Using Machine Learning for Personalized Medicine

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The field of medicine is increasingly focusing on "personalized medicine": identifying the specific treatment that is best for Ms Smith, based on all of her attributes. Over the last decade, essentially all of my research has focused on this challenge. My lab and I have used the "machine learning" approach: finding relevant classifiers, based on datasets of historical patients. While standard machine learning systems were sufficient to learn classifiers for some of these tasks, many others require innovative extensions, (a) to find useful patterns in medical images (eg, extending conditional random fields to enable them to effectively locate brain tumors within a patient's MRI scan), or (b) to deal with the high dimensional data in today's "omics" datasets (eg, the 50,000 features in a gene expression microarray, or the 1 million features in a patient's Single Nucleotide Polymorphism [SNP] profile), or (c) to accommodate time sequence data, both the time spectra in fMRI data, and the sequential control components in helping a patient manage her Type I diabetes. We have also designed new foundational technologies -- eg, to efficiently collect the information required to produce effective classifiers ("budgeted learning"), and to develop a tool that learns a patient specific survival distribution (PSSP; think of a Kaplan-Meier curve, specific to Ms Smith herself). Over the next five years, I plan to continue working in this exciting area of enabling personalized medicine by developing the necessary tools. As before, I anticipate some tasks will involve applying standard techniques; these results will still be medically useful, and will help demonstrate machine learning to the medical community, as a way to produce truly personalized treatments. Others tasks will require further extensions to the innovations developed earlier -- such as additions to the PSSP system (eg, using L1-regularization, to identify which features are relevant at each time), and additional novel ways to apply reinforcement learning techniques to manage diabetes, and other chronic diseases. Many diseases, such as most cancers, are extremely heterogeneous -- not just wrt a population of subjects, but also within a single subject. We will explore ways to effectively model such diseases, as these models will be essential to identify the appropriate treatment for the individuals. Given the challenges (and costs) of collecting samples, many of the training datasets will actually be combinations of data from multiple sites. This leads to several problems: batch effects (due to technical differences between the different labs,etc.), and covariate shift (slightly different populations in each sub-study). I will continue to explore ways to detect, and then correct, these subtle, but extremely important, problems. I plan to devote most of my efforts, however, to finding effective ways for learning algorithms to use prior biological knowledge (eg, of metabolomic and/or signaling pathways) to improve the performance of learned classifiers. Given the imperfections inherent in today's pathway information, this will require (in parallel) identifying ways to improve that pathway information. My experience has shown that such information is essential to deal with today's (and even more-so, for tomorrow's) high-dimensional data (of microarrays, SNPs, CNVs, next generation sequencing, etc.), as there will, otherwise, be no way to identify the complex patterns associated with many multi-focal diseases, such as cancer. Hence, such analyses will be required to produce tools for personalized treatment for patients coping with these important diseases.