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Logistic Models

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Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Pacing and clinical electrophysiology : PACE
BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utili...

Fetal birthweight prediction with measured data by a temporal machine learning method.

BMC medical informatics and decision making
BACKGROUND: Birthweight is an important indicator during the fetal development process to protect the maternal and infant safety. However, birthweight is difficult to be directly measured, and is usually roughly estimated by the empirical formulas ac...

Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serious adverse events due to fluid non-responsiveness. The current method of deciding if patients are responsive to fluid administration is often subjecti...

Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably.

Journal of clinical epidemiology
OBJECTIVE: The objective of the study was to compare the performance of logistic regression and boosted trees for predicting patient mortality from large sets of diagnosis codes in electronic healthcare records.

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers.

Medicina (Kaunas, Lithuania)
BACKGROUND AND OBJECTIVE: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths.

Research on Early Warning Mechanism and Model of Liver Cancer Rehabilitation Based on CS-SVM.

Journal of healthcare engineering
Since the 20 century, cancer has become one of the main diseases threatening human health. Liver cancer is a malignant tumor with extremely high clinical morbidity and fatality rate and easy recurrence after surgery. Research on the postoperative rec...

A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning.

Journal of orthopaedic surgery and research
BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. Ho...

Evaluation of the performance of various machine learning methods on the discrimination of the active compounds.

Chemical biology & drug design
Machine learning (ML) method performances, including deep learning (DL) on a diverse set with or without feature selection (FS), were evaluated. The superior performance of DL on small sets has not been approved previously. On the other hand, the ava...

Supervised machine learning for automated classification of human Wharton's Jelly cells and mechanosensory hair cells.

PloS one
Tissue engineering and gene therapy strategies offer new ways to repair permanent damage to mechanosensory hair cells (MHCs) by differentiating human Wharton's Jelly cells (HWJCs). Conventionally, these strategies require the classification of each c...

Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes.

Journal of medical systems
Deep neural network models are emerging as an important method in healthcare delivery, following the recent success in other domains such as image recognition. Due to the multiple non-linear inner transformations, deep neural networks are viewed by m...