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Constructing an early warning model for elderly sepsis patients based on machine learning.

Scientific reports
Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial intelligence shows benefits in early warning. The aim of the study was to construct an early machin...

Predicting badminton outcomes through machine learning and technical action frequencies.

Scientific reports
The application of machine learning techniques to predict badminton match outcomes through the analysis of technical actions seems to represent an area that has not yet been extensively investigated within the existing body of research. This study ai...

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post-COVID-19 Condition: Protocol for an Observational Study.

JMIR research protocols
BACKGROUND: Post-COVID-19 condition is emerging as a new epidemic, characterized by the persistence of COVID-19 symptoms beyond 3 months, and is anticipated to substantially alter the lives of millions of people globally. Patients with severe episode...

Development and validation of inpatient mortality prediction models for patients with hyperglycemic crisis using machine learning approaches.

BMC endocrine disorders
BACKGROUND: Hyperglycemic crisis is one of the most common and severe complications of diabetes mellitus, associated with a high motarlity rate. Emergency admissions due to hyperglycemic crisis remain prevalent and challenging. This study aimed to de...

Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

Scientific reports
The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Poss...

Deep learning techniques for proton dose prediction across multiple anatomical sites and variable beam configurations.

Physics in medicine and biology
To evaluate the impact of beam mask implementation and data aggregation on artificial intelligence-based dose prediction accuracy in proton therapy, with a focus on scenarios involving limited or highly heterogeneous datasets.In this study, 541 prost...

A prospective study for the examination of peripheral blood smear samples in pediatric population using artificial intelligence.

Turkish journal of medical sciences
BACKGROUND/AIM: Peripheral blood smear (PBS) and bone marrow aspiration are gold standards of manual microscopy diagnostics for blood cell disorders. Nowadays, data-driven artificial intelligence (AI) techniques open new perspectives in digital hemat...

Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and Health Survey.

Frontiers in public health
INTRODUCTION: Khat chewing is a significant public health issue in Ethiopia, influenced by various demographic factors. Understanding the prevalence and determinants of khat chewing practices is essential to developing targeted interventions. Therefo...

Predicting high anger intensity using ecological momentary assessment and wearable-derived physiological data in a trauma-affected sample.

European journal of psychotraumatology
Digital technologies offer tremendous potential to predict dysregulated mood and behavior within an individual's environment, and in doing so can support the development of new digital health interventions. However, no prediction models have been bu...

Monocyte distribution width (MDW) as a reliable diagnostic biomarker for sepsis in patients with HIV.

Emerging microbes & infections
Sepsis is a leading cause of death among patients with HIV, but early diagnosis remains a challenge. This study evaluates the diagnostic performance of monocyte distribution width (MDW) in detecting sepsis in patients with HIV. A prospective observat...