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Utilizing Data Analytics in Laboratory Medicine: Predicting Patient Outcomes Through Laboratory Trends in a Tertiary Hospital Setting

Author(s) Faisal E. Aljwyaied, Ali A. Alshehri
Country Saudi Arabia
Abstract Background:
Data analytics has emerged as a transformative tool in laboratory medicine, enabling the prediction of patient outcomes through the analysis of laboratory trends. This study explores the utility of data analytics in a tertiary hospital setting, focusing on its ability to identify high-risk patients and improve clinical decision-making.
Methods:
A retrospective analysis was conducted using de-identified laboratory data from 200 adult patients. Key laboratory markers, including lactate, CRP, and creatinine, were analyzed for their association with adverse outcomes. Predictive models were developed using machine learning techniques, and their performance was evaluated based on sensitivity, specificity, and area under the ROC curve (AUC).
Results:
Elevated lactate (p = 0.01), CRP (p = 0.02), and creatinine (p = 0.03) were significantly associated with adverse outcomes. The predictive model achieved a sensitivity of 85%, specificity of 90%, and an AUC of 0.92, demonstrating excellent performance in identifying high-risk patients.
Conclusion:
This study highlights the potential of data analytics in leveraging laboratory trends to predict patient outcomes and optimize care delivery. Integrating predictive models into clinical workflows can enhance risk stratification, resource allocation, and personalized care in tertiary hospital settings.
Keywords Data analytics, laboratory medicine, predictive models, patient outcomes, tertiary hospital, lactate, CRP, machine learning.
Field Sociology > Health
Published In Volume 5, Issue 3, July-September 2014
Published On 2014-07-02
Cite This Utilizing Data Analytics in Laboratory Medicine: Predicting Patient Outcomes Through Laboratory Trends in a Tertiary Hospital Setting - Faisal E. Aljwyaied, Ali A. Alshehri - IJSAT Volume 5, Issue 3, July-September 2014. DOI 10.5281/zenodo.14500432
DOI https://doi.org/10.5281/zenodo.14500432
Short DOI https://doi.org/g8vqr5
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