Isaac Kohane, MD, PhD is the inaugural Chair of the Department of Biomedical Informatics at Harvard Medical School. He develops and applies computational techniques to address disease at multiple scales: From whole healthcare systems as “living laboratories” to the functional genomics of neurodevelopment with a focus on autism. Kohane’s i2b2 project is currently deployed internationally to over 120 major academic health centers to drive discovery research in disease and pharmacovigilance (including providing evidence on drugs which ultimately contributed to “black box”ing by the FDA). Dr. Kohane has published several hundred papers in the medical literature and authored a widely used book on Microarrays for an Integrative Genomics. He is a member of the Institute of Medicine and the American Society for Clinical Investigation.
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Suicide rate in Finland high despite high ranking in “happiness” @NAChristakis does this t.co/jKEUZl63Wb analysis resonate with you and #Blueprint t.co/nv5TofYIMX
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@NAChristakis @tomstandage @DanTGilbert More critical issue is how again and again Star Destroyers studded with ack-ack emplacements are destroyed by handfuls of antiquated X-wing fighters. Designers dead or traitors?
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Well, I’m certainly convinced of its effect on *impact factor* (and investing). Here’s hoping that the next decade will be the one that demonstrates actual impact on patients. (And I would be genuinely thrilled if this includes deployment data from the DR alg in India.) t.co/7ygvlFLii2
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@jflier @DrMJoyner @Maratosflier "Lev Zeppelin" the Jewish-Ukrainian cover/fan band? Будинки Святі is admittedly viral.
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Causal inference (CI), in contrast, is a way to get unbiased estimates of an intervention under *very* strong assumptions. There is likely confusion around CI in the ML community. I worry that some might be "disappointed" when they see the assumptions that are required.
