On the 20 to 22 February 2025, Professor Dr Rafdzah Ahmad Zaki represented Universiti Malaya at the Malaysia Technology (MTE) 2025, held at the Putra World Trade Centre in Kuala Lumpur. Her innovative project, DeSProg System: AI-Powered Dengue Severity Prognostication, earned a prestigious Silver Award in the International Innovation Awards category for Healthcare, Personal Care Technology, Biotechnology, and Life Sciences.
The booth showcasing her project received notable visits from YB Dato’ Mohammad Yusof bin Apdal, Deputy Minister of Science, Technology, and Innovation, and Professor Dato’ Seri Ir Dr Noor Azuan Abu Osman, Vice Chancellor of Universiti Malaya.
DeSProg booth at the Malaysia Technology Expo2025 (MTE 2025):
About DeSProg (IP No: PI2024004586)
The DeSProg (Dengue Severity Prognostication) System is a machine learning-based risk scoring system that predicts the risk of hospitalised dengue patients progressing to severe dengue, and it has been tested in the clinical setting.
The DeSProg prototype integrates an electronic clerking form, real-time prediction tools, and a comprehensive dashboard. During clinical testing, the system demonstrated 91% accuracy, 93% specificity, and a negative predictive value of 97%. This high specificity helps identify low-risk patients, reducing unnecessary interventions, optimizing resource allocation, and streamlining patient management.
DeSProg System Dashboard has a total of four visualization modules displaying:
- Summary of Dengue Cases: view the cumulative and the selected time interval of dengue-related cases.
- Dengue Trend: display either weekly or monthly trends of the dengue cases.
- Overall Patients’ Information: displays the dengue diagnosis, patients’ demographic distribution, as well as their past medical history.
- Patient Health Summary: provides a detailed overview of the patient’s medical history and clinical assessments throughout their hospitalization, presented in a concise time-series format.
Beyond immediate benefits, DeSProg facilitates continuous data collection, creating a robust database for further research and model refinement. Its scalable framework holds immense potential for broader applications in other high-burden dengue regions and infectious diseases.
DeSProg team:
Project Leader: Prof Dr Rafdzah Ahmad Zaki
Members: Prof Dr Sanjay Rampal, A/Prof Dr Sharifah Faridah, Prof Dr Lucy Lum, Prof Dr Chan Chee Seng, A/Prof Dr Norimichi Hirahara, Dr Wong Pui Li, Dr Anjanna Kukreja, Dr Hoo Wai Lam, Dr Saw Shier Nee. DeSProg project is supported by: IMPACT-ORIENTED INTERDISCIPLINARY RESEARCH GRANT (IIRG002C-2020HWB) Improving Dengue Severity Prognostication using Artificial Intelligence.
Watch the DeSProg short video.
More about the DeSProg project.
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