AIMC Topic: Machine Learning

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Machine Learning Exploration of Brain Morphological Features and Sensory Measures.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Previous investigations have implicated the neuroanatomical basis of sensory systems; however, definitive neuroimaging biomarkers remain elusive. The present study employs machine learning techniques to probe the relationship between brain morphologi...

Drug-Target Interaction Prediction via Deep Multimodal Graph and Structural Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Drug-target interaction (DTI) prediction speeds up drug repurposing, accelerates drug screening, and reduces drug design timeframe. Previous DTI prediction frameworks lack consideration for the multimodal nature of DTI, advanced feature representatio...

Prediction of Low Bone Mass for Japanese Female Athletes Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Attainment of optimal bone mineral density (BMD) in female athletes is a very important aspect of ensuring their well-being throughout their lives, and monitoring of BMD is essential to avoid fractures and bone-related injuries. Several tools for ass...

Classification of Abnormal Gaits with Machine Learning Algorithms using Sensor-Inherited Insoles.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The digital health industry's interest in gait analysis has driven research into sensor-enabled insoles for practical, everyday gait monitoring. Traditional methods, such as 3D motion capture systems, are costly and time-consuming. To address this, w...

Interictal Epileptiform Discharge Detection Using Time-Frequency Analysis and Transfer Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur in between seizures in Epilepsy patients. Automated detection of IEDs is crucial for assisting clinicians in epilepsy diagnosis as they can help ident...

Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are developed to supplement the often inaccurate interview-based risk assessments. Our research goal is to ...

Predicting Cardiovascular Disease Risk in Tobacco Users Using Machine Learning Algorithms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cardiovascular Diseases (CVDs) present a substantial global health burden, with tobacco use as a major risk factor. While extensive research has identified several risk factors for CVDs, there is a gap in predictive models that account for a combinat...

Estimating Upper-extremity Function with Raw Kinematic Trajectory Data after Stroke using End-to-end Machine Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Although there are some studies on the automatic evaluation of impairment levels after stroke using machine learning (ML) models, few have delved into the predictive capabilities of raw motion data. In this study, we captured kinematic trajectories o...

ANTs, BET, or…neither? An exploration of brain masking and machine learning tools applied to magnetic resonance elastography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Magnetic resonance elastography is a quantitative MRI modality that can aid in diagnosis of disease by detecting altered tissue mechanical properties. While brain masking tools exist for common MRI sequences, such as T1-weighted and T2-weighted imagi...

Learning Physiological Mechanisms that Predict Adverse Cardiovascular Events in Intensive Care Patients with Chronic Heart Disease.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Chronic heart disease is a burdensome, complex, and fatal condition. Learning the mechanisms driving the development of heart disease is key to early risk assessment and intervention. However, many current machine learning approaches lack sufficient ...