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Improving Blood Donor Care in a Collection Center Through Advanced Data Exploitation
Alice V Bottinelli1, Silvia Pozzi1, Alessia Zanni1
1University of Bergamo, Dalmine (Bg), Italy.
Studies in Health Technology and Informatics
|May 12, 2023
Summary
A new software tool leverages blood donor data for early disease detection. Physicians can use this system to visualize donor history and receive alerts, aiding in preventative care.
Area of Science:
- Biomedical Informatics
- Public Health
- Data Science
Background:
- Blood collection centers accumulate extensive donor data over time.
- Existing data analysis methods lack dedicated tools for disease prediction.
- Early disease detection in donors can significantly improve public health outcomes.
Purpose of the Study:
- To develop a software tool integrating donor data with predictive analytics for early disease detection.
- To provide physicians with alerts and visualizations of critical donor histories.
- To enhance preventative care and early diagnosis in blood donation settings.
Main Methods:
- Development of a modular and flexible software tool.
- Implementation of data exchange functions and alert interfaces.
- Integration of machine learning algorithms for predictive analysis.
Main Results:
- A prototype successfully applied at a blood collection center.
- Physicians found the tool suitable for prevention and early diagnosis.
- Machine learning algorithms demonstrated accurate estimations for key variables.
Conclusions:
- Dedicated software tools are crucial for utilizing blood donor data effectively.
- The developed tool supports prevention and early diagnosis initiatives.
- Visualizing clinical data through software enhances healthcare provider decision-making.