data science
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Precision Medicine: Analytics, Data Science and EHRs in the New Age
The promise of genomics and personalized care are closer than many realize. But clinical systems and EHRs are not ready yet. While policymakers and innovators play catch-up, here’s a look at what you need to know. Considering how fast technology advances in the healthcare industry, it seems natural that a once-innovative concept could become obsolete in the span of, say, a dozen years. Knowledge, comprehension and capabilities continue moving forward, and if the instruments of support don't keep pace, it can cause a rift to appear. If nothing is done, it can exacerbate into a seismic event...
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Predictive Data Analytics is Saving Lives and Taxpayer Dollars in New York City
City governments, faced with decreased resources after the Great Recession and rising citizen demand for services with increased urbanization, must be able to make better decisions that are informed by data. To put it another way, in 2012, mayors need to start playing Moneyball in government with evidence-based analysis. Read More »
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Q&A with Andy Oram: How Can We Tell Whether Predictive Analytics Are Biased?
The fear of reproducing society's prejudices through computer algorithms is being hotly discussed in both academic publications and the popular press. Just a few of the publications warning about bias in predictive analytics include the New York Times, the Guardian, the Harvard Business Review, and particularly a famous and hotly contested article by Propublica on predictions of recidivism among criminal defendants...
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Reducing Hospital Readmissions Using Data Science And A Social Twist
Ohio State University Wexner Medical Center uses patient's social setting to improve adherence Read More »
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Robert A. Muenchen
Robert A. Muenchen is the author of R for SAS and SPSS Users, and co-author of R for Stata Users and Introduction to Biomedical Data Science. He is also the creator of r4stats.com, a popular web site devoted to analyzing trends in data science software, reviewing such software, and helping people learn the R language.
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The Appeal of Graph Databases for Health Care
A lot of valuable data can be represented as graphs. Genealogical charts are a familiar example: they represent people as boxes, connected by lines that represent parent/child or marriage relationships. In mathematics and computer science, graphs have become a discipline all their own. Now their value for health care is emerging. Graph computing made a significant advance this past February in the form of a Graph Data Science (GDS) library for the free and open source Neo4j graph database. Graph databases are proving their value in clinical research and public health; I wonder whether they can also boost analytics for providers. This article explains what's special about graph databases, and some applications in health care highlighted by recent webinars offered by the Neo4j company.
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Travis Oliphant to Discuss the Power of the Python Ecosystem and Open Data Science at TDWI Accelerate Boston
Continuum Analytics, the creator and driving force behind Anaconda, the leading Open Data Science platform powered by Python, today announced that Travis Oliphant, chief data scientist and co-founder, will be speaking at TDWI Accelerate Boston on April 4 at 1:30 p.m. EDT. As one of the leading conferences on Big Data and data science, Accelerate brings together the brightest and best data minds in the industry to discuss the future of data science and analytics...
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Using It or Losing It? The Case for Data Scientists Inside Health Care
As much as 30% of the entire world’s stored data is generated in the health care industry. A single patient typically generates close to 80 megabytes each year in imaging and electronic medical record (EMR) data. This trove of data has obvious clinical, financial, and operational value for the health care industry, and the new value pathways that such data could enable have been estimated by McKinsey to be worth more than $300 billion annually in reduced costs alone. If appropriate investments in data science are not made in-house, then hospitals and health systems will run the risk of becoming reliant on outsiders to analyze the data that ultimately will be used to inform decisions and drive innovation”...
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Where HIMSS Can Take Health 2.0
I was quite privileged to talk to the leaders of Health 2.0, Dr. Indu Subaiya and Matthew Holt, in the busy days after their announced merger with HIMSS. I was revving to talk to them because the Health 2.0 events I have attended have always been stimulating and challenging. I wanted to make sure that after their incorporation into the HIMSS empire they would continue to push clinicians as well as technologists to re-evaluate their workflows, goals, and philosophies...
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White House Call to Action to the Tech Community on New Open Access Machine Readable COVID-19 Dataset
Today, researchers and leaders from the Allen Institute for AI, Chan Zuckerberg Initiative (CZI), Georgetown University's Center for Security and Emerging Technology (CSET), Microsoft, and the National Library of Medicine (NLM) at the National Institutes of Health released the COVID-19 Open Research Dataset (CORD-19) of scholarly literature about COVID-19, SARS-CoV-2, and the Coronavirus group. Requested by The White House Office of Science and Technology Policy, the dataset represents the most extensive machine-readable Coronavirus literature collection available for data and text mining to date, with over 29,000 articles, more than 13,000 of which have full text.
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Strata 2014: Making Data Work
Join us at the O'Reilly Strata Conference in Santa Clara to get a clear perspective on the future of big data—as well as all the analytics, architectures, techniques, tools, and technologies you need to use data successfully right now.
The depth and breadth of what's covered at Strata requires 7 different tracks just to organize it all. You can follow one track from beginning to end, or just select the individual sessions that most interest you.
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