health-care data mining
27 views | +0 today
Follow
Your new post is loading...
Your new post is loading...
Rescooped by My Name from Cooking science
Scoop.it!

The Science of Eating: Documentary on Food and Cooking Science (Full Documentary)

The Science of Eating: Documentary on Food and Cooking Science (Full Documentary). 2014 The documentary you will see here along with the other documentaries ...

Via Morgan Brown
more...
No comment yet.
Scooped by My Name
Scoop.it!

Cell - A Nondegenerate Code of Deleterious Variants in Mendelian Loci Contributes to Complex Disease Risk

RT @Alfons_Valencia: ".. Deleterious Variants in Mendelian Loci .&. Complex Disease .." http://t.co/t9iboTf6jl Rzhetsky et al. #comorbidi…
more...
No comment yet.
Scooped by My Name
Scoop.it!

Increasing Stress, Decreasing Empathy: Need Emotional Intelligence

Increasing Stress, Decreasing Empathy: Need Emotional Intelligence | health-care data mining | Scoop.it
Research shows stress is increasing: health problems & business costs. Empathy is decreasing to damage collaboration: The case for emotional intelligence (Stress up, empathy down. Needed: Emotional savvy.
more...
No comment yet.
Scooped by My Name
Scoop.it!

Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients | BMJ

Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients | BMJ | health-care data mining | Scoop.it
Abstract Objective To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England.
more...
No comment yet.
Scooped by My Name
Scoop.it!

Mining complex clinical data for patient safety research: a framework for event discovery

more...
No comment yet.
Rescooped by My Name from Knowmads, Infocology of the future
Scoop.it!

Facebook Building Major Artificial Intelligence System To Understand Who We Are

Facebook Building Major Artificial Intelligence System To Understand Who We Are | health-care data mining | Scoop.it
Facebook is developing deep learning software to understand what its users say and do online

-

Meaningful artificial intelligence has been the aim of computer science since Alan Turing first imagined, in the 1950s, a computer that “passed” as human. Hollywood movies began shortly thereafter to depict computers with human-like intelligence. But, like so many things, artificial intelligence has been much harder to achieve in reality than in the movies.

But following research breakthroughs about five years ago, leading tech companies, including Microsoft, IBM and Google, have begun investing big money in artificial intelligence applications.

.


Via Wildcat2030
more...
Kevin Au's curator insight, October 4, 2013 11:47 AM

Talk about a contrversial topic here guys, as always someone wants to know more about us, even so much to develop their own artificial intelligence system to do so. Ever heard of privacy? How far is too far?

 

C.K

Louie Helm's curator insight, October 4, 2013 11:12 PM

"Following research breakthroughs about five years ago, leading tech companies, including Microsoft, IBM and Google, have begun investing big money in artificial intelligence applications."

KJ's comment, October 12, 2013 9:24 PM
when will one of these companies- ie facebook/google/amazon/cisco/ibm/apple realize they will need to step up and start buying FIBER assets...? its absurd to think as one of these companies that's relying on the internet speeds will continue to DEPEND on ATT or VZ to deliver their BITS
Rescooped by My Name from Analytics & Social media impact on Healthcare
Scoop.it!

The Big Promise of Predictive Analytics in Healthcare

The Big Promise of Predictive Analytics in Healthcare | health-care data mining | Scoop.it

At Duke University’s Fifth Annual Technology and Healthcare Conference, Eric Siegel, founder of Predictive Analytics World and executive editor of the Predictive Analytics Times called new predictive analytical tools “inevitable” disruptions to the way physicians make treatment decisions and patients receive care.

 

Whether you’re at a casino in Las Vegas, or a patient on the active arm of a clinical trial, no knowledge is more coveted than what’s going to happen next.

 

Of course, no one can know with certainty what the future holds – there are far too many variables, known and unknown – but that’s not really the goal of predictive analytics anyway. For his purposes, Eric Siegel defined predictive analytics for conference attendees as “technology that learns from experience – i.e. data – to predict the outcome or behavior of individuals.” But even that definition is a bit deceptive; technology itself is subject to the same chaotic undercurrent that defines the lives of human beings and their machines.

The famous baseball statistician Bill James, who brought scientific analysis and big data to bear on the sport back in the 1970s, began his project by obsessively studying box scores in an attempt to understand why some teams win and others lose. Despite James’s undying interest in hard numbers and percentages as tools for understanding and predicting the game, he always stressed the anomalous factors, and the need to wed traditional player statistics with the more ethereal characteristics the players embody. Things like luck, the effects of playing at home or away, and clutch performances in the bottom of the 9th, with two outs and the bases loaded, turn out to be pretty unpredictable.

