AIMC Topic: Aged

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Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it's unclear to what extent patient-reported CVD information is accu...

Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clusters of differentiation () are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies () afford rich trans-disease repositioning opportunities. Within a ...

Disease Trajectories and End-of-Life Care for Dementias: Latent Topic Modeling and Trend Analysis Using Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Despite the increasing prevalence, growing costs, and high mortality of dementia in older adults in the U.S., little is known about the course of these diseases and what care dementia patients receive in their final years of life. Using a large volum...

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...

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...

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...

Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes.

Journal of translational medicine
BACKGROUND: Increased systemic and local inflammation play a vital role in the pathophysiology of acute coronary syndrome. This study aimed to assess the usefulness of selected machine learning methods and hematological markers of inflammation in pre...

Robot-assisted gait training effectively improved lateropulsion in subacute stroke patients: a single-blinded randomized controlled trial.

European journal of physical and rehabilitation medicine
BACKGROUND: Some stroke patients are known to use nonparetic extremities to push toward the paretic side, a movement known as lateropulsion. Lateropulsion impairs postural balance and interferes with rehabilitation.

A Novel Artificial Organic Control System for Mobile Robot Navigation in Assisted Living Using Vision- and Neural-Based Strategies.

Computational intelligence and neuroscience
Robots in assisted living (RAL) are an alternative to support families and professional caregivers with a wide range of possibilities to take care of elderly people. Navigation of mobile robots is a challenging problem due to the uncertainty and dyna...