Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine learning (2)
- Artificial Intelligence (1)
- Audio Signal Analysis (1)
- COVID-19 (1)
- Causality (1)
-
- Clinical Epidemiology (1)
- Consciousness (1)
- Cost-Sensitive ML (1)
- Cough Analysis (1)
- Cough Sound (1)
- Crowdsourced Data (1)
- Descriptive Epidemiology (1)
- E-Health (1)
- Eddy Current Compensation (1)
- Functional magnetic resonance imaging (fMRI) (1)
- Gradient Coil (1)
- Integrated information theory (IIT) (1)
- Magnetic Resonance Imaging (1)
- Neural correlates of consciousness (1)
- Primary Care (1)
- Primary Health Care (1)
- Propofol (1)
- Radiofrequency Coil (1)
- Resting-state networks (1)
- Sedation (1)
- Shim Coil (1)
- Stream Function (1)
Articles 1 - 4 of 4
Full-Text Articles in Medicine and Health Sciences
Respiratory Pattern Analysis For Covid-19 Digital Screening Using Ai Techniques, Annita Tahsin Priyoti
Respiratory Pattern Analysis For Covid-19 Digital Screening Using Ai Techniques, Annita Tahsin Priyoti
Electronic Thesis and Dissertation Repository
Corona Virus (COVID-19) is a highly contagious respiratory disease that the World Health Organization (WHO) has declared a worldwide epidemic. This virus has spread worldwide, affecting various countries until now, causing millions of deaths globally. To tackle this public health crisis, medical professionals and researchers are working relentlessly, applying different techniques and methods. In terms of diagnosis, respiratory sound has been recognized as an indicator of one’s health condition. Our work is based on cough sound analysis. This study has included an in-depth analysis of the diagnosis of COVID-19 based on human cough sound. Based on cough audio samples from …
Developing Artificial Intelligence And Machine Learning To Support Primary Care Research And Practice, Jacqueline K. Kueper
Developing Artificial Intelligence And Machine Learning To Support Primary Care Research And Practice, Jacqueline K. Kueper
Electronic Thesis and Dissertation Repository
This thesis was motivated by the potential to use "everyday data", especially that collected in electronic health records (EHRs) as part of healthcare delivery, to improve primary care for clients facing complex clinical and/or social situations. Artificial intelligence (AI) techniques can identify patterns or make predictions with these data, producing information to learn about and inform care delivery. Our first objective was to understand and critique the body of literature on AI and primary care. This was achieved through a scoping review wherein we found the field was at an early stage of maturity, primarily focused on clinical decision support …
Magnetic Resonance Systems Development For Point-Of-Care Mri Platforms, Eric J. Lessard
Magnetic Resonance Systems Development For Point-Of-Care Mri Platforms, Eric J. Lessard
Electronic Thesis and Dissertation Repository
Magnetic resonance imaging utilizes electromagnets to produce anatomical images in both clinical and research settings. In the race towards increasing performance head-optimized scanners have begun playing a significant role in providing high quality imaging of the head. However, they are implemented using smaller geometries and as such fail to allow entrance of the patient past their shoulders. This is overcome by designing asymmetric gradient coils which have their imaging region located towards one end of the gradient coil, as opposed to the geometric center, allowing brain imaging. There exists interest in compact configurations which allow imaging further into the cervical …
An Implementation Of Integrated Information Theory In Resting-State Fmri, Idan E. Nemirovsky
An Implementation Of Integrated Information Theory In Resting-State Fmri, Idan E. Nemirovsky
Electronic Thesis and Dissertation Repository
Integrated Information Theory (IIT) is a framework developed to explain consciousness, arguing that conscious systems consist of interacting elements that are integrated through their causal properties. In this study, we present the first application of IIT to functional magnetic resonance imaging (fMRI) data and investigate whether its principal metric, Phi, can meaningfully quantify resting-state cortical activity patterns. Data was acquired from 17 healthy subjects who underwent sedation with propofol, a short acting anesthetic. Using PyPhi, a software package developed for IIT, we thoroughly analyze how Phi varies across different networks and throughout sedation. Our findings indicate that variations in Phi …