Two publications from our data science faculty have recently been published in high-profile journals.
First, a feasibility study to determine the optimum blood pressure of cardiac patients during surgery appeared in British Journal of Anesthesia (BJA: impact factor 9.1, ranking it 2 out of 33 in Anesthesiology at the time of this writing) in December. An earlier blog post described the preliminary work for this article. Shortly after appearing on the BJA website, this work was the topic of an editorial discussing its importance — introducing a bulk, automated process for what was preciously a laborious clinical study procedure that had to be conducted one patient at a time. As described by its senior author, Dr. Domagoj Mladinov, “[this] study demonstrates feasibility of automatically calculating optimal arterial blood pressure based on cerebral autoregulation limits derived from cerebral oximetry during cardiac surgery.” The novelty of this methodology is echoed in the editorial by Hogue and colleagues, “What this report demonstrates is the feasibility of an operator-independent method for monitoring CBF [cerebral blood flow] autoregulation.” Additionally, this work from UAB Anesthesiology and Perioperative Medicine Faculty suggest a methodology for moving autoregulation out of the realm of retrospective studies and toward clinical interventions via a real-time, streaming data analysis platform being actively developed in our department in collaboration with Medical Informatics Corp (MIC, Houston, Texas).
Second, a novel algorithm for for extracting information on community structure from graphs (networks) is in press with Proceedings of the National Academy of Science (PNAS: impact factor 11.2), currently available online. This work — by our Principal Data Scientist in collaboration with his former M.A. thesis advisor at Wake Forest University — suggests a social framework for discussing and calculating the centrality of nodes (participants) in a network. This novel algorithm was cited (page 5) prior to publication by researchers seeking to use the algorithm efficiently on very large networks. As described by the authors, this work demonstrates “how meaningful community structure can be identified without additional inputs (e.g., number of clusters or neighborhood size), optimization criteria, iterative procedures, nor distributional assumptions.” That is, aside from the network itself, an investigator needs no further a priori information to extract the underlying community structure and detect highly central — or important — nodes.
