Fu-Yuan Cheng’s indexed work centers on the application of predictive modeling to clinical and organizational challenges. Their most prominent research involves the development of machine learning models to predict ICU transfers for hospitalized COVID-19 patients, a 2020 study that has received over 200 citations for its utility in pandemic resource management. Beyond acute clinical care, Cheng has explored diverse computational frameworks, including the implementation of a Springboot-based talent recruitment management system designed for small enterprises. Publicly indexed outputs suggest an additional interest in theoretical modeling, evidenced by senior-author contributions regarding variable fuzzy sets for artificial emotions prediction and the characterization of genetic risk factor interactions. The researcher’s broader contributions include significant middle-author roles in large-scale clinical validation studies. Cheng contributed to the development of MUST-Plus, a machine learning classifier for malnutrition screening in acute care facilities, which has been cited across multiple nutrition and dietetics journals. More recent outputs involve high-impact collaborations on real-time machine learning alerts to prevent escalation of care and multimodal risk stratification for delirium. These works, alongside studies on cyberattack behavior detection using LSTM networks, reflect a consistent research arc focused on deploying automated classification tools to improve decision-making in complex, high-stakes environments ranging from hospital wards to digital security systems.
Fu-Yuan Cheng, Himanshu Joshi, Pranai Tandon, Robert Freeman, David L Reich, Madhu Mazumdar, Roopa Kohli-Seth, Matthew Levin, Prem Timsina, Arash Kia
Veysel Kocaman, Fu-Yuan Cheng, Julio Bonis, Ganesh Raut, Prem Timsina, David Talby, Arash Kia
Veysel Kocaman, Fu-Yuan Cheng, Julio Bonis, Ganesh Raut, Prem Timsina, David Talby, Arash Kia
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