On February 13th, CCSA’s Senior Scientist Marie Ann Leyko attended American Association for the Advancement of Science (AAAS) Precision and Personalized Medicine conference. Below is a summary and some key takeaways!


Conference Summary

Precision and Personalized Medicine seeks to tailor disease treatment to an individual’s genetics. The session was divided into three main topics: Personalize Medicine and Big Data, Precision Medicine and Bioethics, and Precision Medicine, Global Reach: Health Solutions from the Big Data Revolution.

With the enormous amount of data collected, beginning with an individual’s state of health down to the molecular level of gene activity and genomic information, the speakers presented their findings for organizing the data to detect patterns, rules, and statistical dependencies.

While several scientists promoted the use of big data for providing global health solutions, concerns about the ethical dimensions of using personal genetic profiling and privacy still remains unresolved. The bioethics issues highlighted were 1) the nature of the public interest in precision medicine and ensuring fair distribution of benefits; 2) the privacy of individuals and the implications of choice in precision medicine initiatives; and 3) personalization of care and the implications for individual responsibility and social solidarity.

News & Tidbits

Dr. Gunnar Ratsch, professor of machine learning and computational biology at Memorial Sloan-Kettering Cancer Center, explained how he and his team mastered the challenges in mining clinical data, particularly clinical notes. The issues that needed to be resolved were reducing the dimensionality of the clinical notes as well as standardization of those notes.

Dr. Ratsch and his team took 2,000,000 clinical notes and reduced them into 100,000 sentences. One sentence represented one observation. If two or more observations were similar he developed an algorithm to identify sentence clusters. Eventually patient similarities were reduced to 10,000 clinical note clusters. The goal of his laboratory is to develop a decision support system utilizing the clinical notes along with genomic information for determining diagnosis as well as to provide suggestions for treatments, and assigning patients to clinical trials.

For more information, check out their website!

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