AIMC Topic: Female

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A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Undiagnosed or untreated moderate-to-severe obstructive sleep apnea (OSA) increases cardiovascular risks and mortality. Early and efficient detection is critical, given its high prevalence. We aimed to develop a practical and effici...

Distributed Representations for Cognitive Control in Frontal Medial Cortex.

Journal of cognitive neuroscience
In natural and artificial neural networks, modularity and distributed structure afford complementary but competing benefits. The former allows for hierarchical representations that can flexibly recombine modules to address novel problems, whereas the...

The combination of physiology and machine learning for prediction of CPAP pressure and residual AHI in OSA.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea; however, some people have residual respiratory events or require significantly higher CPAP pressure while on therapy. Our objective w...

Artificial Intelligence Assessment of Chest Radiographs for COVID-19.

Clinical lymphoma, myeloma & leukemia
BACKGROUND: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; how...

Incorporating computer vision on smart phone photographs into screening for inflammatory arthritis: results from an Indian patient cohort.

Rheumatology (Oxford, England)
OBJECTIVES: Convolutional neural networks (CNNs) are increasingly used to classify medical images, but few studies utilize smartphone photographs. The objective of this study was to assess CNNs for differentiating patients from controls and detecting...

Deep Learning for Predicting Acute Exacerbation and Mortality of Interstitial Lung Disease.

Annals of the American Thoracic Society
Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increased mortality. Early identification of these high-risk patients and accurate prediction of the onset...

Predicting Progression to Dementia Using Auditory Verbal Learning Test in Community-Dwelling Older Adults Based On Machine Learning.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
BACKGROUND: Primary healthcare institutions find identifying individuals with dementia particularly challenging. This study aimed to develop machine learning models for identifying predictive features of older adults with normal cognition to develop ...

Application of Machine Learning Techniques to the Prediction of Onset and Persistence of Binge Eating: A Prospective Study.

European eating disorders review : the journal of the Eating Disorders Association
OBJECTIVE: Machine learning (ML) techniques have shown promise for enhancing prediction of clinical outcomes; however, its application to predicting binge eating has been scarcely explored. We applied ML techniques to predict binge eating onset (vs. ...

Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography.

Abdominal radiology (New York)
BACKGROUND & AIMS: Enhanced computed tomography (CT) is the primary method for focal liver lesion diagnosis. We aimed to use automated machine learning (AutoML) algorithms to differentiate between benign and malignant focal liver lesions on the basis...