Synthetic learner: model-free inference on treatments over time Machine Learning

Understanding of the effect of a particular treatment or a policy pertains to many areas of interest -- ranging from political economics, marketing to health-care and personalized treatment studies. In this paper, we develop a non-parametric, model-free test for detecting the effects of treatment over time that extends widely used Synthetic Control tests. The test is built on counterfactual predictions arising from many learning algorithms. In the Neyman-Rubin potential outcome framework with possible carry-over effects, we show that the proposed test is asymptotically consistent for stationary, beta mixing processes. We do not assume that class of learners captures the correct model necessarily. We also discuss estimates of the average treatment effect, and we provide regret bounds on the predictive performance. To the best of our knowledge, this is the first set of results that allow for example any Random Forest to be useful for provably valid statistical inference in the Synthetic Control setting. In experiments, we show that our Synthetic Learner is substantially more powerful than classical methods based on Synthetic Control or Difference-in-Differences, especially in the presence of non-linear outcome models.

CMS competition seeks predictive AI apps for better health outcomes


The Centers for Medicare and Medicaid Services has launched a new contest it hopes will speed the development of new artificial intelligence technologies that can better predict health outcomes and boost quality of care. WHY IT MATTERS CMS says the Artificial Intelligence Health Outcomes Challenge – announced by the agency on Wednesday, in partnership with American Academy of Family Physicians and the Laura and John Arnold Foundation – seeks to uncover and "unleash" new and innovative tools to help with the push toward value-based care. To do that, CMS is calling on developers from all industries to create new predictive AI applications to help providers participating in CMS Innovation Center models to deliver better care and make quality measures more impactful. "The Artificial Intelligence Health Outcomes Challenge is a three stage competition that will begin with the Launch Stage, in which participants will submit an application at," "Up to 20 participants will be selected to participate in Stage 1 of the Challenge. We anticipate that more information about Stage 1 and Stage 2 will be announced later this year."

Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions Machine Learning

We augment linear Support Vector Machine (SVM) classifiers by adding three important features: (i) we introduce a regularization constraint to induce a sparse classifier; (ii) we devise a method that partitions the positive class into clusters and selects a sparse SVM classifier for each cluster; and (iii) we develop a method to optimize the values of controllable variables in order to reduce the number of data points which are predicted to have an undesirable outcome, which, in our setting, coincides with being in the positive class. The latter feature leads to personalized prescriptions/recommendations. We apply our methods to the problem of predicting and preventing hospital readmissions within 30-days from discharge for patients that underwent a general surgical procedure. To that end, we leverage a large dataset containing over 2.28 million patients who had surgeries in the period 2011--2014 in the U.S. The dataset has been collected as part of the American College of Surgeons National Surgical Quality Improvement Program (NSQIP).

Consumerism will force healthcare's hand on interoperability, Forrester finds at HIMSS19


Forrester Research has published a report summing up its impressions from the HIMSS19 Global Conference & Exhibition. Experts said they came away from the show convinced that big momentum is building behind interoperability, and it's not coming from the places one might expect. Health systems will need to do better with the management and sharing of more data than ever if they hope to stay competitive in a value-based care landscape where patients have more choice than ever about where they get their care, according to the study. WHY IT MATTERS As interoperability continues to gain steam, it's set to boost the profiles of an array of other key technologies, said Forrester researchers. At HIMSS19, it was clear that tools "supporting data management and interoperability, including cloud and AI, showcased their ability to add value and hit on the quadruple aim: improving the customer experience, driving better outcomes, lowering costs, and supporting the whole care team," they said.

Why AI will make healthcare personal


For generations healthcare has been episodic – someone gets sick or breaks a bone, they see a doctor, and then they might not see another one until the next time they get sick or injured. Now, as emerging technologies such as artificial intelligence open up new possibilities for the healthcare industry in the Fourth Industrial Revolution, policymakers and practitioners are developing new ways to deliver continuous healthcare for better outcomes. Consumers already expect access to healthcare providers to be as smart and easy as online banking, retrieving boarding passes and making restaurant reservations, according to Kaiser Permanente CEO Bernard J Tyson. Nearly three-quarters of Americans with health insurance (72%), for example, say it's important that their health insurance provider uses modern communication tools, such as instant message and two-way video. Innovative healthcare organizations such as Kaiser Permanente are listening.


