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Determining Pain Catastrophizing From Daily Pain App Assessment Data: Role of Computer-Based Classification.

The journal of pain
This study compared persons with chronic pain who consistently reported that their pain was worsening with those who reported that their pain was improving or remaining the same per daily assessment data from a smartphone pain app. All participants c...

An accessible and efficient autism screening method for behavioural data and predictive analyses.

Health informatics journal
Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting times for an autism spectrum disorder diagnosis are lengthy due to the fact that current diagnostic p...

Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

The British journal of dermatology
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremel...

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.

Current psychiatry reports
PURPOSE OF REVIEW: As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been propo...

Fetal health status prediction based on maternal clinical history using machine learning techniques.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultrasonographical evaluations of fetuses enha...

Application of Machine Learning to Predict Dietary Lapses During Weight Loss.

Journal of diabetes science and technology
BACKGROUND: Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of resea...

Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health. Trends in these measurements can accurately track diabetes, cardiovascular issue...

ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms.

Journal of healthcare engineering
We introduce an approach to predict deterioration of reaction state for people having neurological movement disorders such as hand tremors and nonvoluntary movements. These involuntary motor features are closely related to the symptoms occurring in p...

Mobile technology and telemedicine for shoulder range of motion: validation of a motion-based machine-learning software development kit.

Journal of shoulder and elbow surgery
BACKGROUND: Mobile technology offers the prospect of delivering high-value care with increased patient access and reduced costs. Advances in mobile health (mHealth) and telemedicine have been inhibited by the lack of interconnectivity between devices...

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

Nutrients
Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approache...