A "meta-study" or "meta-analysis" analyzes multiple medical studies related to the same disease, treatment protocol, and outcome measurement to identify if there is an overall effect or not (e.g., treatment induces remission or causes adverse effects). It's advantage lies in the pooling and analysis of results across independent studies, which increases the population size, mitigates some experimental bias or inconsistent results from a single study, etc. Meta-studies are important for understanding the effectiveness (or not) of treatment, influencing clinical guidelines and for spurring new research directions. However, meta-studies are extremely time consuming to construct by hand and keep updated with the latest results. This limits both their breadth of coverage (since researchers will only invest the time for diseases they are interested in) and their practically. Yet, high-quality medical research is increasing at a staggering rate, and there is an opportunity to apply automation to this increasing body of knowledge, thereby expanding the benefits of meta-studies to (theoretically) all diseases and treatment, as they are published. That is, we envision, long term an automatic process for creating meta-studies across all diseases and treatments, and keeping those meta-studies up-to-date automatically. In this paper we demonstrate that there is potential to perform this task, point out future research directions to make this so, and, hopefully, spur significant interest in this compelling and important research direction at the intersection of medical research and machine learning.

The need for health providers to share information is a pressing need in our ever more connected world. A patient's health information should seamlessly flow from labs to hospitals to primary care offices. To address this need, in this paper we present the Health E-Match, which focuses on the matching health terms in support of semantic interoperability. Health E-Match determines the semantic similarity between data items, realizing, for instance, that "BHGC (UR)" and "BETA-HCG (QUAL)" both refer to the same pregnancy test, known as "Beta human chorionic gonadotropin, urine qualitative." Our approach is grounded in probabilistic machine learning, and leverages several sophisticated methods for comparing the similarity between medical data items beyond simple edit distance. We present two large scale, real-world experiments to verify that our approach is both accurate and has the ability to eventually be "universal" in that models trained on one set of data translate to strong performance on data from a completely different provider.