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Machine learning versus traditional risk stratification methods in acute coronary syndrome: a pooled randomized clinical trial analysis.

Journal of thrombosis and thrombolysis
Traditional statistical models allow population based inferences and comparisons. Machine learning (ML) explores datasets to develop algorithms that do not assume linear relationships between variables and outcomes and that may account for higher ord...

The Purpose of Bedside Robots: Exploring the Needs of Inpatients and Healthcare Professionals.

Computers, informatics, nursing : CIN
Robotic systems are used to support inpatients and healthcare professionals and to improve the efficiency and quality of nursing. There is a lack of scientific literature on how applied robotic systems can be used to support inpatients. This study us...

Machine-learning-based knowledge discovery in rheumatoid arthritis-related registry data to identify predictors of persistent pain.

Pain
Early detection of patients with chronic diseases at risk of developing persistent pain is clinically desirable for timely initiation of multimodal therapies. Quality follow-up registries may provide the necessary clinical data; however, their design...

Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning.

JAMA psychiatry
IMPORTANCE: Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvement in treatment is slow, a primary reason being the lack of objective and systematic methods for meas...

Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Periodontitis (PD), a form of gum disease, is a major public health concern as it is globally prevalent and harms both individual quality of life and economic productivity. Global cost in lost productivity is estimated at US$54 billion an...

Leveraging Electronic Health Records and Machine Learning to Tailor Nursing Care for Patients at High Risk for Readmissions.

Journal of nursing care quality
BACKGROUND: Electronic health record-derived data and novel analytics, such as machine learning, offer promising approaches to identify high-risk patients and inform nursing practice.

3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI.

Neuroinformatics
We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state function...

Imputation Strategy for Reliable Regional MRI Morphological Measurements.

Neuroinformatics
Regional morphological analysis represents a crucial step in most neuroimaging studies. Results from brain segmentation techniques are intrinsically prone to certain degrees of variability, mainly as results of suboptimal segmentation. To reduce this...

Hierarchical Structured Sparse Learning for Schizophrenia Identification.

Neuroinformatics
Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-freque...