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.


Causal Inference through a Witness Protection Program

arXiv.org Machine Learning

One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest "weak" paths in a unknown causal graph. The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that imply conditional independencies but do not rule out the existence of confounding causal paths. The outcome is a posterior distribution over bounds on the average causal effect via a linear programming approach and Bayesian inference. We claim this approach should be used in regular practice along with other default tools in observational studies.


Smart speakers can analyze a baby's breathing and monitor for infant sleep apnea

Daily Mail - Science & tech

Researchers at the University of Washington have devised a new app for smart speakers like Amazon's Echo to help parents monitor their baby's breathing. Called BreathJunior, the experimental app will be able to measure the rate of a baby's breathing and detect symptoms of sleep apnea. The team initially conducted a test of the device with five babies in the neonatal intensive care unit at a hospital in Washington. BreathJunior (pictured above) is an experimental app that monitors a baby's breathing using a smart speaker According to a report from MIT Tech Review, the team plans to eventually release the app as a commercial product via the company Sound Life Sciences. But first, they'll present the results of the trial at the upcoming MobiCom, a yearly conference on mobile computing in Los Cabos, Mexico.


How 3D Printing and IBM Watson Could Replace Doctors

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Health care executives from IBM Watson and Athenahealth athn debated that question onstage at Fortune's inaugural Brainstorm Health conference Tuesday. In addition to partnering with Celgene celg to better track negative drug side effects, IBM ibm is applying its cognitive computing AI technology to recommend cancer treatment in rural areas in the U.S., India, and China, where there is a dearth of oncologists, said Deborah DiSanzo, general manager for IBM Watson Health. For example, IBM Watson could read a patient's electronic medical record, analyze imagery of the cancer, and even look at gene sequencing of the tumor to figure out the optimal treatment plan for a particular person, she said. "That is the promise of AI--not that we are going to replace people, not that we're going to replace doctors, but that we really augment the intelligence and help," DiSanzo said. Athenahealth CEO Jonathan Bush, however, disagreed.