Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment ...
Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of em...
Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole...
OBJECTIVES: This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).
BMC medical informatics and decision making
Nov 2, 2021
BACKGROUND: Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it's hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: ...
BACKGROUND: With the development of minimally invasive surgery technology, patients with bladder cancer are increasingly receiving laparoscopic radical cystectomy (LRC) or robotic-assisted radical cystectomy (RARC) treatment. The main purpose of this...
BACKGROUND: Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is pr...
Robotic devices with soft actuators have been developed to realize the effective rehabilitation of patients with motor paralysis by enabling soft and safe interaction. However, the control of such robots is challenging, especially owing to the differ...
Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenita...
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