Determination of the informational content of symptoms in the dynamic processes of assessing the patient’s condition in e-health

Keywords: clinical signs, information technologies, mobile medicine, digital pathology, patient care


The study is devoted to substantiating the tactics of choosing the signs of the patient's condition for diagnostic decision-making on corrective medical intervention in mobile medicine.

The aim of the research: to study a creation of a methodology for determining the integral informativeness of the patient's symptoms during remote monitoring of his condition.

Materials and methods: this article is based on search results in PubMed, Scopus, MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA articles published between January 1991 and January 2021 and containing the search terms “information technology”, “Mobile medicine”, “digital pathology” and “deep learning”, as well as the results of the authors' own research. The authors independently extracted data on concealment of distribution, consistency of distribution, blindness, completeness of follow-up, and interventions.

Results: concluded that to determine the Informativeness of symptoms in mobile monitoring of patients, it is possible to use risk indicators of predicted conditions as a universal method. Given that the Informativeness of the patient's condition changes constantly, for online diagnosis of conditions during remote monitoring of the patient it is recommended to use the function of informative symptoms from time to time and use a set of approaches to assess the Informativeness of patient symptoms. It is proposed to use the strategy of diagnosis and treatment using probabilistic algorithms based on the values of the risk of complications of the pathological process, as well as the formulas of Kulbach and Shannon to determine individual trends in the pathological patient process.

Conclusion: there was proposed to use risk indicators of predicted conditions as a universal method for determining the informational content of symptoms in mobile monitoring of patients.


Download data is not yet available.

Author Biography

Yaroslav Shevchenko, Shupyk National Healthcare University of Ukraine

Department of Medical Informatics


Boyce, B. (2017). An update on the validation of whole slide imaging systems following FDA approval of a system for a routine pathology diagnostic service in the United States. Biotechnic & Histochemistry, 92 (6), 381–389. doi:

Akmandor, A. O., Jha, N. K. (2017). Keep the Stress Away with SoDA: Stress Detection and Alleviation System. IEEE Transactions on Multi-Scale Computing Systems, 3 (4), 269–282. doi:

Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P. et. al. (2013). The Effectiveness of Mobile-Health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta-Analysis. PLoS Medicine, 10 (1), e1001363. doi:

Denis, F., Basch, E., Septans, A.-L., Bennouna, J., Urban, T., Dueck, A. C., Letellier, C. (2019). Two-Year Survival Comparing Web-Based Symptom Monitoring vs Routine Surveillance Following Treatment for Lung Cancer. JAMA, 321 (3), 306–307. doi:

Gagnon, M.-P., Ngangue, P., Payne-Gagnon, J., Desmartis, M. (2015). m-Health adoption by healthcare professionals: a systematic review. Journal of the American Medical Informatics Association, 23 (1), 212–220. doi:

Hajian, T. K. (2012) Methodological issues of confounding in analytical epidemiologic studies. Caspian Journal of Internal Medicine, 3 (3), 488–495.

Jabour, A. M., Rehman, W., Idrees, S., Thanganadar, H., Hira, K., Alarifi, M. A. (2021). The Adoption of Mobile Health Applications Among University Students in Health Colleges. Journal of Multidisciplinary Healthcare, 14, 1267–1273. doi:

Kapustina, S. V., Kiryakova, O. V., Kapustina, A. V., Lapina, L. A., Stupina, A. A. (2015). The choice of informative features for assessing the severity of the disease. Modern problems of science and education, 2 (2), 55.

Engel, H., Huang, J. J., Tsao, C. K., Lin, C.-Y., Chou, P.-Y., Brey, E. M. et. al. (2011). Remote real-time monitoring of free flaps via smartphone photography and 3G wireless internet: A prospective study evidencing diagnostic accuracy. Microsurgery, 31 (8), 589–595. doi:

Hajat, C. (2010). An Introduction to Epidemiology. Genetic Epidemiology, 27–39. doi:

Kho, A., Henderson, L. E., Dressler, D. D., Kripalani, S. (2006). Use of handheld computers in medical education: A systematic review. Journal of General Internal Medicine, 21 (5), 531–537. doi:

Kachmar, V. O., Avramenko, V. I. (2011). The trends of the informational technologies’ development in medicine. Transport Medicine of Ukraine, 3, 96–103.

Griffin, J., Treanor, D. (2017). Digital pathology in clinical use: where are we now and what is holding us back? Histopathology, 70 (1), 134–145. doi:

Kamel Boulos, M. N., Wilson, J. T., Clauson, K. A. (2018). Geospatial blockchain: promises, challenges, and scenarios in health and healthcare. International Journal of Health Geographics, 17 (1). doi:

Geller, N. L., Kim, D.-Y., Tian, X. (2016). Smart Technology in Lung Disease Clinical Trials. Chest, 149 (1), 22–26. doi:

Florczak, B., Scheurich, A., Croghan, J., Sheridan, P. Jr., Kurtz, D., McGill, W., McClain, B. (2012). An Observational Study to Assess an Electronic Point-of – Care Wound Documentation and Reporting System Regarding User Satisfaction and Potential for Improved Care. Ostomy Wound Manage, 58, 46–51.

Bakkar, N., Kovalik, T., Lorenzini, I., Spangler, S., Lacoste, A., Sponaugle, K. et. al. (2017). Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathologica, 135 (2), 227–247. doi:

Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., Leung, V. C. M. (2010). Body Area Networks: A Survey. Mobile Networks and Applications, 16 (2), 171–193. doi:

Dhar, J., Ranganathan, A. (2015). Machine learning capabilities in medical diagnosis applications: computational results for hepatitis disease. International Journal of Biomedical Engineering and Technology, 17 (4), 330–340. doi:

Gong, F. F., Sun, X. Z., Lin, J., Gu, X. D. (2013). Primary exploration in establishment of China's intelligent medical treatment. Mod. Hos. Manag, 11 (2).

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542 (7639), 115–118. doi:

Hsieh, C.-H., Tsai, H.-H., Yin, J.-W., Chen, C.-Y., Yang, J. C.-S., Jeng, S.-F. (2004). Teleconsultation with the Mobile Camera-Phone in Digital Soft-Tissue Injury: A Feasibility Study. Plastic and Reconstructive Surgery, 114 (7), 1776–1782. doi:

Dunn, J., Runge, R., Snyder, M. (2018). Wearables and the medical revolution. Personalized Medicine, 15 (5), 429–448. doi:

Eysenbach, G., Diepgen, T. L. (2001). The role of e-health and consumer health informatics for evidence-based patient choice in the 21st century. Clinics in Dermatology, 19 (1), 11–17. doi:

Basch, E., Deal, A. M., Dueck, A. C., Scher, H. I., Kris, M. G., Hudis, C., Schrag, D. (2017). Overall Survival Results of a Trial Assessing Patient-Reported Outcomes for Symptom Monitoring During Routine Cancer Treatment. JAMA, 318 (2), 197–198. doi:

Pantanowitz, L., Guo, H., Birsa, J., Farahani, N., Hartman, D., Piccoli, A. et. al. (2016). Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out. Journal of Pathology Informatics, 7 (1), 23. doi:

Hande, A., Polk, T., Walker, W., Bhatia, D. (2006). Self-Powered Wireless Sensor Networks for Remote Patient Monitoring in Hospitals. Sensors, 6 (9), 1102–1117. doi:

Greenland, S., Morgenstern, H. (2001). Confounding in Health Research. Annual Review of Public Health, 22 (1), 189–212. doi:

Buratti, C., Conti, A., Dardari, D., Verdone, R. (2009). An Overview on Wireless Sensor Networks Technology and Evolution. Sensors, 9 (9), 6869–6896. doi:

Demirkan, H. (2013). A Smart Healthcare Systems Framework. IT Professional, 15 (5), 38–45. doi:

Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., Mankodiya, K. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, 78 (2), 659–676. doi:

Álvarez López, Y., Franssen, J., Álvarez Narciandi, G., Pagnozzi, J., González-Pinto Arrillaga, I., Las-Heras Andrés, F. (2018). RFID Technology for Management and Tracking: e-Health Applications. Sensors, 18 (8), 2663. doi:

Andreu-Perez, J., Leff, D. R., Ip, H. M. D., Yang, G.-Z. (2015). From Wearable Sensors to Smart Implants–Toward Pervasive and Personalized Healthcare. IEEE Transactions on Biomedical Engineering, 62 (12), 2750–2762. doi:

Garvin, W. (2012). The Legal Perspective of mHealth in the United States. Journal of Mobile Technology in Medicine, 1 (4), 42–45. doi:

Estrin, D., Sim, I. (2010). Open mHealth Architecture: An Engine for Health Care Innovation. Science, 330 (6005), 759–760. doi:

Willard-Grace, R., DeVore, D., Chen, E. H., Hessler, D., Bodenheimer, T., Thom, D. H. (2013). The effectiveness of medical assistant health coaching for low-income patients with uncontrolled diabetes, hypertension, and hyperlipidemia: protocol for a randomized controlled trial and baseline characteristics of the study population. BMC Family Practice, 14 (1). doi:

