AIMC Topic: Female

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Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), ...

Inferring Clinical Correlations from EEG Reports with Deep Neural Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Successful diagnosis and management of neurological dysfunction relies on proper communication between the neurologist and the primary physician (or other specialists). Because this communication is documented within medical records, the ability to a...

Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Advanced regression and machine learning models can provide personalized risk predictions to support clinical decision-making. We aimed to understand whether modeling methods impact the tendency of calibration to deteriorate as patient populations sh...

Predicting Inpatient Acute Kidney Injury over Different Time Horizons: How Early and Accurate?

AMIA ... Annual Symposium proceedings. AMIA Symposium
Incidence of Acute Kidney Injury (AKI) has increased dramatically over the past two decades due to rising prevalence of comorbidities and broadening repertoire of nephrotoxic medications. Hospitalized patients with AKI are at higher risk for complica...

Deep Learning Solutions for Classifying Patients on Opioid Use.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Opioid analgesics, as commonly prescribed medications used for relieving pain in patients, are especially prevalent in US these years. However, an increasing amount of opioid misuse and abuse have caused lots of consequences. Researchers and clinicia...

Visual Kinship Recognition of Families in the Wild.

IEEE transactions on pattern analysis and machine intelligence
We present the largest database for visual kinship recognition, Families In the Wild (FIW), with over 13,000 family photos of 1,000 family trees with 4-to-38 members. It took only a small team to build FIW with efficient labeling tools and work-flow....

Assessing patient risk of central line-associated bacteremia via machine learning.

American journal of infection control
BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLAB...

Does Cognitive Impairment and Agitation in Dementia Influence Intervention Effectiveness? Findings From a Cluster-Randomized-Controlled Trial With the Therapeutic Robot, PARO.

Journal of the American Medical Directors Association
OBJECTIVES: To explore whether severity of cognitive impairment and agitation of older people with dementia predict outcomes in engagement, mood states, and agitation after a 10-week intervention with the robotic seal, PARO.

Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI.

International journal of neural systems
Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not h...