This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advant...
Predictive models for the occurrence of cancer symptoms by using machine learning (ML) algorithms could be used to aid clinical decision-making in order to enhance the quality of cancer care. This stu...
This was a retrospective cohort study of consecutive cases that had been diagnosed with breast cancer, stages I-IV. Forty-eight variables were grouped into five feature sets. Five classification model...
Of 370 eligible female participants, 91 had BCRL (24.6%). The mean age of this study sample was 49.89 (SD = 7.45). All participants had had breast cancer surgery, and more than half of them had had ...
This study found that in the ML training dataset, the multilayer perceptron model and the logistic regression model were the best discrimination models for predicting the outcome of BCRL, and the...
Emergency departments are operating with limited resources and high levels of unexpected requests. This study aimed to minimize patients' waiting time and the percentage of units' engagement to improv...
A comprehensive combination method involving Discrete Event Simulation (DES), Artificial Neural Network (ANN) algorithm, and finally solving the model by use of Genetic Algorithm (GA) was used in this...
According to the model optimization result, it was determined that the hospitalization unit, as well as the hospitalization units' doctors, were in an optimized condition, but the triage unit, as well...
Using the service optimization method creates a significant improvement in patient's waiting time and stream at emergency departments, which is made possible through appropriate allocation of the huma...
Subarachnoid hemorrhage has a high rate of disability and mortality, and the ability to use existing disease severity scores to estimate the risk of adverse outcomes is limited. Collect relevant infor...
Patient-level data were extracted from MIMIC-IV data. The primary outcome was in-hospital mortality. The models were trained and tested on a data set (ratio 70:30) including age and key past medical h...
Of the 1,787 patients included in the mimic database, a total of 379 died during hospitalization. Recursive feature abstraction (RFE) selected 20 variables. After simplification, we determined 10 feat...
ML approaches significantly enhance predictive discrimination for mortality following subarachnoid hemorrhage compared to existing illness severity scores and LR. The discriminative ability of these M...
Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the sy...
We devise a series of simulations that measure the effects of data drift in patients with sepsis. We simulate multiple scenarios in which data drift may occur, namely the change in the distribution of...
Our results show that the properly retrained XGB models outperform the baseline models in all simulation scenarios, hence signifying the existence of data drift. In the major event scenario, the area ...
Our simulations reveal that retraining periods of a couple of months or using several thousand patients are likely to be adequate to monitor machine learning models that predict sepsis. This indicates...
Extremely preterm birth has been associated with atypical visual and neural processing of faces, as well as differences in gray matter structure in visual processing areas relative to full-term peers....
Extremely preterm adolescents (n = 20) and full-term peers (n = 24) underwent structural and functional magnetic resonance imaging. Group differences in gray matter density, measured via voxel-based m...
Group differences in two partially overlapping clusters emerged: one from the VBM analysis showing less density in the extremely preterm cohort and one from BOLD response to faces showing greater acti...
Consistent with previous findings, we observed neural differences in extremely preterm youth in an area that plays an important role in face processing. Multimodal analyses revealed differences in str...
Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with ne...
We conducted a comprehensive search of five electronic databases using the Patient, Intervention, Comparison and Outcome (PICO) format. We extracted health conditions, population characteristics, outc...
A total of 19 primary studies were included. The predictors most frequently used belonged to the areas of demographic characteristics and stroke assessment through clinical examination. Regarding the ...
We identified several methodological limitations: small sample sizes, a limited number of external validation approaches, and high heterogeneity among input and output variables. Although these elemen...
Intelligent health diagnosis for young children aims at maintaining and promoting the healthy development of young children, aiming to make young children have a healthy state and provide a better fut...
Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning ...
Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, ...
The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation....
The reference iohexol clearance values were derived from 500 simulated profiles (samples collected between 0.1 and 24.7 h) using a population pharmacokinetic model we recently developed in Monolix and...
The MAP-BE limited sampling strategy estimated clearance was corrected by the machine learning predicted error leading to a decrease in root mean squared error by 29% and 24%, and in the percentage of...
In conclusion, this hybrid algorithm represents a significant improvement in comparison to MAP-BE estimation alone....
