A large radiology practice in the Miami area is the test bed for the first real-world application of IBM Watson interpreting medical images. Radiology Associates of South Florida, which has more than 75 physicians and handles about 1 million studies per year, is teaming with Baptist Hospital of Miami to apply Watson-powered "cognitive peer review" to medical imaging in an effort to diagnose aortic stenosis earlier. "We want to identify patients at high risk who may have been missed," said Dr. Ricardo Cury, director of cardiac imaging at Baptist Hospital of Miami and chairman and CEO of Radiology Associates. Watson speeds up the peer review process by assisting cardiologists and sonographers in spotting stenosis cases that otherwise might fall through the cracks, Cury explained at the annual meeting of the Radiological Society of North America in Chicago late last month. Watson looks for variations in practice, based on quality metrics and image analytics, explained Jon DeVries, global offering manager for IBM Watson Health Imaging.
Tandy J. Warnow Department of Computer Science University of Arizona Tucson AZ USA email: tandy cs, arizona, edu Abstract In an earlier paper, we described a new method for phylogenetic tree reconstruction called the Disk Covering Method, or DCM. This is a general method which can be used with an)' existing phylogenetic method in order to improve its performance, lCre showed analytically and experimentally that when DCM is used in conjunction with polynomial time distance-based methods, it improves the accuracy of the trees reconstructed. In this paper, we discuss a variant on DCM, that we call DCM2. DCM2 is designed to be used with phylogenetic methods whose objective is the solution of NPhard optimization problems. We also motivate the need for solutions to NPhard optimization problems by showing that on some very large and important datasets, the most popular (and presumably best performing) polynomial time distance methods have poor accuracy. Introduction 118 HUSON The accurate recovery of the phylogenetic branching order from molecular sequence data is fundamental to many problems in biology. Multiple sequence alignment, gene function prediction, protein structure, and drug design all depend on phylogenetic inference. Although many methods exist for the inference of phylogenetic trees, biologists who specialize in systematics typically compute Maximum Parsimony (MP) or Maximum Likelihood (ML) trees because they are thought to be the best predictors of accurate branching order. Unfortunately, MP and ML optimization problems are NPhard, and typical heuristics use hill-climbing techniques to search through an exponentially large space. When large numbers of taxa are involved, the computational cost of MP and ML methods is so great that it may take years of computation for a local minimum to be obtained on a single dataset (Chase et al. 1993; Rice, Donoghue, & Olmstead 1997). It is because of this computational cost that many biologists resort to distance-based calculations, such as Neighbor-Joining (NJ) (Saitou & Nei 1987), even though these may poor accuracy when the diameter of the tree is large (Huson et al. 1998). As DNA sequencing methods advance, large, divergent, biological datasets are becoming commonplace. For example, the February, 1999 issue of Molecular Biology and Evolution contained five distinct datascts of more than 50 taxa, and two others that had been pruned below that.
Using raw data from the entirety of a patient's electronic health record, Google researchers have developed an artificial intelligence network capable of predicting the course of their disease and risk of death during a hospital stay, with much more accuracy than previous methods. The deep learning models were trained on over 216,000 deidentified EHRs from more than 114,000 adult patients, who had been hospitalized for at least one day at either the University of California, San Francisco or the University of Chicago. For those two academic medical centers, the AI predicted the risks of mortality, readmission and prolonged stays, as well as discharge diagnoses, by ICD-9 code. The network was 95% accurate in predicting a patient's risk of dying while in the hospital--with a much lower rate of false alerts--than the traditional regressive model--the augmented Early Warning Score--which measures 28 factors and was about 85% accurate at the two centers. The researchers' findings were published last month in the Nature journal npj Digital Medicine.
If IBM is looking for a new application for its Watson machine learning tools, it might consider putting health care providers' procurement and systems integration woes ahead of curing cancer. The fall-out from that project has now prompted the resignation of the cancer center's president, Ronald DePinho, the Wall Street Journal reported Thursday. The university recently published an internal audit report into the procurement processes that led it to hand almost $40 million to IBM and over $21 million to PwC for work on the project, almost all of it without board approval. It noted that the scope of its review was limited to contracting and procurement practices and compliance issues, and did not cover project management and system development activities. The audit "should not be interpreted as an opinion on the scientific basis or functional capabilities of the system in its current state," because a separate review of those aspects of the project is being conducted by an external consultant, it said.
UC San Francisco is upping its research into advanced computing in healthcare, launching an artificial intelligence center specifically to advance its use in medical imaging. The Center for Intelligent Imaging will develop and apply artificial intelligence in the quest to find new ways to use radiology to look inside the body and to evaluate health and disease. UCSF investigators in the center will work with Santa Clara, Calif-based NVIDIA, which develops AI products to support infrastructure and tools. The collaboration will aim to create new ways to enable the translation of AI into clinical practice. "Artificial intelligence represents the next frontier for diagnostic medicine," says Christopher Hess, MD, chair of UCSF's Department of Radiology and Biomedical Imaging.