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


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.

Healthtech catalyzing efforts to achieve Universal Health Coverage


Healthcare technology, aka healthtech, is rapidly transforming the way healthcare services are accessed and delivered across the world, particularly to the vulnerable populations in the low and middle-income countries. Health technologies and interventions are critical elements that expand access to effective and affordable health services whilst simultaneously catalyzing efforts to achieve the goal of Universal Health Coverage (UHC). With the advent of electronic health records or digital records, concerns regarding the security and ownership of the sensitive health data have also arisen. For the medical data to be stored and accessed safely, healthcare providers and consumers are utilizing blockchain, the technology behind cryptocurrencies that significantly increases transparency and security by storing and distributing data to all participants across the entire supply chain. Besides data security, the distributed ledger technology is also being used to curb the menace of drug counterfeiting.

Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes

Neural Information Processing Systems

Predicated on the increasing abundance of electronic health records, we investigate the problem of inferring individualized treatment effects using observational data. Stemming from the potential outcomes model, we propose a novel multi-task learning framework in which factual and counterfactual outcomes are modeled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian method for learning the treatment effects using a multi-task Gaussian process (GP) with a linear coregionalization kernel as a prior over the vvRKHS. The Bayesian approach allows us to compute individualized measures of confidence in our estimates via pointwise credible intervals, which are crucial for realizing the full potential of precision medicine. The impact of selection bias is alleviated via a risk-based empirical Bayes method for adapting the multi-task GP prior, which jointly minimizes the empirical error in factual outcomes and the uncertainty in (unobserved) counterfactual outcomes. We conduct experiments on observational datasets for an interventional social program applied to premature infants, and a left ventricular assist device applied to cardiac patients wait-listed for a heart transplant. In both experiments, we show that our method significantly outperforms the state-of-the-art.

Google given access to healthcare data of up to 1.6 million patients


A company owned by Google has been given access to the healthcare data of up to 1.6 million patients from three hospitals run by a major London NHS Trust. DeepMind, the tech giant's London-based company most famous for its innovative use of artificial intelligence, is being provided with the patient information as part of an agreement with the Royal Free NHS trust, which runs the Barnet, Chase Farm and Royal Free hospitals. It includes information about people who are HIV-positive as well as details of drug overdoses, abortions and patient data from the last five years, according to a report by the New Scientist. DeepMind announced in February that it was developing a software in partnership with NHS hospitals to alert staff to patients at risk of deterioration and death through kidney failure. The technology, which is run through a smartphone app, has the support of Lord Darzi, a surgeon and former health minister who is director of the Institute of Global Health Innovation at Imperial College London.

How AI and machine learning are transforming clinical decision support


"Between 12 to 18 million Americans every year will experience some sort of diagnostic error," said Paul Cerrato, a journalist and researcher. "So the question is: Why such a huge number? And what can we do better in terms of reinventing the tools so they catch these conditions more effectively?" Cerrato is co-author, alongside Dr. John Halamka, newly minted president of Mayo Clinic Platform, of the new HIMSS Book Series edition, Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. At HIMSS20, the two of them will discuss the book, and the bigger picture around CDS tools that are fast being transformed by the advent of artificial intelligence, machine learning and big data analytics.