IJBTT

Smart Health Companion: An App for Tracking Diagnoses and Recommending Next Steps
	© 2024 by IJCTT Journal
	Volume-72 Issue-9
	Year of Publication : 2024
	Authors : Kabita Paul
	DOI :  10.14445/22312803/IJCTT-V72I9P107

How to Cite?

Kabita Paul, "Smart Health Companion: An App for Tracking Diagnoses and Recommending Next Steps," International Journal of Computer Trends and Technology, vol. 72, no. 9, pp. 44-47, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I9P107

Abstract
With the overflow of health information online and misinformation in social media, there is a need for a reliable source of consumable health information along with a platform to keep track of medical records. This research project aims to bring a data science approach to the public health and health literacy domain to guide users with reliable health information and enable them to track their medical records. This project aims to build a web-based and mobile dashboard that enables users to enter their medical records like diagnosis values, create to-do lists and reminders, track health progress, and feed medical prescriptions. This project of building customized interactive tools is multi-disciplinary and collaborates with departments like Computer Science, Psychology, Healthcare, and Communication. The results of this research are expected to be a guide for further application development. Moreover, the analysis should be able to help and guide the preparation of user testing questionnaires and focus group/ survey questions for further data collection.

Keywords
Recommendation engine, Health informatics, Health tracker, Medical diagnosis, Health recommender.

Reference

[1] Andre Calero Valdez et al., Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives, Machine Learning for Health Informatics, Springer, Cham, pp. 391-141, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Timothy Bickmore et al., Safety First: Conversational Agents for Health Care, Studies in Conversational UX Design, Springer, Cham, pp. 33-57, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Ming Xia et al., “Effect of Intravenous Lidocaine on Chronic Postoperative Pain in Patients Undergoing Breast Cancer Surgery: A Prospective, Double-blind, Randomized, Placebo-controlled Clinical Trial,” Annals of Translational Medicine, vol. 10, no. 14, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Kim Theodos, and Scott Sittig, “Health Information Privacy Laws in the Digital Age: HIPAA Doesn't Apply,” Online Research Journal Perspectives in Health Information Management, vol. 18, 2020.
[Google Scholar] [Publisher Link]
[5] Naresh Kasoju et al., “Digital Health: Trends, Opportunities and Challenges in Medical Devices, Pharma and Bio-Technology,” CSI Transactions on ICT, vol. 11, pp. 11–30, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Salman Bin Naeem, and Maged N. Kamel Boulos, “COVID-19 Misinformation Online and Health Literacy: A Brief Overview,” International Journal of Environmental Research and Public Health, vol. 18, no. 15, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] NEJM Catalyst, Healthcare Big Data and the Promise of Value-Based Care, NEJM Catalyst, 2018. [Online]. Available: https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0290
[8] Kirk D. Borne, and Booz Allen, Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples, kdnuggets. [Online]. Available: https://www.kdnuggets.com/2019/01/data-scientist-dilemma-cold-start-machine-learning.html
[9] Effect of Perioperative Intravenous Lidocaine Infusion and Magnesium Infusion on the Functional Recovery After General Anesthesia in the Patients Undergoing Breast Mastectomy, Get well soon, 2022. [Online]. Available: http://getwellsoon.ai/trials/NCT02185859/
[10] Data Science Research: Personal Health Libraries for Consumers and Patients (R01 Clinical Trial Optional), National Institutes of Health Grants. [Online]. Available: https://grants.nih.gov/grants/guide/pa-files/PAR-19-072.html