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Potential risk quantification from multiple biological factors via the inverse problem algorithm as an artificial
Shih-Hsun Huang1,2, Bing-Ru Peng1, Chih-Sheng Lin3
1Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung, Taiwan.
Summary
The inverse problem algorithm (IPA) quantifies patient risks using biological data. This AI-driven method analyzes risk factors and their interactions for improved preventive medicine strategies.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Preventive Medicine
Background:
- The inverse problem algorithm (IPA) employs mathematical calculations to assess patient risk factors.
- IPA evaluates and ranks the importance of individual risk factors and their interactions.
Purpose of the Study:
- To quantify potential risks from multiple biological factors using IPA in clinical diagnosis.
- To construct a quantified expectation value from patient biological index series, serving as an AI component.
Main Methods:
- Normalized common biological indices (age, blood pressure) to a -1.0 to +1.0 range.
- Constructed data matrices and solved inverse problems using quasi-Newton and Rosenbrock analyses.
Main Results:
- Developed a nonlinear semi-empirical equation for specific expectation values.
- Analyzed IPA's limitations, validity, and the impact of risk factor interactions on computations.
Conclusions:
- Findings offer practical recommendations for preventive medicine.
- The study enhances risk assessment for patients with various clinical syndromes.
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