{"id":4951,"date":"2022-04-06T16:30:04","date_gmt":"2022-04-06T20:30:04","guid":{"rendered":"https:\/\/www.med.unc.edu\/ppmh\/?p=4951"},"modified":"2023-02-14T17:07:55","modified_gmt":"2023-02-14T22:07:55","slug":"machine-learning-tools-series-may-2022","status":"publish","type":"post","link":"https:\/\/www.med.unc.edu\/ppmh\/2022\/04\/machine-learning-tools-series-may-2022\/","title":{"rendered":"Machine Learning Tools for Clinical Researchers: A Pragmatic Approach Series | May 11, 18, 25, 2022"},"content":{"rendered":"

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Machine learning analysis methods offer the opportunity to integrate and learn from large amounts of biological, clinical, and environmental data, and there is a growing interest in how these tools can be used to inform and individualize clinical decision making in a variety of disease areas. Machine learning can offer different, yet often complementary, insights compared to traditional statistical analyses to better understand heterogeneity in patient presentation, prognoses, and treatment response, generating critical data for precision medicine research. These methods can allow integration across diverse data types and large feature sets, overcoming some limitations of traditional tools to answer clinical questions. However, many clinical researchers have little exposure to machine learning methods, presenting a barrier to utilization of these tools themselves and\/or to effective collaboration with methodologists in their own research.<\/p>\n

The objectives of this series are to:<\/p>\n