no code implementations • 26 Aug 2023 • Kian Wei Ng, Yujia Gao, Shaheryar Mohammed Furqan, Zachery Yeo, Joel Lau, Kee Yuan Ngiam, Eng Tat Khoo
The effective usage of conventional 2D US for interventional guidance requires extensive experience to project the image plane onto the patient, and the interpretation of images in diagnostics suffers from high intra- and inter-user variability.
no code implementations • 17 Oct 2020 • Zhaojing Luo, Sai Ho Yeung, Meihui Zhang, Kaiping Zheng, Lei Zhu, Gang Chen, Feiyi Fan, Qian Lin, Kee Yuan Ngiam, Beng Chin Ooi
In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask.
no code implementations • 24 Mar 2020 • Kaiping Zheng, Shaofeng Cai, Horng Ruey Chua, Wei Wang, Kee Yuan Ngiam, Beng Chin Ooi
In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions.
no code implementations • WS 2019 • Steven Kester Yuwono, Hwee Tou Ng, Kee Yuan Ngiam
The objective of this work is to develop an automated diagnosis system that is able to predict the probability of appendicitis given a free-text emergency department (ED) note and additional structured information (e. g., lab test results).
no code implementations • 22 Jul 2019 • Si-Qi Liu, Kee Yuan Ngiam, Mengling Feng
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care.
no code implementations • 14 Jan 2019 • Ziyuan Pan, Hao Du, Kee Yuan Ngiam, Fei Wang, Ping Shum, Mengling Feng
Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.
1 code implementation • 6 Jun 2018 • Xiangrui Cai, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, Ying Zhang, Xiaojie Yuan
Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics.
no code implementations • WS 2016 • Steven Kester Yuwono, Hwee Tou Ng, Kee Yuan Ngiam
Personal health information (PHI) (such as name and identification number) needs to be removed so that patients cannot be identified.