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Logistic Models

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Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths.

The Journal of emergency medicine
BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, mig...

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.

Computational and mathematical methods in medicine
Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pa...

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters.

Sensors (Basel, Switzerland)
Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identific...

Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models.

BMC cardiovascular disorders
BACKGROUND: Machine learning (ML) can include more diverse and more complex variables to construct models. This study aimed to develop models based on ML methods to predict the all-cause mortality in coronary artery disease (CAD) patients with atrial...

Deep embeddings and logistic regression for rapid active learning in histopathological images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recognizing different tissue components is one of the most fundamental and essential works in digital pathology. Current methods are often based on convolutional neural networks (CNNs), which need numerous annotated samples ...

Artificial intelligence forecasting mortality at an intensive care unit and comparison to a logistic regression system.

Einstein (Sao Paulo, Brazil)
OBJECTIVE: To explore an artificial intelligence approach based on gradient-boosted decision trees for prediction of all-cause mortality at an intensive care unit, comparing its performance to a recent logistic regression system in the literature, an...

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach.

International journal of environmental research and public health
BACKGROUND: Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strat...

BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning Model.

Computational intelligence and neuroscience
Financial early warning mechanism is of great significance to the long-term healthy development and stable operation of listed enterprises. This paper adopts the logistic regression early warning model and BP neural network early warning model. Based...

PredAPP: Predicting Anti-Parasitic Peptides with Undersampling and Ensemble Approaches.

Interdisciplinary sciences, computational life sciences
Anti-parasitic peptides (APPs) have been regarded as promising therapeutic candidate drugs against parasitic diseases. Due to the fact that the experimental techniques for identifying APPs are expensive and time-consuming, there is an urgent need to ...