Potential risk quantification from multiple biological factors via the inverse problem algorithm as an artificial intelligence tool in clinical diagnosis.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read Online Read More Add to Saved list
  • Additional Information
    • Source:
      Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9314590 Publication Model: Print Cited Medium: Internet ISSN: 1878-7401 (Electronic) Linking ISSN: 09287329 NLM ISO Abbreviation: Technol Health Care Subsets: MEDLINE
    • Publication Information:
      Publication: Amsterdam : IOS Press
      Original Publication: Amsterdam ; New York : Elsevier, c1993-
    • Subject Terms:
      Artificial Intelligence* ; Algorithms*; Humans ; Risk Factors
    • Abstract:
      Background: The inverse problem algorithm (IPA) uses mathematical calculations to estimate the expectation value of a specific index according to patient risk factor groups. The contributions of particular risk factors or their cross-interactions can be evaluated and ranked by their importance.
      Objective: This paper quantified the potential risks from multiple biological factors by integrated case studies in clinical diagnosis via the IPA technique. Acting as artificial intelligence field component, this technique constructs a quantified expectation value from multiple patients' biological index series, e.g., the optimal trigger timing for CTA, a particular drug in blood concentration data, the risk for patients with clinical syndromes.
      Methods: Common biological indices such as age, body surface area, mean artery pressure, and others are treated as risk factors upon their normalization to the range from -1.0 to +1.0, with a non-dimensional zero point 0.0 corresponding to the average risk factor index. The patients' quantified indices are re-arranged into a large data matrix. Next, the inverse and column matrices of the compromised numerical solution are constructed.
      Results: This paper discusses quasi-Newton and Rosenbrock analyses performed via the STATISTICA program to solve the above inverse problem, yielding the specific expectation value in the form of a multiple-term nonlinear semi-empirical equation. The extensive background, including six previous publications of these authors' team on IPA, was comprehensively re-addressed and scrutinized, focusing on limitations, stumbling blocks, and validity range of the IPA approach as applied to various tasks of preventive medicine. Other key contributions of this study are detailed estimations of the effect of risk factors' coupling/cross-interactions on the IPA computations and the convergence rate of the derived semi-empirical equation viz. the final constant term.
      Conclusion: The main findings and practical recommendations are considered useful for preventive medicine tasks concerning potential risks of patients with various clinical syndromes.
    • References:
      Hypertension. 2000 Nov;36(5):801-7. (PMID: 11082146)
      Biomed Mater Eng. 2015;26 Suppl 1:S851-60. (PMID: 26406083)
      Comput Assist Surg (Abingdon). 2017 Dec;22(sup1):70-78. (PMID: 28922944)
      Technol Health Care. 2022;30(S1):91-103. (PMID: 35124587)
    • Contributed Indexing:
      Keywords: Clinical syndrome; artificial intelligence; biological index series; column matrix; inverse problem algorithm; nonlinear semi-empirical equation; risk factors
    • Publication Date:
      Date Created: 20230411 Date Completed: 20230502 Latest Revision: 20230523
    • Publication Date:
      20231215
    • Accession Number:
      PMC10200174
    • Accession Number:
      10.3233/THC-236008
    • Accession Number:
      37038783