Projects - Smarthealthcenter

Wastewater Epidemiological Surveillance in Vietnam

Principal Investigators & Key Members:

While the pandemic is global, transmission is local. Surveillance should be designed based on local context. Besides COVID-19, antimicrobial resistance (AMR) is a silent pandemic, and it only gets worse, as predicted by WHO. The identification of the sentinel sites and the frequency of sampling depends on the surveillance goals. Actionable public health goals in a crisis situation requires a different approach from those for endemic conditions.

Privacy-Preserving, Robust, and Explainable Federated Learning Framework for Healthcare System

Principal Investigators & Key Members:

Kok-Seng Wong | Bo Li | Khoa Doan

The project focuses on designing a trustworthy federated learning (FL) framework for the healthcare system with theoretical guarantees for its privacy, robustness, and agent-level data valuation and explainability, aiming to make the healthcare systems more efficient and trustworthy.

Developing a unified, low-cost, self-care mobile health application for common disease screening and early detection in low-and middle-income countries

Principal Investigators & Key Members:

This work aims to develop a low-cost, unified machine learning-based screening tool using multimodal signals collected from smartphones and wearable devices to evaluate the risk of presenting with common, high-demanding NCDs (stroke, chronic respiratory diseases, and neurodegenerative diseases) in low-and middle-income countries. This tool can be used at home or the point of care and opens up the opportunity to bring digital healthcare solutions to millions of people across countries.

Evaluating the Effect of Antiviral Drugs using Polarized Light Imaging and Machine Learning Approaches: The Case of Human-induced Pluripotent Stem Cell-derived Cardiomyocytes

Principal Investigators & Key Members:

In this project, we propose to develop a standard and robust procedure to evaluate the effectiveness of antiviral drugs using label-free, noninvasive light imaging, and machine learning-based approaches. To demonstrate this with a representative example, we will start with the evaluation of the effects of Molnupiravir, used for treating SAR-CoV- 2, on cardiomyocytes derived from a human-induced pluripotent stem cell. This project will then be extended to the evaluation of other antiviral drugs.

Detection and quantitation of cancer ctDNA and miRNA for point of care lung cancer therapy selection

Principal Investigators & Key Members:

Brian Cunningham, PhD | Xing Wang, PhD | Nguyen Xuan Hung | Yi Hyeon Gyu | Tran Thi Mai, PhD

This project focuses specifically on rigorously demonstrating the performance of a novel assay approach called “Activate, Cleave, Capture, and Count” (AC3) for ultra-sensitive detection and quantification of several well-known mutations with clinical relevance for guiding initial therapy selection. We aim to design, demonstrate, and validate AC3 assays for KRAS mutations in lung, colorectal, and pancreatic cancers.

Envisioning Urban Environments Resilient to Vector-Borne Diseases: A One Health Approach to Dengue Management

Principal Investigators & Key Members:

This project aims to develop a comprehensive modeling framework to predict the risk of Dengue infection in Vietnam. We create digital twins of the urban environment, which receive sensor data for factors influencing Dengue transmissions, such as temperature, humidity, and CO2 concentration. Our model will predict infection risk levels in real-time and make corresponding public health recommendations for risk reduction.