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Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation.

International journal of medical informatics
OBJECTIVES: Prediction of activities of daily living (ADL) is crucial for optimized care of post-stroke patients. However, no suitably-validated and practical models are currently available in clinical practice.

Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography - Time of flight mass spectrometry and machine learning.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Tuberculosis (TB) remains a global public health malady that claims almost 1.8 million lives annually. Diagnosis of TB represents perhaps one of the most challenging aspects of tuberculosis control. Gold standards for diagnosis of active TB (culture ...

Role of α-fetoprotein in differentiation of regulatory T lymphocytes.

Doklady biological sciences : proceedings of the Academy of Sciences of the USSR, Biological sciences sections
The effect of native α-fetoprotein (AFP) on the expression of T-regulatory lymphocyte (Treg) markers by activated CD4 lymphocytes with different proliferative status was studied. α-Fetoprotein did not affect the ratio of proliferating and non-prolife...

Comparison of radiofrequency and transoral robotic surgery in obstructive sleep apnea syndrome treatment.

Acta oto-laryngologica
INTRODUCTION: Radiofrequency tissue ablation (RFTA) and transoral robotic surgery (TORS) are the methods used in OSAS surgery. We also aimed to compare the advantages and disadvantages of RF and TORS as treatment methods applied in OSAS patients in t...

Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features.

Computers in biology and medicine
Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of thes...

Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives.

Computers in biology and medicine
Venous thromboembolism (VTE) is the third most common cardiovascular disorder. It affects people of both genders at ages as young as 20 years. The increased number of VTE cases with a high fatality rate of 25% at first occurrence makes preventive mea...

Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals.

Computers in biology and medicine
Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the disease progresses to an advanced stage. The electrocardiogram (ECG) is a widely accessible...

Sleep in patients with disorders of consciousness characterized by means of machine learning.

PloS one
Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continu...