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Full-Text Articles in Medicine and Health Sciences
Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho
Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho
SMU Data Science Review
Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …
Examining The Viability Of Computational Psychiatry: Approaches Into The Future, Mitchell Ostrow
Examining The Viability Of Computational Psychiatry: Approaches Into The Future, Mitchell Ostrow
The Yale Undergraduate Research Journal
As modern medicine becomes increasingly personalized, psychiatry lags behind, using poorly-understood drugs and therapies to treat mental disorders. With the advent of methods that capture large quantities of data, such as genome-wide analyses or fMRI, machine learning (ML) approaches have become prominent in neuroscience. This is promising for studying the brain’s function, but perhaps more importantly, these techniques can potentially predict the onset of disorder and treatment response. Experimental approaches that use naive machine learning algorithms have dominated research in computational psychiatry over the past decade. In a critical review and analysis, I argue that biologically realistic approaches will be …
Validation Of A Single Channel Eeg For The Athlete: A Machine Learning Protocol To Accurately Detect Sleep Stages, Kayla Thompson, Kamil Celoch, Frankie Pizzo, Ana I. Fins, Jaime Tartar
Validation Of A Single Channel Eeg For The Athlete: A Machine Learning Protocol To Accurately Detect Sleep Stages, Kayla Thompson, Kamil Celoch, Frankie Pizzo, Ana I. Fins, Jaime Tartar
Journal for Sports Neuroscience
There is a large and growing movement towards the use of wearable technologies for sleep assessment. This trend is largely due to the desire for comfortable, burden free, and inexpensive technology. In tandem, given the competitive nature of professional athletes enduring high training load, sleep is often jeopardized which can result in adverse outcomes. Wearable devices hold the promise of increasing the ease of monitoring sleep in athletes which can inform health and recovery status, as well as aid performance optimization. However, wearable devices typically lack sufficient validity to assess sleep – and especially sleep stages. To address this concern, …
Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova
Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova
SMU Data Science Review
Cardiovascular diseases, Congestive Heart Failure in particular, are a leading cause of deaths worldwide. Congestive Heart Failure has high mortality and morbidity rates. The key to decreasing the morbidity and mortality rates associated with Congestive Heart Failure is determining a method to detect high-risk individuals prior to the development of this often-fatal disease. Providing high-risk individuals with advanced knowledge of risk factors that could potentially lead to Congestive Heart Failure, enhances the likelihood of preventing the disease through implementation of lifestyle changes for healthy living. When dealing with healthcare and patient data, there are restrictions that led to difficulties accessing …