Ahmad Baghal
Sourced from institutional research profiles (UAMS TRI or ARA).
Assistant Professor
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Ahmad Baghal's research focuses on informatics and data management within health sciences, with a particular emphasis on applications related to COVID-19 and multimorbidity. He has investigated data quality within centralized and federated systems, as well as methods for participant identification pseudonymization in multi-study research. His work also includes exploring the relationship between HDL cholesterol levels and COVID-19 susceptibility, and the potential for resting-state brain metabolic fingerprinting in predicting major depression in multiple myeloma patients. Baghal has published on quantifying care delivery team influences on hospitalization outcomes for patients with multimorbidity and cultivating informatics capacity within learning health systems. His research network includes collaborators such as Bradley C. Martin, Antonije Lazic, Mandana Rezaeiahari, and J. Mick Tilford, all at the University of Arkansas for Medical Sciences. Baghal's scholarship metrics include an h-index of 7, with 18 total publications and 253 total citations.
Metrics
- h-index: 7
- Publications: 18
- Citations: 256
Selected Publications
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Linking <i>The Cancer Imaging Archive</i> and <scp>GenBank</scp> to the <scp>National Clinical Cohort Collaborative</scp> (2024)
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Implementing Data Governance with Multi-Modal Privacy-Preserving Record Linkages between Restricted and Public Open Enclaves (2024)
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In-Hospital Mortality by Race and Ethnicity Among Hospitalized COVID-19 Patients Using Data From the US National COVID Cohort Collaborative (2024)
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In-Hospital Mortality by Race and Ethnicity Among Hospitalized Covid-19 Patients Using Data from the Us National Covid Cohort Collaborative (2023)
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Quantifying care delivery team influences on the hospitalization outcomes of patients with multimorbidity: Implications for clinical informatics (2023)
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Cultivating informatics capacity for multimorbidity: A learning health systems use case (2022)
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HDL cholesterol levels and susceptibility to COVID-19 (2022)
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Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection (2022)
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Resting-state brain metabolic fingerprinting clusters (biomarkers) and predictive models for major depression in multiple myeloma patients (2021)
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API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research (2021)
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CRRT Is Associated with Improved Kidney Recovery from Dialysis-Requiring AKI in a Multicenter Retrospective Analysis (2020)
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Chest imaging representing a COVID-19 positive rural U.S. population (2020)
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Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models (2020)
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Agile Natural Language Processing Model for Pathology Knowledge Extraction and Integration with Clinical Enterprise Data Warehouse (2019)
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Leveraging Graph Models to Design Acute Kidney Injury Disease Research Data Warehouse (2019)
Grants & Funding
As listed on this researcher's institutional profile.
- ACT Wave Site Consortium Agreement with University of Pittsburgh CTSI - Continuation NIH/National Center for Advancing Translational Sciences via University of Pittsburgh
- OneFlorida+ Clinical Research Network Phase 4 Patient-Centered Outcomes Research Institute via University of Florida
- TO1 - TCIA TO 2 TCIA-TCGA NIH/Nat. Cancer Institute via Leidos
- PCORI-UF Pass through SUB00004899 Patient-Centered Outcomes Research Institute via University of Florida
- Expanding Translational Research in Arkansas NIH
Collaboration Network
Top Collaborators
- API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research
- Implementing Data Governance with Multi-Modal Privacy-Preserving Record Linkages between Restricted and Public Open Enclaves
- Linking <i>The Cancer Imaging Archive</i> and <scp>GenBank</scp> to the <scp>National Clinical Cohort Collaborative</scp>
- API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research
- Cultivating informatics capacity for multimorbidity: A learning health systems use case
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- HDL cholesterol levels and susceptibility to COVID-19
- Higher HDL Cholesterol Levels Decrease Susceptibility to COVID-19 Infection
- Cultivating informatics capacity for multimorbidity: A learning health systems use case
- Quantifying care delivery team influences on the hospitalization outcomes of patients with multimorbidity: Implications for clinical informatics
- Cultivating informatics capacity for multimorbidity: A learning health systems use case
- Quantifying care delivery team influences on the hospitalization outcomes of patients with multimorbidity: Implications for clinical informatics
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