AIMC Topic: Adult

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Identifying patients with familial hypercholesterolemia using data mining methods in the Northern Great Plain region of Hungary.

Atherosclerosis
BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is one of the most frequent diseases with monogenic inheritance. Previous data indicated that the heterozygous form occurred in 1:250 people. Based on these reports, around 36,000-40,000 people ...

Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

Clinical orthopaedics and related research
BACKGROUND: Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artific...

Deep Learning-Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study.

Thyroid : official journal of the American Thyroid Association
BACKGROUND: The presence of metastatic lymph nodes is a prognostic indicator for patients with thyroid carcinomas and is an important determinant of clinical decision making. However, evaluating neck lymph nodes requires experience and is labor- and ...

Unique Clinical Language Patterns Among Expert Vestibular Providers Can Predict Vestibular Diagnoses.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
OBJECTIVE: To identify novel language usage by expert providers predictive of specific vestibular conditions.

Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension.

Anesthesiology
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Hypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive ana...

The impact of social housing on mental health: longitudinal analyses using marginal structural models and machine learning-generated weights.

International journal of epidemiology
BACKGROUND: Social housing may provide an affordable and secure residential environment, but has also been associated with stigma, poor housing conditions and locational disadvantage. We examined the cumulative effect of additional years, and tenure ...

Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis.

Anesthesiology
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors' goal was to apply machine learning to ar...

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Anesthesiology
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality.

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.

Neurosurgery
BACKGROUND: Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but i...

Hemodynamic Instability and Cardiovascular Events After Traumatic Brain Injury Predict Outcome After Artifact Removal With Deep Belief Network Analysis.

Journal of neurosurgical anesthesiology
BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopa...