|Details of person submitting|
|Details of abstract|
|Title of abstract||Deep Learning – Unlocking The hidden Potential of Clinical Data|
With a massive influx of multimodality data, the role of data analytics in clinical trials has grown rapidly, the wealth of data generated in clinical research is still underused due to Crowdsourced Data Collection, Lack of data standardization & oversight, “Deep learning” has gained a central position in recent years, a technique with its foundation in artificial neural networks is a powerful tool for Machine Learning where data is filtered through a cascade of multiple layers with each successive layer using the output from the previous one to inform its results. Deep learning model becomes more & more accurate as they process more data, essentially learning from previous results to refine their ability to make correlations and connections as ability to analyze real time instreaming data in structured or unstructured formats and without the need for data experts to write specific queries, enabling development of more data-driven solutions by allowing automatic generation of features that reduce the amount of human intervention and gives early disease signal detection, Advances precision & Evidence based medicine with lower cost & reduce uncertainty in decision-making process by predictive-modeling & Pre-adjudication and offers Data as service (DaaS) with high Return of interest (ROI). At last patient centricity today is a human-machine collaboration that may ultimately become a symbiosis in the coming future & potentially assist in securing a faster time to market.
|First learning objective||Predictive Modelling by Deep Learning|
|Second learning objective||Metaanalysis of Clinical data|
|Third learning objective|
|Job title||Clinical Data Analyst|