AIMC Topic: Female

Clear Filters Showing 19041 to 19050 of 29210 articles

Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.

The Lancet. Digital health
BACKGROUND: Current lung cancer screening guidelines use mean diameter, volume or density of the largest lung nodule in the prior computed tomography (CT) or appearance of new nodule to determine the timing of the next CT. We aimed at developing a mo...

Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study.

Journal of medical Internet research
BACKGROUND: Poor quality primary health care is a major issue in China, particularly in blindness prevention. Artificial intelligence (AI) could provide early screening and accurate auxiliary diagnosis to improve primary care services and reduce unne...

Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk.

Translational psychiatry
Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predicto...

Voice-Controlled Intelligent Personal Assistants to Support Aging in Place.

Journal of the American Geriatrics Society
BACKGROUND/OBJECTIVES: Many older adults wish to age in place, and voice-controlled intelligent personal assistants (VIPAs; eg, Amazon Echo and Google Home) potentially could support unmet home needs. No prior studies have researched the real-world u...

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Computers in biology and medicine
PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.

Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to i...

Utility of Big Data in Predicting Short-Term Blood Glucose Levels in Type 1 Diabetes Mellitus Through Machine Learning Techniques.

Sensors (Basel, Switzerland)
Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose-insulin dynamics based on the sensor data collected by monitoring severa...

Machine Learning Based Prediction of Insufficient Herbage Allowance with Automated Feeding Behaviour and Activity Data.

Sensors (Basel, Switzerland)
Sensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support f...

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.

JMIR mHealth and uHealth
BACKGROUND: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devic...

Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms.

Respiratory research
BACKGROUND: The differential diagnosis of tuberculous pleural effusion (TPE) is challenging. In recent years, artificial intelligence (AI) machine learning algorithms have started being used to an increasing extent in disease diagnosis due to the hig...