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Articles 1 - 6 of 6
Full-Text Articles in Biomedical
Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)
Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)
Electrical & Computer Engineering Faculty Publications
According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …
Heart Disease Prediction Using Stacking Model With Balancing Techniques And Dimensionality Reduction, Ayesha Noor, Nadeem Javaid, Nabil Alrajeh, Babar Mansoor, Ali Khaqan, Safdar Hussain Bouk
Heart Disease Prediction Using Stacking Model With Balancing Techniques And Dimensionality Reduction, Ayesha Noor, Nadeem Javaid, Nabil Alrajeh, Babar Mansoor, Ali Khaqan, Safdar Hussain Bouk
School of Cybersecurity Faculty Publications
Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the …
Tpm: Cloud-Based Tele Ptsd Monitor Using Multi-Dimensional Information, Roger Xu, Gang Mei, Guangfan Zhang, Pan Gao, Aaron Pepe, Jiang Li, James D. Westwood (Ed.), Susan W. Westwood (Ed.), Li Felländer-Tsai (Ed.), Randy S. Haluck (Ed.), Richard A. Robb (Ed.), Steven Senger (Ed.), Kirby G. Vosburgh (Ed.)
Tpm: Cloud-Based Tele Ptsd Monitor Using Multi-Dimensional Information, Roger Xu, Gang Mei, Guangfan Zhang, Pan Gao, Aaron Pepe, Jiang Li, James D. Westwood (Ed.), Susan W. Westwood (Ed.), Li Felländer-Tsai (Ed.), Randy S. Haluck (Ed.), Richard A. Robb (Ed.), Steven Senger (Ed.), Kirby G. Vosburgh (Ed.)
Electrical & Computer Engineering Faculty Publications
An automated system that can remotely and non-intrusively screen individuals at high risk for Post-Traumatic Stress Disorder (PTSD) and monitor their progress during treatment would be desired by many Veterans Affairs (VAs) as well as other PTSD treatment and research organizations. In this paper, we present an automated, cloud-based Tele-PTSD Monitor (TPM) system based on the fusion of multiple sources of information. The TPM system can be hosted in a cloud environment and accessed through landline or cell phones, or on the Internet through a web portal or mobile application (app).
A Voice-Based Automated System For Ptsd Screening And Monitoring, Roger Xu, Gang Mei, Guangfan Zhang, Pan Gao, Timothy Judkins, Michael Cannizzaro, Jiang Li, James D. Westwood (Ed.), Susan W. Westwood (Ed.), Li Felländer-Tsai (Ed.), Randy S. Haluck (Ed.), Richard A. Robb (Ed.), Steven Senger (Ed.), Kirby G. Vosburgh (Ed.)
A Voice-Based Automated System For Ptsd Screening And Monitoring, Roger Xu, Gang Mei, Guangfan Zhang, Pan Gao, Timothy Judkins, Michael Cannizzaro, Jiang Li, James D. Westwood (Ed.), Susan W. Westwood (Ed.), Li Felländer-Tsai (Ed.), Randy S. Haluck (Ed.), Richard A. Robb (Ed.), Steven Senger (Ed.), Kirby G. Vosburgh (Ed.)
Electrical & Computer Engineering Faculty Publications
Comprehensive evaluation of PTSD includes diagnostic interviews, self-report testing, and physiological reactivity measures. It is often difficult and costly to diagnose PTSD due to patient access and the variability in symptoms presented. Additionally, potential patients are often reluctant to seek help due to the stigma associated with the disorder. A voice-based automated system that is able to remotely screen individuals at high risk for PTSD and monitor their symptoms during treatment has the potential to make great strides in alleviating the barriers to cost effective PTSD assessment and progress monitoring. In this paper we present a voice-based automated Tele-PTSD Monitor …
Automatic Diagnosis For Prostate Cancer Using Run-Length Matrix Method, Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, Frederic Mckenzie, Nico Karssemeijer (Ed.), Maryellen L. Giger (Ed.)
Automatic Diagnosis For Prostate Cancer Using Run-Length Matrix Method, Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, Frederic Mckenzie, Nico Karssemeijer (Ed.), Maryellen L. Giger (Ed.)
Electrical & Computer Engineering Faculty Publications
Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist …
Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)
Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)
Electrical & Computer Engineering Faculty Publications
We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features …