Kuo Wei Chen
5 Papers
30 Citations
Kuo Wei Chen is an academic researcher. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 2 publications.
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Papers
Reinforcement learning strategies in cancer chemotherapy treatments: A review
TL;DR: In this article , the authors reviewed the emerging trends involved in forming a computational solution from the aspect of reinforcement learning, and highlighted the challenges, open questions, possible solutions, and future steps in inventing a realistic solution for the aforementioned problem.
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Factors affecting survival of medulloblastoma in children: the changing concept of management
Tai-Tong Wong,Yen Lin Liu,Donald Ming-Tak Ho,Kai Ping Chang,Muh Lii Liang,Hsin Hung Chen,Yi Yen Lee,Feng Chi Chang,Shih-Chieh Lin,Ting-Rong Hsu,Kuo Wei Chen,Wei Kang Kwang,Wu Yu Hou,Chung Yih Wang,Sang Hue Yen,Wan You Guo,Yi Wei Chen +16 more
TL;DR: The evolution of risk stratification and the correlated changing concept of management in the past years are traced to provide clues for age-specific and risk-adjusted optimal, effective, but beneficial and protective treatment strategies of these tumors in children.
19
Clinical considerations and surgical approaches for low-grade gliomas in deep hemispheric locations: thalamic lesions
Tai-Tong Wong,Hsin Hung Chen,Muh Lii Liang,Kevin Li Chun Hsieh,Yi Shan Yang,Donald Ming-Tak Ho,Kai Ping Chang,Yi Yen Lee,Shih-Chieh Lin,Ting-Rong Hsu,Yi Wei Chen,Sang Hue Yen,Feng Chi Chang,Wan You Guo,Kuo Wei Chen,Wei Kang Kwang,Wu Yu Hou,Chung Yih Wang +17 more
TL;DR: Thalamic LGGs are mainly LGAs and are indolent; radical (>90 %) resection was achieved better in PAs comparing with DAs in the present series.
16
Mathematical Lung Cancer Radiotherapy Model – Computational Simulation and Analysis
Wei-Fu Li,Chung-Yih Wang,Kuo Wei Chen,Hooman Samani,Changguo Yang +4 more
- 26 May 2022
TL;DR: In this paper , a mathematical tumor growth model derived from the classic Gompertz tumor model, and use appropriate parameters to obtain a radiotherapy model was fitted with a number of studies and clinical data through computer simulations, and analyzed the effects of certain doses and α/β values on the effect of radiotherapy.
Supervised Optimal Chemotherapy Regimen Based on Offline Reinforcement Learning
TL;DR: In this paper , a supervised optimal chemotherapy-dosing schedule for cancer patients was presented by using offline reinforcement learning, where the optimal policy suggested by the RL approach was supervised by incorporating previous treatment decisions of oncologists, which could add clinical expertise knowledge on algorithmic results.