AIMC Topic: Humans

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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals.

Sensors (Basel, Switzerland)
Sleep apnea syndrome (SAS) affects about 3-7% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard for diagnosing SAS i...

AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method.

International journal of molecular sciences
MicroRNAs (miRNAs) play a crucial role in gene regulation and are strongly linked to various diseases, including cancer. This study presents AEmiGAP, an advanced deep learning model that integrates autoencoders with long short-term memory (LSTM) netw...

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools.

Critical care (London, England)
BACKGROUND: Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic informati...

Effectiveness of unilateral lower-limb exoskeleton robot on balance and gait recovery and neuroplasticity in patients with subacute stroke: a randomized controlled trial.

Journal of neuroengineering and rehabilitation
BACKGROUND: Impaired balance and gait in stroke survivors are associated with decreased functional independence. This study aimed to evaluate the effectiveness of unilateral lower-limb exoskeleton robot-assisted overground gait training compared with...

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Cell communication and signaling : CCS
BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selec...

A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine.

BMC cancer
BACKGROUND AND OBJECTIVE: Positron emission tomography/computed tomography (PET/CT) is recommended as the standard imaging modality for diffuse large B-cell lymphoma (DLBCL) staging. However, many studies have neglected the role of patients' prognost...

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BMC infectious diseases
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...

Machine learning classification meets migraine: recommendations for study evaluation.

The journal of headache and pain
The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodolo...

AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.

Scientific reports
AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these...

Robust modelling of arterial blood pressure reconstruction from photoplethysmography.

Scientific reports
Blood pressure is a crucial indicator of cardiovascular disease, and arterial blood pressure (ABP) waveforms contain information that reflects the cardiovascular status. We propose a novel deep-learning method that converts photoplethysmogram (PPG) s...