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EPICS Project

DSN3099


Project: Patient Risk Profiling and Care Management Tool

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Brief

Brief about the project


Task List

  • Research and Development
  • Initial Work
  • Source Dataset
  • AI and ML Models
  • Create App (Ongoing)
  • Create Website (Ongoing)
  • Implementing Core Features (Ongoing)
  • Additional Features

Aim

  • To make a tool that identifies the health status of a participant
  • To provide stratification of a person’s health status.
  • To allow a local multidisciplinary approach to identify those who are seriously ill, or at immediate risk of a hospital admission
  • To provide a platform for professionals to coordinate the care of the participant
  • To provide remote active care management and diagnosis

Use

  • At least a quarterly risk profiling of the participant to identify those who are predicted to become or at significant risk of emergency hospital admission.
  • Actively and passively measure the participants’ health status via various factors such as weight, hydration, activity stress, etc...
  • Keep a holistic record of the participant's health records, previous conditions, known hereditary conditions, etc...
  • Keep a record of the participants’ current trends such as activity or recovery.
  • Shared and Integrated approach to care management to improve quality of care and reduce individual risk of emergency hospital admission

Role

In prevention and health promotion interventions, screening methods, and risk profile assessments, are often used as methods for establishing the interventions’ effectiveness, for the selection and determination of the health status of participants. This tool is meant to do the same in a way such that it is accessible to all via a gamified approach


Expected Result

Improved quality of care and life and fewer avoidable emergency admissions to hospital


Effects

Screening methods and health risk assessments create effects as they objectify health risks and/or the health status of individuals, i.e. they select the individuals' ‘at risk’ and indicate the lifestyle modifications these people are required to make to improve their health


Pitfalls

Reduce the group of participants thereby decreasing the possible effect of interventions, as they provide legitimizations for people to make choices, whether they enroll or not, and what changes they incorporate into their lives. i.e. They provide a space of interaction, in which agency is distributed to a professional, the participants, and the test (Risk profiling and screening).


Result

Decisions are not made just upon the projection of the outcome of these instruments, they were made by both the participants and the professionals and were based on their opinions on these outcomes.


Conclusion

Advancements in scientific research have yielded a deeper understanding of health determinants, behaviors, risk factors, and their prevalence across different populations. While this has empowered individuals with greater access to health knowledge, it has also created a situation where some may struggle to discern reliable information, particularly regarding urgent medical situations. This highlights the need for trustworthy resources that can alleviate confusion and provide accurate guidance.

The proposed work directly addresses this challenge by offering a tool that serves two key purposes. First, it acts as a reliable source of health information, mitigating the confusion caused by the abundance of online resources. Second, the tool facilitates the identification of health risks, enabling a more efficient and targeted allocation of resources within the healthcare system.


