Bio-IT World 2021
Speaker on the Data Science and Analytics Technologies Track.
Presentation Title: Identification of Clinical Response Patterns Through Application of Unsupervised Machine Learning on Clinical Trial Time Series Data
This presentation showcased an innovative application of the popular K-means clustering algorithm to discern novel insights from clinical trial data. By using an unsupervised machine learning approach, distinct patient response patterns, independent of and unbiased by prespecified assumptions, were uncovered. This approach made it possible to compare the entire time-series data, identifying major trends in patient response that emerge naturally from the data.
Unsupervised machine learning was applied on time series data collected from a clinical trial to uncover distinct patterns of patient treatment response. This talk covered the challenges of using unsupervised machine learning on clinical trial data and the technical solutions to overcome these challenges, including data imputation, cluster optimization, secondary analysis, and clinical interpretation of results.
Presentation File
About Bio-IT World
About the Conference
For 20 years, the Bio-IT World Conference & Expo has been the world’s premier event showcasing technologies and analytic approaches that solve problems, accelerate science, and drive the future of precision medicine. Bio-IT World unites a community of leading life sciences, pharmaceutical, clinical, healthcare, informatics and technology experts in the fields of biomedical research, drug discovery & development, and healthcare from around the world.
About the Track
The Data Science and Analytics Technologies track will explore popular data science and analytics tools, technologies, and languages that data scientists are using to gain extra insights and value from data. Presentations will explore becoming a data-driven organization, innovative approaches to data management and analytics, making real impact with data science, and applying data science and tools.