DOI: https://doi.org/10.15588/1607-3274-2019-3-8

DEVELOPMENT OF A METHOD FOR CALCULATING SECONDARY SIGNS OF DISEASE BY ITS PRIMARY SYMPTOMS

A. V. Zharkova, V. V. Nahornyi

Abstract


Context. The article describes тhe actual problem of raising the informativeness of diagnostic signs (symptoms) ensuring the introduction
of computer technologies, for example, in medical practice, for the rapid formulation of a reliable diagnosis has been solved.
Objective. The goal of the work is to develop a methodology for quantitative assessment of the dynamics of symptoms and the unification of the form of their presentation for the formalization on this basis of the process of diagnosis and prediction of the moment of exacerbation of the disease.
Method. Modern methods of diagnosis based on a comparison of the current values of symptoms with their normative maximum permissible values. However, the norms are based on average statistical data, which can only relate to a specific patient with a certain degree of probability. This is the cause of errors in predicting for this individual the moment of exacerbation of his disease. In this regard, using the example of cardio disease to solve this problem, it was proposed for the first time to supplement the traditional method of diagnosis with the calculation of secondary informative signs (indicators). At the same time, the process of making a diagnosis in accordance with the rules of discriminant analysis consists in comparing indicators with a limited number of clusters describing consistently worsening disease pathology. Thus, it is possible to replace many combinations of the values analyzed at the diagnosis
of symptoms with a finite series of clusters, which significantly increases the speed of diagnosis and is a necessary condition for
the computerization of the diagnosis process itself.
Results. The reviewed methodology was successfully tested to control the severity of three patients with coronary heart disease, allowing retrospectively predicting the actual calendar date of exacerbation of the disease, which in practice allows you to plan the optimal strategy for timely treatment of the disease.
Conclusions. A methodology has been developed to increase the informativeness of the symptoms of the disease, formalizing the
process of diagnosis and thereby ensuring the introduction of computer technology into medical practice in order to promptly determine
for each patient the time of exacerbation of his disease and to establish a reliable diagnosis on this basis

Keywords


Еarly diagnosis of diseases, informativeness of symptoms, ECG parameters, status indicators, computer technologies

References


Colkesen E. B., Ferket B. S., Tijssen J. G. P. et al. Effects on cardiovascular disease risk of a web-based health risk assessment

with tailored health advice: a follow-up study, Vascular Health and Risk Management, 2011, No. 7, pp. 67–74. DOI: 10.2147/VHRM.S16340.

Allender S., Scarborough P., Peto V. et al. European Cardiovascular Disease Statistics. Oxford, Department of Public

Health, University of Oxford, 2008, 112 p.

Yusuf S., Hawken S., Ounpuu S. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study, Lancet, 2004, No. 364, pp. 937–952. DOI: 10.1016/S0140-6736(04)17018-9.

Sadki R., Ewart S., Grangier Ch. et al. World Health Organization Preventing Chronic Diseases: a Vital Investment

– WHO global report. Geneva, World Health Organization, 2005, 178 p.

Yach D., Hawkes C., Gould C. L. et al. The global burden of chronic diseases: overcoming impediments to prevention

and control, JAMA, 2004, No. 291, pp. 2616–2622. DOI:10.1001/jama.291.21.2616.

Carnethon M., Whitsel L. P., Franklin B. A. Worksite wellness programs for cardiovascular disease prevention: a policy

statement from the American heart association, Circulation, 2009, No. 120, pp. 1725–1741.

DOI: 10.1161/CIRCULATIONAHA.109.192653.

Shaw L. J., Narula J. Risk Assessment and Predictive Value of Coronary Artery Disease Testing, Journal of Nuclear

Medicine, 2009, No. 50, pp. 1296–1306. DOI:10.2967/jnumed.108.059592.

O’Connor C. M., Velazquez E. J., Gardner L. H. et al. Comparison of coronary artery bypass grafting versus medical

therapy on long-term outcome in patients with ischemic cardiomyopathy (a 25-year experience from the Duke cardiovascular

disease data bank), American Journal of Cardiology, 2002, №90, pp. 101–107.

Cleland J. G., Calvert M., Freemantle N. et al. The heart failure revascularisation trial (HEART), European Journal

of Heart Failure, 2011, No. 13. – P. 227 – 233. DOI:10.1093/eurjhf/hfq230.

Velazquez E. J., Lee K. L., Deja M. A. et al. Coronaryartery bypass surgery in patients with left ventricular dysfunction,

The New England Journal of Medicine, 2011, No. 364, pp. 1607–1616. DOI: 10.1056/NEJMoa1100356.

Ghali J. K. CABG in patients with left ventricular dysfunction, The New England Journal of Medicine, 2011, No. 365,

pp. 470–461. DOI: 10.1056/NEJMoa1100356.

