Prediction of vancomycin initial dosage using artificial intelligence models applying ensemble strategy.

Background: Antibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow. Therefore, clinical dosing decisions must be made with the utmost care; such decisions are said to be "suitable" only when both "efficacy" and "safety" are considered.

An AI-powered Electronic Nose System with Fingerprint Extraction for Aroma Recognition of Coffee Beans.

Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions.

Non-destructive acoustic screening of pineapple ripeness by unsupervised machine learning and Wavelet Kernel methods.

In a pineapple exporting factory, manual lines are usually built to screen fruits of non-ripen hitting sounds from millions of undecided fruits for long-haul transportation. However, human workers cannot concentratedly listen and make consistent judgments over long hours. Pineapple screening becomes arbitrary after approximately an hour.

Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation.

Background: Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke.

Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method.

Background: Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs.

Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method.

Background: To classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images.

Results: A convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy.

An Ensemble Model With Clustering Assumption for Warfarin Dose Prediction in Chinese Patients.

The prediction of daily stable warfarin dosage for a specific patient is difficult. To improve the predictive accuracy and to build a highly accurate predictive model, we developed an ensemble learning method, called evolutionary fuzzy c-mean (EFCM) clustering algorithm with support vector regression (SVR). A dataset of 517 Han Chinese patients was collected from the data of The First Affiliated Hospital of Soochow University and dataset of International Warfarin Pharmacogenetics Consortium for training and testing.

Retrieving hidden atrial repolarization waves from standard surface ECGs.

Background: This study estimates atrial repolarization activities (Ta waves), which are typically hidden most of the time from body surface electrocardiography when diagnosing cardiovascular diseases. The morphology of Ta waves has been proven to be an important marker for the early sign of inferior injury, such as acute atrial infarction, or arrhythmia, such as atrial fibrillation. However, Ta waves are usually unseen except during conduction system malfunction, such as long QT interval or atrioventricular block.

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices.

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak.

Evolutionary Ensemble Learning Algorithm to Modeling of Warfarin Dose Prediction for Chinese.

An evolutionary ensemble modeling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and the genetic algorithm optimizes the parameters of the GP. The EEM model is assembled by using the prepared base models through a technique called "bagging.

Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury.

Background: Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision.

Objective: The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment.

Continued use of an interactive computer game-based visual perception learning system in children with developmental delay.

This study developed an interactive computer game-based visual perception learning system for special education children with developmental delay. To investigate whether perceived interactivity affects continued use of the system, this study developed a theoretical model of the process in which learners decide whether to continue using an interactive computer game-based visual perception learning system. The technology acceptance model, which considers perceived ease of use, perceived usefulness, and perceived playfulness, was extended by integrating perceived interaction (i.