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Age Factors

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Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).

Age and ageing
INTRODUCTION: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop...

Age-Specific Diagnostic Classification of ASD Using Deep Learning Approaches.

Studies in health technology and informatics
Autism Spectrum Disorder (ASD) is a highly heterogeneous condition, due to high variance in its etiology, comorbidity, pathogenesis, severity, genetics, and brain functional connectivity (FC). This makes it devoid of any robust universal biomarker. T...

Gait changes following robot-assisted gait training in children with cerebral palsy.

Physiological research
This study investigated changes of gait pattern induced by a 4-week robot-assisted gait training (RAGT) in twelve ambulatory spastic diparesis children with cerebral palsy (CP) aged 10.4+/-3.2 years old by using computerized gait analysis (CGA). Pre-...

New approach of prediction of recurrence in thyroid cancer patients using machine learning.

Medicine
Although papillary thyroid cancers are known to have a relatively low risk of recurrence, several factors are associated with a higher risk of recurrence, such as extrathyroidal extension, nodal metastasis, and BRAF gene mutation. However, predicting...

Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

Anesthesia and analgesia
BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surgery (POUS). While risk factors have been previously identified, no methods currently exist to determin...

The Value of Artificial Intelligence in Laboratory Medicine.

American journal of clinical pathology
OBJECTIVES: As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evalua...

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

Medicine
In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefo...

DS-GCNs: Connectome Classification using Dynamic Spectral Graph Convolution Networks with Assistant Task Training.

Cerebral cortex (New York, N.Y. : 1991)
Functional connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. A brain network, also named as connectome, could form a graph structure naturally, the nodes ...

Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning.

Cancer control : journal of the Moffitt Cancer Center
INTRODUCTION: Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within le...