References

  1. Klasnja, P., Pratt, W., & Paasche-Orlow, M. K. (2017). Mobile health apps for promoting healthy behaviors: A systematic review and meta-analysis. Journal of medical Internet research, 19(3), e83.
  2. Mitesh, A., & Singh, S. K. (2019). Mobile health apps for early detection and prevention of non-communicable diseases: A narrative review. Journal of Clinical and Diagnostic Research, 13(11), FE01-FE05.
  3. Ventola, C. L. (2014). Mobile apps for the healthcare professional: Benefits, risks, and a framework for evaluation. Pharmacy and Therapeutics, 39(4), 280-287.
  4. Daniel Johnson, Sebastian Deterding, Kerri-Ann Kuhn, Aleksandra Staneva, Stoyan Stoyanov, Leanne Hides, Gamification for health and wellbeing: A systematic review of the literature, Internet Interventions, ISSN 2214-7829
  5. Pereira, P., Duarte, E., Rebelo, F., Noriega, P. (2014). A Review of Gamification for Health-Related Contexts. In: Marcus, A. (eds) Design, User Experience, and Usability. User Experience Design for Diverse Interaction Platforms and Environments. DUXU 2014.
  6. King D, Greaves F, Exeter C, Darzi A. ‘Gamification’: Influencing health behaviours with games. Journal of the Royal Society of Medicine. 2013;106(3):76-78
  7. van Gaalen, A.E.J., Brouwer, J., Schönrock-Adema, J. et al. Gamification of health professions education: a systematic review. Adv in Health Sci Educ 26, 683–711 (2021).
  8. Sean A. Munson, Erika Poole, Daniel B. Perry, Tamara Peyton, 2015. "Gamification and Health", The Gameful World: Approaches, Issues, Applications, Steffen P. Walz, Sebastian Deterding
  9. Muangsrinoon, S.; Boonbrahm, P. Game elements from literature review of gamification in healthcare context. "JOTSE: Journal of Technology and Science Education", Febrer 2019, vol. 9, núm. 1, p. 20-31.
  10. Lamyae Sardi, Ali Idri, José Luis Fernández-Alemán, A systematic review of gamification in e-Health,Journal of Biomedical Informatics,Volume 71,2017,Pages 31-48,ISSN 1532-0464.
  11. Tracy MC, Okorie CUA, Foley EA, Moss RB: Allergic Bronchopulmonary Aspergillosis. Journal of fungi 2016, 2(2).
  12. Huang, C., Leng, D., Li, L., Zheng, P., Sun, B., & Zhang, X. D. (2018). Transcriptome analysis of human peripheral blood reveals key circRNAs implicated in Allergic bronchopulmonary aspergillosis.
  13. Firdaus Abdullah, M., Noraini Sulaiman, S., Khusairi Osman, M., Karim, N. K. A., Lutfi Shuaib, I., & Danial Irfan Alhamdu, M. (2020). Classification of Lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours.
  14. Riemann, D., Cwikowski, M., Turzer, S., Giese, T., Grallert, M., Schütte, W., & Seliger, B. (2018). Blood immune cell biomarkers in lung cancer.
  15. Girard, N., Lou, E., Azzoli, C. G., Reddy, R., Robson, M., Harlan, M., … Pao, W. (2010). Analysis of Genetic Variants in Never-Smokers with Lung Cancer Facilitated by an Internet-Based Blood Collection Protocol: A Preliminary Report.Uu
  16. Liu, B., Li, Y., Ghosh, S., Sun, Z., Ng, K., & Hu, J. (2019). Complication Risk Profiling in Diabetes Care: A Bayesian Multi-Task and Feature Relationship Learning Approach.
  17. Shi, X., Hu, Y., Zhang, Y., Li, W., Hao, Y., Alelaiwi, A., … Shamim Hossain, M. (2016). Multiple Disease Risk Assessment With Uniform Model Based on Medical Clinical Notes.
  18. N. Y. Philip, M. Razaak, J. Chang, S. M, M. O’Kane and B. K. Pierscionek, "A Data Analytics Suite for Exploratory Predictive, and Visual Analysis of Type 2 Diabetes"
  19. Grover, A., Kalani, A., & Dubey, S. K. (2020). Analytical Approach towards Prediction of Diseases Using Machine Learning Algorithms.
  20. Rahman, T. M., Siddiqua, S., Rabby, S. E., Hasan, N., & Imam, M. H. (2019). Early Detection of Kidney Disease Using ECG Signals Through Machine Learning Based Modelling.
  21. Shailaja, K., Seetharamulu, B., & Jabbar, M. A. (2018). Machine Learning in Healthcare: A Review.
  22. Boonchieng, E., & Duangchaemkarn, K. (2016). Digital disease detection: Application of machine learning in community health informatics.
  23. Roy, A. P., Chatterjee, S., Maji, P., & Mondal, H. K. (2020). Classification of ECG Signals for IoT-based Smart Healthcare Applications using WBAN.
  24. Kaul, C., Kaul, A., & Verma, S. (2015). Comparitive study on healthcare prediction systems using big data.
  25. Flynn, J. T., Mitsnefes, M., Pierce, C., Cole, S. R., Parekh, R. S., … Furth, S. L. (2008). Blood Pressure in Children With Chronic Kidney Disease: A Report From the Chronic Kidney Disease in Children Study.
  26. Malik, S., Kanwal, N., Asghar, M. N., Sadiq, M. A. A., Karamat, I., & Fleury, M. (2019). Data Driven Approach for Eye Disease Classification with Machine Learning.
  27. Weiss, N., Miller, F., Cazaubon, S., & Couraud, P.-O. (2009). The blood-brain barrier in brain homeostasis and neurological diseases.
  28. Park, D. J., Park, M. W., Lee, H., Kim, Y.-J., Kim, Y., & Park, Y. H. (2021). Development of machine learning model for diagnostic disease prediction based on laboratory tests.
  29. Hiroki Kaneko, Hironobu Umakoshi, Masatoshi Ogata, Norio Wada (2021): Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test
  30. Stevens, T., Rosenberg, R., Aird, W., Quertermous, T., Johnson, F. L., Garcia, J. G. N., … Garfinkel, S. (2001). NHLBI workshop report: endothelial cell phenotypes in heart, lung, and blood diseases.

Links

WebApp
Architecture


Legend

|Professional | Doctors, Nurses, Paramedics, Trainers, Advisors| |Participant | User, Patient | |Instrument | Product, Tool |


Group

Group 33

  1. Ankit Sankar 21BCG10075
  2. Ashutosh Kumar Srivastava 21BAC10005
  3. Chetan Khoche 21BCG10100
  4. Devansh Trivedi 21BCE11407
  5. GPV Mruthunjai 21BCE11559
  6. Rishabh Pradhaan 21BCE11342
  7. Rishikesh M 21BCG10072
  8. Siddharth Dayal 21BCY10019
  9. Yashsh Sujithkumar Randive 21BCE11334

VIT Bhopal 2023-24


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EPICS Project DSN3099 VIT Bhopal 2023-24

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