Machine Learning Modeling for Autism Spectrum Disorder Handling and Treatment

The project is about developing a decision support tool to provide a personalized handling and treatment for Autistic Syndrome Disorder patients. This decision support tool will integrate machine learning modeling that will be used to suggest optimal and personalized guidelines for a very large spectrum of ASD patients, not available so far. Theses guidelines will be used by concerned parents, teachers and therapist that are in charge of these patients. The partner is a company that offer accompanying and training services to ASD patients’ parents and therapists.

Sex: It’s a matter of the heart

Heart failure is a complex cardiovascular disease with increasing global burden while the prognosis for patients remains poor. Risk factors and the type of heart failure differ between men and women. These differences can be due to sex – referring to biological differences – or gender – referring to social differences. In our project we will study the role of genetics in the different types of heart failure in men and women, using models that distinguish the contribution of both sex and gender.

Preference analyses and development of an e-health app facilitating communication of test results for hereditary cancer syndromes

An estimated 5-10% of cancers are inherited through family members. To identify patients’ risk for developing hereditary cancers, genetic testing can be used. Communicating hereditary cancer genetic results to patients is challenging for health care practitioners. Practitioners want to ensure that patients understand and communicate their preferences for receiving information. Tools to aid patients in communicating their preferences need to be developed.

Salivary insulin profiles throughout the day in humans with elevated waist circumference

The hormone insulin rises in the blood after consuming food. Too high of an insulin response may be a sign of dysfunctional metabolism and has also been shown to promote weight gain. Thus, if insulin levels rise too high after a meal it may indicate that someone is metabolically unhealthy or is prone to obesity. Currently insulin is only measured in research studies using blood samples and costly, time consuming measurement techniques. This project aims to determine if saliva can be used to measure insulin in order to allow for non-invasive insulin measurement.

Childhood Healthy Weights Early Intervention Program

The Early Intervention Program (EIP) is a family-based intervention targeting families of children who are off the healthy weight trajectory. The EIP is a 10-week program offered at community centers across BC where children and their families meet once a week for 90 minutes. Parents will be provided with healthy lifestyle content and will engage in discussions on how to engage in health behaviours, and children will participate in physical activities aiming to enhance their motor skills.

The Genetics of Blood Biomarkers in COPD

COPD is a progressive inflammatory airway disease characterized by persistent and progressive airway inflammation. It is a major cause of global morbidity and mortality and is predicted to become the third leading cause of death by 2020. Biomarkers may be useful for diagnosing disease considering that the usually used lung function measures have poor correlation with both symptoms and other measures of disease progression. However, the relationship between biomarkers and COPD is still elusive.

Predicting recovery from concussion during military cadet training using multimodal MRI data and machine learning

In the military, concussions are common and many occur while non-deployed, including during cadet training exercises. For the majority of those with concussions, symptoms resolve on their own but for a “miserable minority” symptoms persist beyond the typical 3-month recovery period, impacting quality of life. Most concussion research produces group level inferences which cannot be used to make individual predictions. We propose a supervised machine learning approach to build a model to predict symptom recovery from multiple MRI brain measures.

Development of Dielectric Coating for the Next Generation Wearable Heart Monitoring System

Electrocardiography or ECG is a technology that can be used to monitor the electrical activity of the heart over a long period of time. Such signals can be utilized for interpreting both the structure and function of the heart. Traditional ECG designs consist of several electrodes that need to be placed directly in contact with a patient’s skin. These metallic sensory electrodes are usually made into disc-like configuration and one of the major drawbacks is the inconsistency in signal recording due to poor skin contact from the non-flexible nature of the electrode design.

A method to reduce floating ground effect on wearable Electrocardiography (ECG) sensors

Heart’s electrical activities are traditionally collected by employing multiple electrodes that are connected the patient’s body and collected and recorded on an electrocardiogram. The objective of this project is to enhance the patient’s comfort and allow for a continues ECG monitoring with improved flexibility while minimizing the sacrifices of detected and accumulated data. In this proposal an unconventional method is suggested that employs physically separated sensors and circuits to enhance patient comfort and allows for a better flexibility.

Effects of choir participation and musical training on auditory processing in hearing aided older adults.

Hearing loss, which most adults will experience to some degree as they age, has been associated with decreased emotional wellbeing and reduced quality of life in aging adults. Although hearing aids can target aspects of peripheral hearing loss, persistent perceptual deficits are widely reported. One prevalent example is the loss of the ability to perceive speech in a noisy environment, which severely impacts quality of life and goes relatively unremediated by hearing aids.

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