<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Improving the Prediction of Clinical Success Using Machine Learning"^^ . "In pharmaceutical research, assessing drug candidates’ odds of success as they move through clinical\r\nresearch often relies on crude methods based on historical data. However, the rapid progress of\r\nmachine learning offers a new tool to identify the more promising projects. To evaluate its usefulness,\r\nwe trained and validated several machine learning algorithms on a large database of projects. Using\r\nvarious project descriptors as input data we were able to predict the clinical success and failure rates\r\nof projects with an average balanced accuracy of 83% to 89%, which compares favorably with the 56%\r\nto 70% balanced accuracy of the method based on historical data. We also identified the variables that\r\ncontributed most to trial success and used the algorithm to predict the success (or failure) of assets\r\ncurrently in the industry pipeline. We conclude by discussing how pharmaceutical companies can use\r\nsuch model to improve the quantity and quality of their new drugs, and how the broad adoption of\r\nthis technology could reduce the industry’s risk profile with important consequences for industry\r\nstructure, R&D investment, and the cost of innovation."^^ . "2020-10" . . "3" . . . . . . . . . . . . "Jan"^^ . "Niederreiter"^^ . "Jan Niederreiter"^^ . . "Massimo"^^ . "Riccaboni"^^ . "Massimo Riccaboni"^^ . . "Bernard"^^ . "Munos"^^ . "Bernard Munos"^^ . . . . . . "Improving the Prediction of Clinical Success Using Machine Learning (PDF)"^^ . . . . . . "WP_3_2020.pdf"^^ . . "HTML Summary of #4079 \n\nImproving the Prediction of Clinical Success Using Machine Learning\n\n" . "text/html" . . . "HA Statistics"@en . . . "RM Therapeutics. Pharmacology"@en . .