AIMC Topic: Logistic Models

Clear Filters Showing 641 to 650 of 1261 articles

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.

Neurobiology of aging
Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learni...

Predicting hospitalization following psychiatric crisis care using machine learning.

BMC medical informatics and decision making
BACKGROUND: Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this pa...

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.

Journal of translational medicine
BACKGROUND: Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible predicti...

Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.

American journal of surgery
BACKGROUND: Accurate prediction of thyroidectomy complications is necessary to inform treatment decisions. Ensemble machine learning provides one approach to improve prediction.

Machine learning-based e-commerce platform repurchase customer prediction model.

PloS one
In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This pap...

Predict multicategory causes of death in lung cancer patients using clinicopathologic factors.

Computers in biology and medicine
BACKGROUND: Random forests (RF) is a widely used machine-learning algorithm, and outperforms many other machine learning algorithms in prediction-accuracy. But it is rarely used for predicting causes of death (COD) in cancer patients. On the other ha...

Machine learning algorithm for early detection of end-stage renal disease.

BMC nephrology
BACKGROUND: End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD...

Machine learning to predict mortality after rehabilitation among patients with severe stroke.

Scientific reports
Stroke is among the leading causes of death and disability worldwide. Approximately 20-25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decisi...

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning.

Scientific reports
Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and co...