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Logistic Models

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Application of machine learning based methods in exposure-response analysis.

Journal of pharmacokinetics and pharmacodynamics
Robust estimation of exposure response analysis relies on correct specification of the model structure with traditional parametric approach. However, the assumptions of the handcrafted model may not always hold or verifiable. Here, we conducted a sim...

Machine Learning Improves Prediction Over Logistic Regression on Resected Colon Cancer Patients.

The Journal of surgical research
INTRODUCTION: Despite advances, readmission and mortality rates for surgical patients with colon cancer remain high. Prediction models using regression techniques allows for risk stratification to aid periprocedural care. Technological advances have ...

Application Analysis of Combining BP Neural Network and Logistic Regression in Human Resource Management System.

Computational intelligence and neuroscience
Human resource management involves a variety of data processing, and the process is complicated. In order to improve the effect of human resource management, this paper combines BP neural network and logistic regression analysis to construct an intel...

Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results.

International journal of laboratory hematology
INTRODUCTION: Wrong blood in tube (WBIT) errors are a significant patient-safety issue encountered by clinical laboratories. This study assessed the performance of machine learning models for the identification of WBIT errors affecting complete blood...

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

World neurosurgery
OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of ...

Comparative analysis of explainable machine learning prediction models for hospital mortality.

BMC medical research methodology
BACKGROUND: Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acce...

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation.

Computational and mathematical methods in medicine
Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with...

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization.

Scientific reports
Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application o...

Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees.

International journal of environmental research and public health
Pillars are important structural elements that provide temporary or permanent support in underground spaces. Unstable pillars can result in rock sloughing leading to roof collapse, and they can also cause rock burst. Hence, the prediction of undergro...

Interpretable Classification of Bacterial Raman Spectra With Knockoff Wavelets.

IEEE journal of biomedical and health informatics
Deep neural networks and other machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especi...