Browsing by Author "Kanakasabai, Saravanan"

Now showing 1 - 4 of 4
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    348 Real-time data synchronization: Assessing the implementation of REDCap CDIS (Clinical Data Interoperability Service) for EHR systems
    (Cambridge University Press, 2025-04-11) Amin, Waqas; Kanakasabai, Saravanan; Grout, Randall; Butler, Joe; Michael, Scott; Schleyer, Titus; Pediatrics, School of Medicine
    Objectives/Goals: This study tests the REDCap Clinical Data Interoperability Service (CDIS) for streamlined data extraction from electronic health records (EHRs) for research. Managed by Clinical and Translational Science Institute, IU Health, and Eskenazi Health, CDIS offers real-time data syncing, automated workflows, and HIPAA-compliant data security. Methods/Study Population: The REDCap CDIS uses the Fast Health Interoperability Resource (FHIR) Application Programming Interface (API) to extract data from EHRs. It includes the Clinical Data Pull (CDP), which automatically pulls EHR data into user-defined REDCap fields, and the Clinical Data Mart (CDM), which collects longitudinal patient data. Three use cases were selected to assess the CDIS’s effectiveness in extracting data from the IUH Cerner and Eskenazi Epic EHR systems. The technical team set up clinical data mapping and adjudication processes, simplifying complex manual data extraction. Results/Anticipated Results: The CDIS successfully achieved real-time data synchronization during pilot testing with each EHR system. We extracted demographics, drugs, procedures, labs, and conditions. The mapping interface supports many-to-one data point mapping for the study data dictionary, and the adjudication process ensures data quality before integration into the REDCap database. The CDIS also improved data security and HIPAA compliance. An implementation intake process was developed for Indiana University investigators, allowing them to use the service for affordable clinical data extraction from EHR systems. Discussion/Significance of Impact: The implementation and testing of the REDCap CDIS demonstrates its effectiveness in streamlining EHR data extraction for research. The CDIS facilitates real-time data synchronization, automated workflows, and enhanced data security, offering a cost-effective solution through collaborative oversight with research teams.
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    Analysis of Co-Indicators and Counter-Indicators Among Patients Using Coding Algorithms: Learning Phenotype study
    Reddy, Nagarjuna; Jones, Josette; Kanakasabai, Saravanan; Klapper, Gregory
    Chronic complications associated with the diabetes are responsible for increase in mortality and morbidity rate. The main aim of the project is to analyze the co-indicators and counter-indicators among the patients by mapping the conditions with ICD codes and developing an algorithm. A positive and strong correlation is identified with respect to BMI, Poverty, Education, Age and T2DM cohorts and it's comorbidities.
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    Neural networks for mining the associations between diseases and symptoms in clinical notes
    (Springer, 2018-11-28) Shah, Setu; Luo, Xiao; Kanakasabai, Saravanan; Tuason, Ricardo; Klopper, Gregory; Engineering Technology, School of Engineering and Technology
    There are challenges for analyzing the narrative clinical notes in Electronic Health Records (EHRs) because of their unstructured nature. Mining the associations between the clinical concepts within the clinical notes can support physicians in making decisions, and provide researchers evidence about disease development and treatment. In this paper, in order to model and analyze disease and symptom relationships in the clinical notes, we present a concept association mining framework that is based on word embedding learned through neural networks. The approach is tested using 154,738 clinical notes from 500 patients, which are extracted from the Indiana University Health’s Electronic Health Records system. All patients are diagnosed with more than one type of disease. The results show that this concept association mining framework can identify related diseases and symptoms. We also propose a method to visualize a patients’ diseases and related symptoms in chronological order. This visualization can provide physicians an overview of the medical history of a patient and support decision making. The presented approach can also be expanded to analyze the associations of other clinical concepts, such as social history, family history, medications, etc.
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    PPARγ Agonists Promote Oligodendrocyte Differentiation of Neural Stem Cells by Modulating Stemness and Differentiation Genes
    (Public Library of Science, 2012) Kanakasabai, Saravanan; Pestereva, Ecaterina; Chearwae, Wanida; Gupta, Sushil K.; Ansari, Saif; Bright, John J.; Medicine, School of Medicine
    Neural stem cells (NSCs) are a small population of resident cells that can grow, migrate and differentiate into neuro-glial cells in the central nervous system (CNS). Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor transcription factor that regulates cell growth and differentiation. In this study we analyzed the influence of PPARγ agonists on neural stem cell growth and differentiation in culture. We found that in vitro culture of mouse NSCs in neurobasal medium with B27 in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF) induced their growth and expansion as neurospheres. Addition of all-trans retinoic acid (ATRA) and PPARγ agonist ciglitazone or 15-Deoxy-Δ(12,14)-Prostaglandin J(2) (15d-PGJ2) resulted in a dose-dependent inhibition of cell viability and proliferation of NSCs in culture. Interestingly, NSCs cultured with PPARγ agonists, but not ATRA, showed significant increase in oligodendrocyte precursor-specific O4 and NG2 reactivity with a reduction in NSC marker nestin, in 3-7 days. In vitro treatment with PPARγ agonists and ATRA also induced modest increase in the expression of neuronal β-III tubulin and astrocyte-specific GFAP in NSCs in 3-7 days. Further analyses showed that PPARγ agonists and ATRA induced significant alterations in the expression of many stemness and differentiation genes associated with neuro-glial differentiation in NSCs. These findings highlight the influence of PPARγ agonists in promoting neuro-glial differentiation of NSCs and its significance in the treatment of neurodegenerative diseases.