Janes, H., Pepe, M. S. (2008). Adjusting for Covariates in Studies of Diagnostic, Screening, or Prognostic Markers: An Old Concept in a New Setting. American Journal of Epidemiology, 168 (1), 89–97. doi:

Denis, F., Yossi, S., Septans, A.-L., Charron, A., Voog, E., Dupuis, O. et. al. (2017). Improving Survival in Patients Treated for a Lung Cancer Using Self-Evaluated Symptoms Reported Through a Web Application. American Journal of Clinical Oncology, 40 (5), 464–469. doi:

Ben Elhadj, H., Chaari, L., Kamoun, L. (2012). A Survey of Routing Protocols in Wireless Body Area Networks for Healthcare Applications. International Journal of E-Health and Medical Communications, 3 (2), 1–18. doi:

Fernandez-Lopez, H., Afonso, J. A., Correia, J. H., Simoes, R. (2014). Remote Patient Monitoring Based on ZigBee: Lessons from a Real-World Deployment. Telemedicine and e-Health, 20 (1), 47–54. doi:

Blair, A., Stewart, P., Lubin, J. H., Forastiere, F. (2007). Methodological issues regarding confounding and exposure misclassification in epidemiological studies of occupational exposures. American Journal of Industrial Medicine, 50 (3), 199–207. doi:

Austin, P. C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46 (3), 399–424. doi:

Barakah, D. M., Ahmad-Uddin, M. (2012). A survey of challenges and applications of wireless body area network (WBAN) and role of a virtual doctor server in existing architecture. 3rd International Conference on Intelligent Systems Modelling and Simulation. Piscataway: IEEE, 214–219. doi:

Car, J., Gurol-Urganci, I., de Jongh, T., Vodopivec-Jamsek, V., Atun, R. (2012). Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database of Systematic Reviews, 7, CD007458. doi:

Chen, Q., Lu, Y. (2018). Construction and application effect evaluation of integrated management platform of intelligent hospital based on big data analysis. China Medical Herald, 15 (35), 161–164.

Chakraborty, C., Gupta, B., Ghosh, S. K. (2013). A Review on Telemedicine-Based WBAN Framework for Patient Monitoring. Telemedicine and e-Health, 19 (8), 619–626. doi:

Curtis, D. W., Pino, E. J., Bailey, J. M., Shih, E. I., Waterman, J., Vinterbo, S. A. et. al. (2008). SMART–An Integrated Wireless System for Monitoring Unattended Patients. Journal of the American Medical Informatics Association, 15 (1), 44–53. doi:

Ali, M., Saif, U., Dunkels, A., Voigt, T., Römer, K., Langendoen, K. et. al. (2006). Medium access control issues in sensor networks. ACM SIGCOMM Computer Communication Review, 36 (2), 33–36. doi:

Bastawrous, A., Leak, C., Howard, F., Kumar, V. (2012). Validation of Near Eye Tool for Refractive Assessment (NETRA) – Pilot Study. Journal of Mobile Technology in Medicine, 1 (3), 6–16. doi:

Chih-Chung Huang, Po-Yang Lee, Pay-Yu Chen, Ting-Yu Liu. (2012). Design and implementation of a smartphone-based portable ultrasound pulsed-wave doppler device for blood flow measurement. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 59 (1), 182–188. doi:

Krishna, S., Boren, S. A., Balas, E. A. (2009). Healthcare via Cell Phones: A Systematic Review. Telemedicine and e-Health, 15 (3), 231–240. doi:

Mechael, P. N., Batavia, H., Kaonga, N., Searle, S., Kwan, A. et. al. (2010). Barriers and Gaps Affecting mHealth in Low and Middle Income Countries: Policy White Paper. Center for Global Health and Economic Development, Earth Institute, Columbia University, 79.

Zurovac, D., Sudoi, R. K., Akhwale, W. S., Ndiritu, M., Hamer, D. H., et. al. (2011). The effect of mobile phone text-message reminders on Kenyan health workers' adherence to malaria treatment guidelines: a cluster randomised trial. Lancet, 378, 795–803. doi:

Lindquist, A. M., Johansson, P. E., Petersson, G. I., Saveman, B.-I., Nilsson, G. C. (2008). The Use of the Personal Digital Assistant (PDA) Among Personnel and Students in Health Care: A Review. Journal of Medical Internet Research, 10 (4), e31. doi:

Boulos, M., Wheeler, S., Tavares, C., Jones, R. (2011). How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. BioMedical Engineering OnLine, 10 (1), 24. doi:

👁 44
⬇ 27
How to Cite
Shevchenko, Y. (2021). Determination of the informational content of symptoms in the dynamic processes of assessing the patient’s condition in e-health. EUREKA: Health Sciences, (5), 47-60.
Medicine and Dentistry