We here propose a new method of combining a mathematical model that describes a chemotherapy treatment for breast cancer with a machine-learning (ML) algorithm to increase performance in predicting tumor size using a five-step procedure. The first st...
Despite increasing interest in how artificial intelligence (AI) can augment and improve healthcare delivery, the development of new AI models continues to outpace adoption in existing healthcare processes. Integration is difficult because current app...
Advances in 3D imaging technology are transforming how radiologists search for cancer and how security officers scrutinize baggage for dangerous objects. These new 3D technologies often improve search over 2D images but vastly increase the image data...
A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML...
Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a prediction model for NCI among people with HIV using clinical- and magnetic resonance imaging (MRI)-derived f...
Postoperative acute urinary retention (pAUR) is a known occurrence after robot-assisted laparoscopic ureteral reimplantation via an extravesical approach (RALUR-EV). We hypothesized that the risk factor of pAUR after RALUR-EV might be similar to tha...
In this research, we focused on Human-Robot collaboration. There were two goals: (1) to develop and evaluate a real-time Human-Robot collaborative system, and (2) to achieve concrete tasks such as collaborative peg-in-hole using the developed system....
Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assista...
International journal of molecular sciences
Jan 19, 2021
Accurately identifying protein-ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein-AT...
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