This isn’t an attempt to debunk predictive analysis as a marketing tool and a potential route to better health outcomes. The ROIs are written on the walls. But the dramatic increase in the number of people wearing biometric sensors, paired with all of the “listening” or spying campaigns being conducted on social media platforms, to name just two small streams in the flood of new and accessible data, have made certain commercial enterprises increasingly confident about the degree to which they can predict an individual’s behavior.

That capability, always described at conferences as “the holy grail” or, in Siegel’s parlance, “the golden egg,” is starting to make the question of what technology can accurately predict about people less interesting than what it still can’t.

At any rate, Siegel got around to admitting that predictive analysis is “not necessarily [about] predicting individual outcomes,” but is more about segmenting risk levels. The easiest and most basic form of predictive analysis begins with a decision tree. But even before constructing the decision tree, the crucial first step is to prepare the data by organizing it so that two time frames are juxtaposed: historic data on the one hand, and present day data, which companies would like to be able to predict. Siegel says the relationship between past data and present data is analogous to the relationship between present data and future data. Once the data is prepped, the decision tree can take root.

In an example from Chase Bank’s mortgage business, Siegel described the top of the decision tree as an interest rate of <7.94%. By asking a series of yes or no questions, involving income level, total mortgage amount, lone-to-value ratio, etc. etc., Chase was able to very accurately predict an individual’s risk of loan defection.

In healthcare, the idea is that a similar decision tree, based on extensive patient data and clinical drug information might help bring personalized medicine a lot closer to home for many patients. And it might also upend traditional treatment pathways and protocols, since no two people are exactly alike. Siegel said predictive analytics at the patient bedside is “inevitable,” although it could start happening in five years or 20. Not because the technology and methodology isn’t ready for prime time, and not because predictive analysis is too complicated, but because “cultural change is hard…we have to learn to trust the machine.”

The three most promising applications for predictive analytics in the healthcare space, according to Siegel, are in the areas of clinical (diagnosis, outcome prediction, and treatment decision-making); marketing; and insurance coverage. In his presentation, Siegel cited examples of pharma companies who have dabbled in clinical predictive analysis – GSK has experimented with predicting clinical trial enrollment, Pfizer with predicting health outcomes – but Siegel himself hasn’t fully waded into the healthcare industry as of yet. That will change next year; Siegel announced an inaugural healthcare-focused conference that his organization, Predictive Analytics World, will host in Boston next October.

Prediction has come a long way since Nostradamus. Today’s predictive analysis isn’t concerned with causality, for two major reasons. One, it’s often impossible to determine; and two, it’s largely irrelevant. What matters are the correlations, which readily emerge once the datasets grow large enough. The owners of those datasets, or the people and machines that have the best access to them, are in a position of power that will only increase. Toward the beginning of his keynote, Siegel told attendees “your experience today depends on how organizations and companies treat you.” The most unsettling thing about that statement is that it’s probably true.


Via Chatu Jayadewa
more...
No comment yet.
Scooped by My Name
Scoop.it!

The New Outcomes of Care Dashboard for Mortality and Readmissions From Health Data Intelligence Sheds Light on Patient Outcomes 30 Days After Discharge From the Hospital

The New Outcomes of Care Dashboard for Mortality and Readmissions From Health Data Intelligence Sheds Light on Patient Outcomes 30 Days After Discharge From the Hospital | health-care data mining | Scoop.it
Columbus, OH (PRWEB) September 23, 2013 -- The new Outcomes of Care Dashboard contains risk-standardized Heart Attack, Heart Failure, and Pneumonia 30-Day Mortality and Readmission measures that comply with standards for publicly reported outcomes...
more...
No comment yet.
Scooped by My Name
Scoop.it!

Working differently to provide early diagnosis - Deloitte UK Centre for Health Solutions | Deloitte UK

Working differently to provide early diagnosis - Deloitte UK Centre for Health Solutions | Deloitte UK | health-care data mining | Scoop.it
Early diagnosis of disease is better for patients and makes good economic sense. (How can UK #healthcare work differently with the #diagnostic industry for increased early diagnosis & efficiency?
more...
No comment yet.
Scooped by My Name
Scoop.it!

Best Way to Identify High-Risk Patients Focus of AJMC Study - SBWire (press release)

Best Way to Identify High-Risk Patients Focus of AJMC Study - SBWire (press release) | health-care data mining | Scoop.it
Best Way to Identify High-Risk Patients Focus of AJMC Study
SBWire (press release)
Originally derived to classify comorbidities affecting 1-year mortality in cancer patients, sums weights for 17 specific conditions.
more...
No comment yet.
Scooped by My Name
Scoop.it!

Predictive Analytics, Cloud Computing, Data Mining, PMML: Predictive analytics and the power of open standards and cloud computing

RT @Zementis: Predictive analytics and the power of open standards and #cloud computing http://t.co/qILH7kTRTw #EC2 #AWS #SmartCloud#datami…
more...
No comment yet.
Scooped by My Name
Scoop.it!

