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The use of 4D data-independent acquisition-based proteomic analysis and machine learning to reveal potential biomarkers for stress levels.

Journal of bioinformatics and computational biology
Research suggests that individuals who experience prolonged exposure to stress may be at higher risk for developing psychological stress disorders. Currently, psychological stress is primarily evaluated by professional physicians using rating scales,...

Improved Surface Electromyogram-Based Hand-Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning.

Sensors (Basel, Switzerland)
Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Her...

Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction.

Current oncology (Toronto, Ont.)
Prostate cancer (PCa) is a clinically heterogeneous disease. Predicting clinically significant PCa with low-intermediate prostate-specific antigen (PSA), which often includes aggressive cancers, is imperative. This study evaluated the predictive accu...

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

Cardiovascular diabetology
BACKGROUND: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop ...

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study.

Scientific reports
The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% mal...

Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

Scientific reports
OBJECTIVE: The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.

Scientific reports
The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive,...

Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration.

Scientific reports
To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical coherence tomography (OCT) in intermediate AMD (iAMD). Patients with iAMD were examined by OCT (Spec...

MIMIC-BP: A curated dataset for blood pressure estimation.

Scientific data
Blood pressure (BP) is one of the most prominent indicators of potential cardiovascular disorders. Traditionally, BP measurement relies on inflatable cuffs, which is inconvenient and limit the acquisition of such important health-related information ...

Predictors for improvement in personality functioning during outpatient psychotherapy: A machine learning approach within a psychodynamic psychotherapy sample.

European psychiatry : the journal of the Association of European Psychiatrists
BACKGROUND: Since its introduction in the diagnostic manuals DSM-5 and ICD-11, the construct of personality functioning has gained increasing attention. However, it remains unclear which factors might predict improvement in personality functioning.