IBM Watson aligns with 16 health systems and imaging firms to apply cognitive computing to battle cancer, diabetes, heart disease

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IBM Watson Health has formed a medical imaging collaborative with more than 15 leading healthcare organizations. The goal: To take on some of the most deadly diseases. The collaborative, which includes health systems, academic medical centers, ambulatory radiology providers and imaging technology companies, aims to help doctors address breast, lung, and other cancers; diabetes; eye health; brain disease; and heart disease and related conditions, such as stroke. Watson will mine insights from what IBM calls previously invisible unstructured imaging data and combine it with a broad variety of data from other sources, such as data from electronic health records, radiology and pathology reports, lab results, doctors' progress notes, medical journals, clinical care guidelines and published outcomes studies. As the work of the collaborative evolves, Watson's rationale and insights will evolve, informed by the latest combined thinking of the participating organizations.


Solving Large Scale Phylogenetic Problems using DCM2

AAAI Conferences

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.


10 Mobile Health Startups Making You Feel Better - Nanalyze

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In 1983, the IBM PC XT debuted with 128K of RAM and a 10MB hard disk. In that same year, the first mobile phone debuted weighing about 2.5 pounds and with a $4,000 price tag. Fast forward to today and the average person unlocks their smartphone 76-80 times a day and relies on it for every aspect of their lives. These amazing pieces of hardware are millions of times more capable than all of NASA's computing power in the 1960s. Now that we have a supercomputer that never leaves people's sides, maybe it's time that we do some more innovation and see how that device can be used for "mobile health".


Experts see advances in AI helping diagnose, find cures for hard-to-treat diseases

The Japan Times

WASHINGTON – Your next doctor could very well be a bot. And bots, or automated programs, are likely to play a key role in finding cures for some of the most difficult-to-treat diseases and conditions. Artificial intelligence is rapidly moving into health care, led by some of the biggest technology companies and emerging startups using it to diagnose and respond to a raft of conditions. California researchers detected cardiac arrhythmia with 97 percent accuracy on wearers of an Apple Watch with the AI-based Cariogram application, opening up early treatment options to avert strokes. Scientists from Harvard and the University of Vermont developed a machine learning tool -- a type of AI that enables computers to learn without being explicitly programmed -- to better identify depression by studying Instagram posts, suggesting "new avenues for early screening and detection of mental illness."


What to expect in 2014

AITopics Original Links

An artist's impression of the European Space Agency's Rosetta probe, which aims to be the first to land on a comet. Several research groups, including a team led by geneticist Erika Sasaki and stem-cell biologist Hideyuki Okano at Keio University in Tokyo, hope to create transgenic primates with immune-system deficiencies or brain disorders. This could raise ethical concerns, but might bring us closer to therapies that are relevant to humans (mice can be poor models for such disorders). The work will probably make use of a gene-editing method called CRISPR, which saw rapid take-up last year. The European Space Agency's Rosetta spacecraft could become the first mission to land a probe on a comet.