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Full-Text Articles in Computer Engineering
Breast Cancer Detection From Histopathology Images Using Machine Learning Techniques: A Bibliometric Analysis, Shubhangi A. Joshi, Anupkumar M. Bongale Dr., Arunkumar M. Bongale Dr.
Breast Cancer Detection From Histopathology Images Using Machine Learning Techniques: A Bibliometric Analysis, Shubhangi A. Joshi, Anupkumar M. Bongale Dr., Arunkumar M. Bongale Dr.
Library Philosophy and Practice (e-journal)
Computer aided diagnosis has become upcoming area of research over past few years. With the advent of machine learning and especially deep learning techniques, the scenario of work flow management in healthcare sector is changing drastically. Artificial intelligence has shown potential in the field of breast cancer care. With datasets for machine learning frameworks getting eventually richer with time, we can definitely get newer insights in the field of breast cancer care. This will help in narrowing down the treatment range for patients and increasing patient survivability. The purpose of this study was to perform bibliometric analysis of the literature …
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Public Health Faculty Publications
The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Engineering Faculty Articles and Research
Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …
Design Of A Distributed Real-Time E-Health Cyber Ecosystem With Collective Actions: Diagnosis, Dynamic Queueing, And Decision Making, Yanlin Zhou
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
In this thesis, we develop a framework for E-health Cyber Ecosystems, and look into different involved actors. The three interested parties in the ecosystem including patients, doctors, and healthcare providers are discussed in 3 different phases. In Phase 1, machine-learning based modeling and simulation analysis is performed to remotely predict a patient's risk level of having heart diseases in real time. In Phase 2, an online dynamic queueing model is devised to pair doctors with patients having high risk levels (diagnosed in Phase 1) to confirm the risk, and provide help. In Phase 3, a decision making paradigm is proposed …