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Predicting hearing recovery following treatment of idiopathic sudden sensorineural hearing loss with machine learning models.

American journal of otolaryngology
PURPOSE: Idiopathic sudden sensorineural hearing loss (ISSHL) is an emergency otological disease, and its definite prognostic factors remain unclear. This study applied machine learning methods to develop a new ISSHL prognosis prediction model.

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.

Interdisciplinary sciences, computational life sciences
Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the...

Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.

PLoS neglected tropical diseases
BACKGROUND: Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patien...

Treatment effect prediction with adversarial deep learning using electronic health records.

BMC medical informatics and decision making
BACKGROUND: Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized cl...

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...