Allied Health Regional Workforce Analysis: Central California

Analyzes the racial/ethnic compositions of workers in twenty-two health occupations and graduates of healthcare education programs in the Central Valley. Examines disparities by race/ethnicity in occupations held, educational attainment, and wages

Allied Health Regional Workforce Analysis: Bay Area Region

Analyzes the racial/ethnic composition of workers in twenty-two health occupations and graduates of healthcare education programs in the Bay Area. Examines disparities by race/ethnicity in the types of occupations held, educational attainment, and wages

The Potential of the Human Connectome as a Biomarker of Brain Disease

The human connectome at the level of fiber tracts between brain regions has been shown to differ in patients with brain disorders compared to healthy control groups. Nonetheless, there is a potentially large number of different network organizations for individual patients that could lead to cognitive deficits prohibiting correct diagnosis. Therefore changes that can distinguish groups might not be sufficient to diagnose the disease that an individual patient suffers from and to indicate the best treatment option for that patient. We describe the challenges introduced by the large variability of connectomes within healthy subjects and patients and outline three common strategies to use connectomes as biomarkers of brain diseases. Finally, we propose a fourth option in using models of simulated brain activity (the dynamic connectome) based on structural connectivity rather than the structure (connectome) itself as a biomarker of disease. Dynamic connectomes, in addition to currently used structural, functional, or effective connectivity, could be an important future biomarker for clinical applications.Comment: Perspective Article for special issue on Magnetic Resonance Imaging of Healthy and Diseased Brain Network

High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands

We applied machine learning to diagnose epilepsy based on the fine-graded spectral analysis of seizure-free (resting state) EEG recordings. Despite using unspecific agglomerated EEG spectra, our fine-graded spectral analysis specifically identified the two EEG resting state sub-bands differentiating healthy people from epileptics (1.5-2 Hz and 11-12.5 Hz). The rigorous evaluation of completely unseen data of 100 EEG recordings (50 belonging to epileptics and the other 50 to healthy people) shows that the approach works successfully, achieving an outstanding accuracy of 99 percent, which significantly outperforms the current benchmark of 70% to 95% by a panel of up to three experienced neurologists. Our epilepsy diagnosis classifier can be implemented in modern EEG analysis devices, especially in intensive care units where early diagnosis and appropriate treatment are decisive in life and death scenarios and where physicians’ error rates are particularly high. Our approach is accurate, robust, fast, and cost-efficient and substantially contributes to Information Systems research in healthcare. The approach is also of high practical and theoretical relevance

The Impact of Emotion Focused Features on SVM and MLR Models for Depression Detection

Major depressive disorder (MDD) is a common mental health diagnosis with estimates upwards of 25% of the United States population remain undiagnosed. Psychomotor symptoms of MDD impacts speed of control of the vocal tract, glottal source features and the rhythm of speech. Speech enables people to perceive the emotion of the speaker and MDD decreases the mood magnitudes expressed by an individual. This study asks the questions: “if high level features deigned to combine acoustic features related to emotion detection are added to glottal source features and mean response time in support vector machines and multivariate logistic regression models, would that improve the recall of the MDD class?” To answer this question, a literature review goes through common features in MDD detection, especially features related to emotion recognition. Using feature transformation, emotion recognition composite features are produced and added to glottal source features for model evaluation

Detection of Abnormalities based on Gamma Wave EEG Signal for Autism Spectrum Disorder

Diagnosing Autism Spectrum Disorder (ASD) by using the traits of abnormalities in their gamma waveform has been proposed in this study to suggest an objective method to detect the disorder using Electroencephalography (EEG) signal. Gamma waveform plays an important role in learning, memory and information processing where it shows slower activities in ASD person compared to a normal person, thus, causing the patients to have trouble in processing knowledge, communicate and pay attention. This study applies Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) to classify the data into normal and abnormal classes. Classification algorithm by PNN was used as a benchmark for the outcomes. The results show that even though PNN and GRNN have similar architecture, but with fundamental difference, the outcomes are different. In this case, PNN performs better than GRNN. To obtain the desired results, we used three and four statistical features (mean, minimum, maximum and standard deviation) for both methods. The outcomes of using PNN with four features are more accurate (99.5% for normal class and 80.5% for abnormal class) compared to only three features. Furthermore, the outcomes of using GRNN with four features also have improvement (95% for normal class and 63.5% for abnormal class) compared to only three features

Detecting somatisation disorder via speech: introducing the Shenzhen Somatisation Speech Corpus

Objective Speech recognition technology is widely used as a mature technical approach in many fields. In the study of depression recognition, speech signals are commonly used due to their convenience and ease of acquisition. Though speech recognition is popular in the research field of depression recognition, it has been little studied in somatisation disorder recognition. The reason for this is the lack of a publicly accessible database of relevant speech and benchmark studies. To this end, we introduce our somatisation disorder speech database and give benchmark results. Methods By collecting speech samples of somatisation disorder patients, in cooperation with the Shenzhen University General Hospital, we introduce our somatisation disorder speech database, the Shenzhen Somatisation Speech Corpus (SSSC). Moreover, a benchmark for SSSC using classic acoustic features and a machine learning model is proposed in our work. Results To obtain a more scientific benchmark, we have compared and analysed the performance of different acoustic features, i. e., the full ComParE feature set, or only MFCCs, fundamental frequency (F0), and frequency and bandwidth of the formants (F1-F3). By comparison. the best result of our benchmark is the 76.0 % unweighted average recall achieved by a support vector machine with formants F1–F3. Conclusion The proposal of SSSC bridges a research gap in somatisation disorder, providing researchers with a publicly accessible speech database. In addition, the results of the benchmark show the scientific validity and feasibility of computer audition for speech recognition in somatization disorders