Ever since Amazon turned its gaze to healthcare, questions about the eCommerce operator's plans and intentions have outnumbered answers. That's still true, and may remain so for the time being, but this week new details emerged that, at the least, is shedding light on Amazon's healthcare vision. Here's the scoop, courtesy of court testimony: Amazon wants to, in the words of The Wall Street Journal, "redesign health insurance." And that's only one possible aim -- others involve using artificial intelligence to improve diagnoses and the overall patient experience, but we'll get to that in a bit. Among the biggest ongoing mysteries for even the most sophisticated Amazon watchers is the specific purpose of the independent healthcare company that includes Amazon, Berkshire Hathaway and JPMorgan Chase, a venture that was announced last year.

China: Big data & AI to help combat health insurance fraud


The announcement came after Hu Jinglin, director of the National Healthcare Security Administration, made a pledge during the ongoing annual two sessions in Beijing that it will severely crack down on fraudulent practices affecting the country's healthcare insurance funds. The health insurance sector has long been plagued with various fraud cases, like fabricated medical services and documents and fake invoices. Last November, two hospitals in Shenyang, capital of northeast China's Liaoning Province, were accused of insurance scams, which prompted the regulators to audit other medical institutions. While much progress has been made since supervision was tightened, the overall situation remains "grim", so it will be a top priority for the authorities to carry on the fight against insurance fraud in 2019. The administration will partner with more third-party service providers like Ping An HealthKonnect to identify potential fraud risks and gain better control of medical insurance costs by leveraging their advanced technologies in areas of big data and artificial intelligence.

Addressing the promises and challenges of AI


A three-day celebration event this week for the MIT Stephen A. Schwarzman College of Computing put focus on the Institute's new role in helping society navigate a promising yet challenging future for artificial intelligence (AI), as it seeps into nearly all aspects of society. On Thursday, the final day of the event, a series of talks and panel discussions by researchers and industry experts conveyed enthusiasm for AI-enabled advances in many global sectors, but emphasized concerns -- on topics such as data privacy, job automation, and personal and social issues -- that accompany the computing revolution. Kicking off the day's events, MIT President Rafael Reif said the MIT Schwarzman College of Computing will train students in an interdisciplinary approach to AI. It will also train them to take a step back and weigh potential downsides of AI, which is poised to disrupt "every sector of our society." "Everyone knows pushing the limits of new technologies can be so thrilling that it's hard to think about consequences and how [AI] too might be misused," Reif said.

ML helps health plans tackle SDOH, improve outcomes


With the passage of the Chronic Care Act, Medicare Advantage plans have been scrambling to figure out how to offer supplemental benefits to their members. Passed as part of a Bipartisan Budget Act last year, the Chronic Care Act promotes the use of benefits that maintain health or keep a beneficiary's health from deteriorating, and the benefits don't have to be health-related. Instead, they can include help for social determinants of health that include housing, nutrition and transportation. Under the act, the supplements can also be tailored to the individual, when it comes to qualifications. The same benefits don't have to be offered to every beneficiary, he says.

H2O Users Share Data Science Stories


When it comes to analytics tools, data scientists have a plethora of options available to them. Features that may appeal to one data scientist don't necessarily work for another. When it comes to offerings from, users expressed different reasons for their choices. Last week, Datanami was a guest at's annual user conference, called H2O World, and had a chance to talk with several customers, including Ruben Diaz, a data scientist with Vision Banco, and Bharath Sudharsan, director of data science and innovation at Armada Health. Vision Banco is one of Paraguay's largest banks, with consumer and micro-finance lines of business.