April 2018CIOAPPLICATIONS.COM9Personal devices that capture health related data at growing rates -- such as sleep, electrocardiography (ECG), behavioral data, steps and more -- also present valuable data types that companies and research institutes are using to enable insights and discoveries in health and precision medicine. Diagnostics continues to evolve with the advancement of technologies like machine learning. Specifically, the rapid growth of deep learning methods (a method of machine learning) in imaging diagnostics for decision support at clinical sites in addition to deployments for remote health applications as a first line of diagnosis - examples include Stanford Artificial Intelligence Laboratory's skin-cancer diagnostic product published in Jan 2017 issue of Nature. Genetic information from customers who consent to participate in research is increasingly available and accessible. The combination of genomics and electronic health records/medical history data has benefits in the health and therapeutics space. This data, layered with machine learning techniques, enables new powerful insights where we begin to predict predisposition to disorders, optimize cohorts for clinical trials, understand medication response, design drugs and more. It's an exciting time to be a part of this field.One of the main reasons I joined 23andMe was because of its focus on research and therapeutics development, made possible by customers who consent to share their genetic and phenotypic data for health discoveries, and enabled by the unique research and compute platform at 23andMe. This large-scale research infrastructure enables the data processing and computations (including building and deploying models) for our consumer facing products but also for our research and therapeutics teams.23andMe's consumer business provides health and ancestry reports to customers. Some of the reports predict predisposition to conditions, including the recent Type 2 Diabetes Health Predisposition Report, while lighter `trait' reports could tell you if you're likely to get motion sickness or match musical notes. As more customers consent to participate in research, these models can become increasingly sophisticated. The impact to date and the potential of machine learning in biology, health and therapeutics is vast, as it is for many other sectors including retail, manufacturing, and media. With the right balance of partnerships with technical leaders and growing in-house capabilities, organizations can benefit from the deployment and use of machine learning to enable new discoveries to benefit humanity, gain valuable insights for businesses, and generally allow predictive analytics to play a bigger role in their organizations. With the right balance of partnerships with technical leaders and growing in-house capabilities, organizations can benefit from the deployment and use of machine learning to enable new discoveries
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