Evaluation of the Simplified Comorbidity Score (Colinet) as a prognostic indicator for patients with lung cancer: A cancer registry study

Evaluation of the Simplified Comorbidity Score (Colinet) as a prognostic indicator for patients with lung cancer: A cancer registry study | health-care data mining | Scoop.it
Oncology Medical Article: Evaluation of the Simplified Comorbidity Score (Colinet) as a prognostic indicator for patients with lung cancer: A cancer registry study (Evaluation of the Simplified Comorbidity Score (Colinet) as a prognostic indicator...
more...
No comment yet.
Scooped by My Name
Scoop.it!

Identifying Subgroups of Complex Patients With Cluster Analysis | Page 3 | Page 3

Identifying Subgroups of Complex Patients With Cluster Analysis | Page 3 | Page 3 | health-care data mining | Scoop.it
Cluster analysis can aid in identifying subgroups of patients with similar patterns of comorbid conditions for targeted care management.
more...
No comment yet.
Scooped by My Name
Scoop.it!

Exploring Predictors of Complication in Older Surgical Patients: A Deficit Accumulation Index and the Braden Scale - Cohen - 2012 - Journal of the American Geriatrics Society - Wiley Online Library

Exploring Predictors of Complication in Older Surgical Patients: A Deficit Accumulation Index and the Braden Scale - Cohen - 2012 - Journal of the American Geriatrics Society - Wiley Online Library | health-care data mining | Scoop.it
more...
No comment yet.
Rescooped by My Name from healthcare technology
Scoop.it!

Predictive Analytics: Crunching the Numbers to Deliver Personalized Care

Predictive Analytics: Crunching the Numbers to Deliver Personalized Care | health-care data mining | Scoop.it

Predictive analytics is made possible by the growing ability of health researchers to tap into massive databases, extract relevant information and convey the results to clinicians and their patients – a field known as “big data” because of the number-crunching power and sophisticated data analysis tools necessary to find the “nuggets” of information.

 

In the near future, predictive analytics may be able to help physicians, hospitals and other providers in many ways:


• Identifying high-risk patients at an early stage of disease for better long-term outcomes


• Delivering personalized treatments based on a patient’s genetic or metabolic status


• Determining which patients are most likely to be readmitted to a hospital after treatment


• Developing wellness “prescriptions” personalized to specific individuals


• Charting the complex relationships between chronic health care disorders, such as obesity, diabetes and sleep disorders


But there is at least one important step necessary to capitalize on the promise of predictive analytics: integrating patient information now kept in “siloed” databases in order to get a more complete picture. 

 

While the electronic health record (EHR) is a remarkable IT tool for physicians to capture and analyze patient information, other clinical information now resides in the databases maintained by pharmacies, laboratories and non-traditional providers, such as acupuncturists.  Prior insurance claims may also yield valuable insights into a patient’s medical history.

 

 


Via nrip
more...
No comment yet.
Rescooped by My Name from The MarTech Digest
Scoop.it!

The Power of Prediction: Turning Predictive Analytics into Meaningful Metrics - AMA

The future may be right now but predicting it and how consumers will behave can help organizations better prepare for tomorrow.  That’s where predictive analytics comes in.

 

In this exclusive interview, Eric Siegel, Ph.D., founder of Predictive Analytics World and Executive Editor of the Predictive Analytics Times, shares his insights about predictive analytics and how it can be used to turn Big Data into meaningful metrics.  


Via marketingIO
more...
marketingIO's curator insight, March 7, 2013 10:56 AM

We're still waiting for the genie to jump out of the bottle. Not there yet, but perhaps Big Data provides some guidance to the B2B marketer.


  • See the article at www.marketingpower.com
  • Receive a daily summary of The Marketing Automation Alert directly to your inbox. Subscribe here (your privacy is protected).
  • If you like this scoop, PLEASE share by using the links below.
  • iNeoMarketing merges marketing automation with content marketing for a powerful lead management solution, configured and managed by our knowledgeable, experienced staff.  Contact us.
Scooped by My Name
Scoop.it!

Trends in 1-year survival of people admitted to hospital in Ontario, 1994–2009 - CMAJ

Trends in 1-year survival of people admitted to hospital in Ontario, 1994–2009 CMAJ During this time, patients in hospital became significantly older (median age increased from 51 to 58 yr) and sicker (the proportion with a Charlson comorbidity...
more...
No comment yet.
Scooped by My Name
Scoop.it!

Case Study: Pillai Center Student Describes Benefits: Health, Vitality & Brain Intelligence

LightBody http://www.pillaicenter.com/minilightbodyprogram.aspx Subscribe http://www.youtube.com/subscription_center?add_user?=PillaiCenter Real Life Case St...
more...
No comment yet.