We developed algorithm...