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Articles 1 - 5 of 5
Full-Text Articles in Biomedical Informatics
Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine., Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Ada Bess, Kevin Contrera, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara
Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine., Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Ada Bess, Kevin Contrera, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara
School of Medicine Faculty Publications
Simple Summary: This review was conducted to highlight current and potential applications of artificial intelligence (AI) in the care of patients with head and neck cancer, the barriers to its clinical implementation, and the relative scarcity of research in this field. While AI has demonstrated success in other areas of oncology, its role in head and neck cancer remains limited, with progress slowed by technical, ethical, and logistical challenges. Nevertheless, integrating AI with multiomics approaches presents a promising opportunity to advance precision medicine in this patient population. The findings presented in this manuscript may encourage further multi-institutional studies aimed at …
Deep Learning-Driven Proteomics Analysis For Gene Annotation In The Renin-Angiotensin System, Mortaza Eivazi, Kamran Hosseini, Shahin Alipanahi, Huijing Xia, Luke Restivo, Ayushi Patel, Mahdieh Gozali, Tahereh Ebrahimi, Amy Scarborough, Vahideh Tarhriz, Eric Lazartigues
Deep Learning-Driven Proteomics Analysis For Gene Annotation In The Renin-Angiotensin System, Mortaza Eivazi, Kamran Hosseini, Shahin Alipanahi, Huijing Xia, Luke Restivo, Ayushi Patel, Mahdieh Gozali, Tahereh Ebrahimi, Amy Scarborough, Vahideh Tarhriz, Eric Lazartigues
School of Medicine Faculty Publications
The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RAS gene functions and elucidate their roles in biological pathways. A total of 39,463 RAS-related publications from PubMed and PMC were processed into text format. Feature matrices were generated using TF-IDF and token processing, followed by dimensionality reduction via Principal Component Analysis (PCA). A Multi-Layer Perceptron (MLP) was applied for multi-label classification, with performance evaluated using Precision, F1-Score, Ranking Loss, and ROC-AUC metrics. The model outperformed …
Building Models, Building Capacity: A Review Of Participatory Machine Learning For Hiv Prevention, Mark Sendak, Meg Young, Jee Young Kim, Alifia Hasan, Clare Kelsey, Catherine O’Neal, Tonya Jagneaux, Wayne Wilbright, John Couk, Stephen Lim, Tamachia Davenport, Shirley Lolis, Jennifer Thomas, Shannon Widman, Suresh Balu, Meredith Clement, Lance Okeke
Building Models, Building Capacity: A Review Of Participatory Machine Learning For Hiv Prevention, Mark Sendak, Meg Young, Jee Young Kim, Alifia Hasan, Clare Kelsey, Catherine O’Neal, Tonya Jagneaux, Wayne Wilbright, John Couk, Stephen Lim, Tamachia Davenport, Shirley Lolis, Jennifer Thomas, Shannon Widman, Suresh Balu, Meredith Clement, Lance Okeke
School of Medicine Faculty Publications
A growing number of researchers and practitioners are embracing a “participatory turn” in machine learning (ML) to improve model development, prevent harm, and provide communities more influence over systems that impact them. In this paper, we explore the intersection of participatory practices in healthcare and the emerging focus on responsible AI with a focus on human immunodeficiency virus (HIV) care. We review the historical context of participation in HIV treatment and prevention, emphasizing how patient activism has shaped practices in this field. We then review participatory ML in HIV prevention and present a brief case study of a project designed …
Dynamic Prognosis Prediction For Patients On Dapt After Drug-Eluting Stent Implantation: Model Development And Validation, Fang Li, Laila Rasmy, Yang Xiang, Jingna Feng, Ahmed Abdelhameed, Xinyue Hu, Zenan Sun, David Aguilar, Abhijeet Dhoble, Jingcheng Du, Qing Wang, Shuteng Niu, Yifang Dang, Xinyuan Zhang, Ziqian Xie, Yi Nian, Jianping He, Yujia Zhou, Jianfu Li, Mattia Prosperi, Jiang Bian, Degui Zhi, Cui Tao
Dynamic Prognosis Prediction For Patients On Dapt After Drug-Eluting Stent Implantation: Model Development And Validation, Fang Li, Laila Rasmy, Yang Xiang, Jingna Feng, Ahmed Abdelhameed, Xinyue Hu, Zenan Sun, David Aguilar, Abhijeet Dhoble, Jingcheng Du, Qing Wang, Shuteng Niu, Yifang Dang, Xinyuan Zhang, Ziqian Xie, Yi Nian, Jianping He, Yujia Zhou, Jianfu Li, Mattia Prosperi, Jiang Bian, Degui Zhi, Cui Tao
School of Medicine Faculty Publications
BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum’s de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were …
Pca-Clf: A Classifier Of Prostate Cancer Patients Into Patients With Indolent And Aggressive Tumors Using Machine Learning, Yashwanth Karthik Kumar Mamidi, Tarun Karthik Kumar Mamidi, Md Wasi Ul Kabir, Jiande Wu, Md Tamjidul Hoque, Chindo Hicks
Pca-Clf: A Classifier Of Prostate Cancer Patients Into Patients With Indolent And Aggressive Tumors Using Machine Learning, Yashwanth Karthik Kumar Mamidi, Tarun Karthik Kumar Mamidi, Md Wasi Ul Kabir, Jiande Wu, Md Tamjidul Hoque, Chindo Hicks
School of Medicine Faculty Publications
A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML …