Bioinformatics Commons^™

Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman

Undergraduate Research Posters

Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …

Computational Approaches To Understand Chemoresistance & Tumor Evolution Using Longitudinal Clinical Data And Lineage Tracing, Sahil Seth

Dissertations & Theses (Open Access)

Tumors are highly heterogeneous and dynamic, continually adapting and evolving in response to their microenvironment as well as external perturbations. Multi-region (spatial) and single cell sequencing has enabled us to anatomize the heterogeneity further and provide evidence of its association with chemo and drug resistance. To investigate this further we took two different approaches to understand the chemo-resistance, and functional heterogeneity in Triple negative breast cancer (TNBC) and Pancreatic ductal carcinoma in situ (PDAC) from an evolutionary perspective.

The first approach was to leverage tumor profiling from an ongoing randomized clinical trial in triple-negative breast cancer (ARTEMIS) to assess mechanisms …

Enhancing Timeliness Of Drug Overdose Mortality Surveillance: A Machine Learning Approach, Patrick J. Ward, Peter J. Rock, Svetla Slavova, April M. Young, Terry L. Bunn, Ramakanth Kavuluru

Kentucky Injury Prevention and Research Center Faculty Publications

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.

METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created …