AIMC Topic: Male

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Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. NAFLD patients have excessive liver fat (steatosis), without other liver diseases and without excessive alcohol consumption. NAFLD consists of a spectr...

Towards the Inference of Social and Behavioral Determinants of Sexual Health: Development of a Gold-Standard Corpus with Semi-Supervised Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Social and behavioral determinants of health (SBDH) are environmental and behavioral factors that are increasingly recognized for their impact on health outcomes. We describe ongoing research to extract SBDH related to sexual health from clinical doc...

Applying Machine Learning to Linked Administrative and Clinical Data to Enhance the Detection of Homelessness among Vulnerable Veterans.

AMIA ... Annual Symposium proceedings. AMIA Symposium
U.S. military veterans who were discharged from service for misconduct are at high risk for homelessness. Stratifying homelessness risk based on both military service factors and clinical characteristics could facilitate targeted provision of prevent...

An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Digital rectal examination (DRE) is considered a quality metric for prostate cancer care. However, much of the DRE related rich information is documented as free-text in clinical narratives. Therefore, we aimed to develop a natural language processin...

A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports. The objective of this study was to determine whether natural language processing (NLP) with machine learning per...

Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition.

PloS one
Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ult...

Predicting posterior urethral obstruction in boys with lower urinary tract symptoms using deep artificial neural network.

World journal of urology
PURPOSE: To assess the prediction model for late-presenting posterior urethral valve (PUV) in boys with lower urinary tract symptoms (LUTS) using artificial neural network (ANN).

Defining Massive Transfusion in Civilian Pediatric Trauma With Traumatic Brain Injury.

The Journal of surgical research
The purpose of this study was to identify an optimal definition of massive transfusion in civilian pediatric trauma with severe traumatic brain injury (TBI) METHODS: Severely injured children (age ≤18 y) with severe TBI in the Trauma Quality Improvem...

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

Echocardiography (Mount Kisco, N.Y.)
BACKGROUND: Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during init...