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

Author:

Huang Shih-Hsun12,Peng Bing-Ru1,Lin Chih-Sheng3,Tsai Hui-Chieh42,Pan Lung-Fa15,Pan Lung-Kwang1

Affiliation:

1. Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung, Taiwan

2. Department of Nursing, Taichung Armed Forces General Hospital, Taichung, Taiwan

3. Department of Radiology, BenQ Medical Center, The Affiliated BenQ Hospital of the Nanjing Medical University, Nanjing, Jiangsu, China

4. Department of Nursing, Chung Shan Medical University, Taichung, Taiwan

5. Department of Cardiology, Taichung Armed Forces General Hospital, Taichung, Taiwan

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.

Publisher

IOS Press

Subject

Health Informatics,Biomedical Engineering,Information Systems,Biomaterials,Bioengineering,Biophysics

Reference12 articles.

1. Quantitative evaluation of contrast-induced-nephropathy in vascular post-angiography patients: Feasibility study of a semi-empirical model;Pan;Bio-Medical Materials and Engineering.,2015

2. Revised inverse problem algorithm-based prediction of coronary artery stenosis readings from the clinical data of patients with coronary heart diseases;Pan;Computer Assisted Surgery.,2017

3. Assessment of effective blood concentration readings from clinical data on patients with heart failure diseases after digoxin intake: A projection based on the inverse problem algorithm;Lin;JMMB.,2019

4. Inverse problem algorithm application to semi-quantitative analysis of 272 patients with ischemic stroke symptoms: Carotid stenosis risk assessment for five risk factors;Lin;JMMB.,2020

5. A six-parameter semi-quantitative analysis of 251 patients for the enhanced triggered timing of head and neck CT angiography scanning via the inverse problem algorithm;Lin;JMMB.,2020

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3