Machine Learning Is Everywhere: Netflix, Personalized Medicine, and Fraud Prevention Udacity

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"The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions." The potential implications for modern healthcare are almost literally staggering. A look at some of the talks from another recent conference--"Machine Learning for Personalized Medicine" (which is held as a satellite meeting of the European Human Genetics Conference, and which took place last week in Barcelona, Spain)--gives a bit of a window into the kinds of things already underway: The MLPM (Machine Learning for Personalized Medicine) organization is committed to growing a new generation of Machine Learning scientists who will "develop and employ the computational and statistical tools that are necessary to enable personalized medical treatment of patients according to their genetic and molecular properties and who are aware of the scientific, clinical and industrial implications of this research."


Machine Learning Is Everywhere: Netflix, Personalized Medicine, and Fraud Prevention Udacity

#artificialintelligence

"The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions." The potential implications for modern healthcare are almost literally staggering. A look at some of the talks from another recent conference--"Machine Learning for Personalized Medicine" (which is held as a satellite meeting of the European Human Genetics Conference, and which took place last week in Barcelona, Spain)--gives a bit of a window into the kinds of things already underway: The MLPM (Machine Learning for Personalized Medicine) organization is committed to growing a new generation of Machine Learning scientists who will "develop and employ the computational and statistical tools that are necessary to enable personalized medical treatment of patients according to their genetic and molecular properties and who are aware of the scientific, clinical and industrial implications of this research."


Study finds use of bots and misleading posts on social media has exploded globally in the last year

Daily Mail - Science & tech

'Junk news' and fake accounts are rapidly spreading across the globe. A new study from Oxford University has found just how common these practices have become on social media, with researchers estimating that online'manipulation campaigns' were carried out by government and political parties in 48 countries in the last year. That's up from the 28 countries they identified in a study examining social media activity between 2016 and 2017. A new study from Oxford University has found that online'manipulation campaigns' were carried out by government and political parties in 48 countries in the last year'The majority of growth comes from political parties who spread disinformation and junk news around election periods,' said Samantha Bradshaw, a co-author of the report, in a statement. 'There are more political parties learning from the strategies deployed during Brexit and the US 2016 Presidential election: more campaigns are using bots, junk news and disinformation to polarise and manipulate voters.'


Massive Machine Learning Study Demonstrates Gender Stereotyping And Sexist Language In Literature

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An unsupervised machine learning study presented at the 2019 meeting of Association for Computational Linguistics--which examined 3.5M books published between 1900 and 2008--indicates that men are described based on their behavior, where women are described based on appearance. In specific, words like "beautiful" and "sexy" are two of the adjectives most frequently used to describe women, while common descriptors for men were "brave," "rational," and "righteous." The books, which amounted to approximately 11B words in sum, included a mix of fiction and non-fiction. "We are clearly able to see that the words used for women refer much more to their appearances than the words used to describe men," said University of Copenhagen computer scientist and assistant professor Isabelle Augenstein in a statement. "Thus, we have been able to confirm a widespread perception, only now at a statistical level."


Doctors 'vastly outperform' symptom checker apps - Health News - NHS Choices

AITopics Original Links

A US study ran a head-to-head comparison between doctors and a series of symptom checkers using what are known as clinical vignettes. Clinical vignettes have been used for many years to help hone trainee doctors' diagnostic skills. They are essentially diagnostic puzzles based on real-life case reports designed to test training and clinical knowledge. The researchers provided 45 clinical vignettes to more than 200 doctors. They found doctors were twice as likely to diagnose accurately first time compared with online symptom-checking applications.