To facilitate the development and early institution of targeted interventions to reduce the frequency of gout-related admissions, and potentially allow for the improvement of the care of these complex multimorbid patients, we examined the potential factors that predict the gout-related admissions and come up with a risk prediction model with 5 easy predictors.
Wang Han* and Wesley Yeung*, Angeline Tung, Joey Tay Ai Meng, Feng Mengling#, Shalini Arulanadam#
Our Name Entity Recognition tool for Emergency Medical Service Report (NEREMSR) is an online natural language processing system to identify the potential entities in the paramedics report for auditing purposes.
Mukkesh Kumar, Li Chen, Karen Tan, Li Ting Ang, Cindy Ho, Gerard Wong, Shu E Soh, Kok Hian Tan, Jerry Chan, Keith M Godfrey, Shiao-yng Chan, Mary Foong Fong Chong, John E Connolly, Yap Seng Chong, Johan G Eriksson^, Mengling Feng*^, Neerja Karnani*^
We have developed the first population-centric AI-based GDM risk prediction model for Asian women. The 4 features used in the non-invasive model can be easily measured and assessed during the first trimester (mean arterial blood pressure at booking appointment, maternal age, previous history of GDM and ethnicity). The non-invasive GDM prediction model can be an effective approach to screen and intervene early in women at risk, and also minimize the economic burden of universal testing and GDM associated healthcare in Asian populations.