Hachamovitch R., Hayes S. W., Friedman J. D. Comparison of the short-term survival benefit associated with revascularization

compared with medical therapy in patients with noprior coronary artery disease undergoing stress myocardial perfusion ingle photonemission computed omography / [//Circulation, 2003, No. 107, pp. 2900–2907. DOI:10.1161/01.CIR.0000072790.23090.41.

Lawson A. E., Daniel E. S. Inferences of clinical diagnostic reasoning and diagnostic error, Journal of Biomedical Informatics,

, No. 44, pp. 402–412. DOI:10.1016/j.jbi.2010.01.003.

Berner E. S., Graber M. L. Overconfidence as a cause of diagnostic error in medicine, The American Journal of

Medicine, 2008, No. 121, pp. 2–23. DOI: 10.1016/j.amjmed.2008.01.001.

Croskerry P., Norman G. Overconfidence in clinical decision making, The American Journal of Medicine, 2008, No. 121, pp. 24–29. DOI: 10.1016/j.amjmed.2008.02.001.

Ely J. W., Graber M. L., Croskerry P. Checklists to reduce diagnostic errors, Academic Medicin, 2011, No. 86, pp. 307–

DOI: 10.1097/ACM.0b013e31820824cd.

Schiff G. D., Hasan O., Kim S. et al. Diagnostic error in medicine, Archives of Internal Medicine, 2009, No. 169,

pp. 1881–1887. DOI: 10.1001/archinternmed.2009.333.

Coderre S., Mandin H., Harasym P. H. et al. Diagnostic reasoning strategies and diagnostic success, Medical Education

Journal, 2003, No. 37, pp. 695–703.

Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them, Academic Medicin, 2003,

No. 78, pp. 775–780.

Mitchell J., Feldman M. D., Edward P. et al. Impact of a Computer-Based Diagnostic Decision Support Tool on the

Differential Diagnoses of Medicine Residents, The Journal of Graduate Medical Education, 2012, No. 4, pp. 227–231.

DOI: 10.4300/JGME-D-11-00180.1.

Miller R. A. Computer-assisted diagnostic decision support: history, challenges, and possible paths forward, Advances in

Health Sciences Education, 2009, No. 14, pp. 89–106. DOI:10.1007/s10459-009-9186-y.

[Roy P. M., Durieux P., Gillaizeau F. et al. A computerized handheld decision-support system to improve pulmonary

embolism diagnosis, Annals of Internal Medicine, 2009, No. 151, pp. 677–686. DOI: 10.7326/0003-4819-151-10-

-00003.

Curry S. J., Krist A. H., Owens D. K. et al. Screening for Cardiovascular Disease Risk With Electrocardiography: US

Preventive Services Task Force Recommendation Statement, JAMA, 2018, № 319, pp. 2308–2314.

DOI: 10.1001/jama.2018.6848.

Bhardwaj R. Chest pain, dynamic ECG changes and coronary artery disease, The Journal of the Association of Physicians

of India, 2007, No. 55, pp. 556–559.

Alpaydin E. Introduction to Machine Learning. London, The MIT Press, 2010, 640 p.


