A5090242240- Machine Learning in Healthcare
- COVID-19 diagnosis using AI
- COVID-19 Clinical Research Studies
- Biotechnology and Related Fields
- Intellectual Property and Patents
- Genetics, Bioinformatics, and Biomedical Research
- Health Systems, Economic Evaluations, Quality of Life
- Sepsis Diagnosis and Treatment
- Meta-analysis and systematic reviews
- Rheumatoid Arthritis Research and Therapies
- Biomedical Text Mining and Ontologies
- COVID-19 and healthcare impacts
- Artificial Intelligence in Healthcare and Education
- Particle accelerators and beam dynamics
- COVID-19 epidemiological studies
- Chromosomal and Genetic Variations
- Forecasting Techniques and Applications
- Wireless Sensor Networks for Data Analysis
- Chronic Disease Management Strategies
- Research Data Management Practices
- Survey Methodology and Nonresponse
- Biosimilars and Bioanalytical Methods
- Time Series Analysis and Forecasting
- Risk and Portfolio Optimization
- Survey Sampling and Estimation Techniques
Erasmus MC
2020-2024
Florida State University
2023
Erasmus University Rotterdam
2017-2022
Columbia University
2020
Bristol-Myers Squibb (United States)
2008-2017
Morristown High School
2013
Abstract Background There is currently no consensus on the impact of class imbalance methods performance clinical prediction models. We aimed to empirically investigate random oversampling and undersampling, two commonly used methods, internal external validation models developed using observational health data. Methods externally validated for various outcomes interest within a target population people with pharmaceutically treated depression across four large databases. three different...
Abstract Background We investigated whether we could use influenza data to develop prediction models for COVID-19 increase the speed at which can reliably be developed and validated early in a pandemic. Estimated Risk (COVER) scores that quantify patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization requiring intensive services or death (COVER-I), fatality (COVER-F) 30-days following diagnosis using historical from patients flu-like symptoms tested this patients....
As a response to the ongoing COVID-19 pandemic, several prediction models in existing literature were rapidly developed, with aim of providing evidence-based guidance. However, none these have been found be reliable. Models are commonly assessed risk bias, often due insufficient reporting, use non-representative data, and lack large-scale external validation. In this paper, we present Observational Health Data Sciences Informatics (OHDSI) analytics pipeline for patient-level modeling as...
Objective To develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following diagnosis. Methods We analyzed federated network electronic medical records administrative claims data from 14 sources 6 countries. developed validated 3 using 6,869,127 patients with general practice, emergency room, outpatient visit diagnosed influenza...
Identification of rheumatoid arthritis (RA) patients at high risk adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for variety in RA initiating first-line methotrexate (MTX) monotherapy. Data from 15 claims electronic record databases across 9 countries were used. Models developed internally validated on Optum® De-identified Clinformatics® Mart Database using L1-regularized logistic regression estimate the within 3 months (leukopenia,...
Background SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those do not. COVID-19 vulnerability (C-19) index, a model predicts which will admitted to hospital for treatment of pneumonia or proxies, has been developed and proposed as valuable tool decision-making pandemic. However, at high risk bias according “prediction...
Abstract Background SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. COVID-19 vulnerability (C-19) index, a model predicts which will admitted to hospital for treatment of pneumonia or proxies, has been developed proposed as valuable tool decision making pandemic. However, at high risk bias according...
We investigated a stacking ensemble method that combines multiple base learners within database. The results on external validation across four large databases suggest could improve model transportability.
Asian and Pacific Islander Americans (APIAs) are a diverse group, representing many cultures of origin, range immigration experiences, varying access to economic other resources. Despite stereotypes such as the "model minority" cultural values that stigmatize mental illness complicate health help-seeking, APIAs' psychiatric rehabilitation recovery needs significant. These inadequately treated within existing systems care. Passage California's Mental Health Services Act (MHSA) in 2004 created...
<sec> <title>BACKGROUND</title> SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those do not. COVID-19 vulnerability (C-19) index, a model predicts which will admitted to hospital for treatment of pneumonia or proxies, has been developed and proposed as valuable tool decision-making pandemic. However, at high risk bias according...
Objective: We assessed the comparative risks associated with first line conventional synthetic disease modifying antirheumatic drugs (csDMARDs) in rheumatoid arthritis (RA).Methods: Routine health data from 8 databases (5 US, 1 UK, Germany, and Spain) informed analysis. All were transformed to OMOP common model. New users of monotherapy csDMARD after an RA diagnosis aged 18 or over 2005-2019 included study. Adverse events for methotrexate (MTX), hydroxychloroquine (HCQ), sulfasalazine (SSZ),...