Transforming telemedicine through big data analytics
M Coakley, G Crocetti, P Dressner, W Kellum, T Lamin
A look at how big data is transforming telemedicine to provide better care by tapping into a larger source of patient information. Telemedicine will have a profound impact on patient care, increase access and quality, and represent an opportunity to keep health care costs down. Data generated by smart devices will enable the real-time monitoring of chronic diseases, allowing optimal dosage of drugs and improve patient outcomes
Several Emerging Information Technology Topics and Related Doctoral Dissertation Ideas
S Cicoria, J Sherlock, M Muniswamaiah, L Clarke, G Crocetti, M Coakley, ...
Seidenberg School of CSIS, Pace University, White Plains, New York, D1. 1-D1 8
Information technology is emerging on many fronts. Spectators, scientists and researchers are anticipating a faster pace of technological advancement turning our day to day interaction into a mesh of connected devices. Internet of Things (IoT) is where we are driving toward, and these connected ‘things’ encapsulate four main areas. Identity and how this is turning into a service to accommodate the upcoming wave of connected devices, wearable devices, Crypto-Currency which is the futuristic currency flowing between these emerging technologies and the new emerging concept of big data.
A Multivariate Biomarker for Parkinson's Disease
G Crocetti, M Coakley, P Dressner, W Kellum, T Lamin
In this study, we executed a genomic analysis with the objective of selecting a set of genes (possibly small) that would help in the detection and classification of samples from patients affected by Parkinson Disease. We performed a complete data analysis and during the exploratory phase, we selected a list of differentially expressed genes. Despite their association with the diseased state, we could not use them as a biomarker tool. Therefore, our research was extended to include a multivariate analysis approach resulting in the identification and selection of a group of 20 genes that showed a clear potential in detecting and correctly classify Parkinson Disease samples even in the presence of other neurodegenerative disorders
Using Ensemble Models in the Histological Examination of Tissue Abnormalities
G Crocetti, M Coakley, P Dressner, W Kellum, T Lamin
Classification models for the automatic detection of abnormalities on histological samples do exists, with an active debate on the cost associated with false negative diagnosis (underdiagnosis) and false positive diagnosis (overdiagnosis). Current models tend to under diagnose, failing to recognize a potentially fatal disease
The objective of this study is to investigate the possibility of automatically identifying abnormalities in tissue samples through the use of an ensemble modelondata generated by histological examination and to minimize the number of false negative cases.Keywords—Histology, data mining, CART, logistic regression, ensemble model, classification, breast cancer