GOST Style Citations


1. Effects on cardiovascular disease risk of a web-based health risk assessment with tailored health advice: a follow-up study / [E. B. Colkesen, B. S. Ferket, J. G. P. Tijssen et al.] // Vascular Health and Risk Management. – 2011. – № 7. – P. 67–74. DOI: 10.2147/VHRM.S16340.
2. European Cardiovascular Disease Statistics / [S. Allender, P. Scarborough, V. Peto et al.]. – Oxford : Department of Public Health, University of Oxford, 2008. – 112 p.
3. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study / [S. Yusuf, S. Hawken, S. Ounpuu] // Lancet. – 2004. – № 364. – P. 937–952. DOI:10.1016/S0140-6736(04)17018-9.
4. World Health Organization Preventing Chronic Diseases: a Vital Investment – WHO global report / [R. Sadki, S. Ewart, Ch. Grangier et al.]. – Geneva : World Health Organization, 2005. – 178 p.
5. The global burden of chronic diseases: overcoming impediments to prevention and control / [D. Yach, C. Hawkes, C. L. Gould et al.] // JAMA. – 2004. – № 291. – P. 2616–2622. DOI: 10.1001/jama.291.21.2616.
6. Worksite wellness programs for cardiovascular disease prevention: a policy statement from the American heart association / [M. Carnethon, L. P. Whitsel, B. A. Franklin] // Circulation. – 2009. – № 120. – P. 1725–1741. DOI: 10.1161/CIRCULATIONAHA.109.192653.
7. Shaw L. J. Risk Assessment and Predictive Value of Coronary Artery Disease Testing / L. J. Shaw, J. Narula // Journal of Nuclear Medicine. – 2009. – № 50. – P. 1296–1306. DOI:10.2967/jnumed.108.059592.
8. Comparison of coronary artery bypass grafting versus medical therapy on long-term outcome in patients with ischemic cardiomyopathy (a 25-year experience from the Duke cardiovascular disease data bank) / [C. M. O’Connor, E. J. Velazquez, L. H. Gardner et al.] // American Journal of Cardiology.
– 2002. – №90. – P. 101 – 107.
9. The heart failure revascularisation trial (HEART) / [J. G. Cleland, M. Calvert, N. Freemantle et al.] // European Journal of Heart Failure. – 2011. – №13. – P. 227 – 233. DOI: 10.1093/eurjhf/hfq230.
10. Coronary-artery bypass surgery in patients with left ventricular dysfunction / [E. J. Velazquez, K. L. Lee, M. A. Deja et al.] // The New England Journal of Medicine. – 2011.– № 364. – P. 1607–1616. DOI:10.1056/NEJMoa1100356.
11. Ghali J. K. CABG in patients with left ventricular dysfunction / J. K. Ghali // The New England Journal of Medicine. – 2011. – № 365. – P. 470–461.
DOI: 10.1056/NEJMoa1100356.
12. Comparison of the short-term survival benefit associated with revascularization compared with medical therapy in patients with no prior coronary artery disease undergoing stress myocardial perfusion ingle photonemission computed omography / [R. Hachamovitch, S. W. Hayes, J. D. Friedman] // Circulation. – 2003. – № 107. – P. 2900–2907. DOI: 10.1161/01.CIR.0000072790.23090.41.
13. Lawson A.E. Inferences of clinical diagnostic reasoning and diagnostic error / A. E. Lawson, E. S. Daniel // Journal of Biomedical Informatics. – 2011. – № 44. – P. 402–412. DOI: 10.1016/j.jbi.2010.01.003.
14. Berner E. S. Overconfidence as a cause of diagnostic error in medicine / E. S. Berner, M. L. Graber // The American Journal of Medicine. – 2008. – № 121. – P. 2–23. DOI: 10.1016/j.amjmed.2008.01.001.
15. Croskerry P. Overconfidence in clinical decision making / P. Croskerry, G. Norman // The American Journal of Medicine. – 2008. – № 121. – P. 24–29.
DOI: 10.1016/j.amjmed.2008.02.001.
16. Ely J. W. Checklists to reduce diagnostic errors / J. W. Ely, M. L. Graber, P. Croskerry // Academic Medicin. – 2011. – № 86. – P. 307–313. DOI:
10.1097/ACM.0b013e31820824cd.
17. Diagnostic error in medicine / [G. D. Schiff, O. Hasan, S. Kim et al.] // Archives of Internal Medicine. – 2009. – № 169. – P. 1881–1887.
DOI: 10.1001/archinternmed.2009.333.
18. Diagnostic reasoning strategies and diagnostic success / [S. Coderre, H. Mandin, P. H. Harasym et al.] // Medical Education Journal. – 2003. – №37. – P. 695 – 703.
19. Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them / P. Croskerry // Academic Medicin. – 2003. – № 78. – P. 775–780.
20. Impact of a Computer-Based Diagnostic Decision Support Tool on the Differential Diagnoses of Medicine Residents / [J. Mitchell, M. D. Feldman, P. Edward et al.] // The Journal of Graduate Medical Education. – 2012. – № 4. – P. 227–231. DOI: 10.4300/JGME-D-11-00180.1.
21. Miller R. A. Computer-assisted diagnostic decision support:history, challenges, and possible paths forward / R. A. Miller
// Advances in Health Sciences Education. – 2009. – №14. – P. 89–106. DOI: 10.1007/s10459-009-9186-y.
22. A computerized handheld decision-support system to improve pulmonary embolism diagnosis / [P. M. Roy, P. Durieux, F. Gillaizeau et al.] // Annals of Internal Medicine. – 2009. – №151. – P. 677–686. DOI: 10.7326/0003-4819-151-10-200911170-00003.
23. Screening for Cardiovascular Disease Risk With Electrocardiography: US Preventive Services Task Force Recommendation
Statement / [S. J. Curry, A. H. Krist, D. K. Owens et al.] // JAMA. – 2018. – № 319. – P. 2308–2314. DOI: 10.1001/jama.2018.6848.
24. Bhardwaj R. Chest pain, dynamic ECG changes and coronary artery disease / R. Bhardwaj // The Journal of the Association of Physicians of India. – 2007. – №55. – P. 556–559.
25. Alpaydin E. Introduction to Machine Learning / E. Alpaydin. – London : The MIT Press, 2010. – 640 p.






Copyright (c) 2019 A. V. Zharkova, V. V. Nahornyi

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Address of the journal editorial office:
Editorial office of the journal «Radio Electronics, Computer Science, Control»,
National University "Zaporizhzhia Polytechnic", 
Zhukovskogo street, 64, Zaporizhzhia, 69063, Ukraine. 
Telephone: +38-061-769-82-96 – the Editing and Publishing Department.
E-mail: rvv@zntu.edu.ua

The reference to the journal is obligatory in the cases of complete or partial use of its materials.