Machine Learning Touches All Aspects of Medical Care

Jennifer Barrett
Courtesy of George Mason University

Writing for Mason Research at George Mason University, Michele McDonald reports on how machine learning is helping doctors determine the best course of treatment for their patients. What’s more, machine learning is improving efficiency in medical billing and even predicting patients’ future medical conditions.

Using complex algorithms to mine the data, individualized medicine becomes possible according to Janusz Wojtusiak, director of the Machine Learning and Inference Laboratory and the Center for Discovery Science and Health Informatics at Mason’s College of Health and Human Services.

Wojtusiak points out how current research and studies focus on the average patient whereas those being treated want personalized care at the lowest risk for the best outcome.

Machine learning can identify patterns in reams of data and place the patient’s conditions and symptoms in context to build an individualized treatment model.

As such, machine learning seeks to support the physician based on the history of the condition as well as the history of the patient.

The data to be mined is vast and detailed. It includes the lab tests, diagnoses, treatments, and qualitative notes of individual patients who, taken together, form large populations.

Machine learning uses algorithms that recognize the data, identify patterns in it and derive meaningful analyses.

For example, researchers at the Machine Learning and Inference Lab are comparing five different treatment options for patients with prostate cancer.

To determine the best treatment option, machine learning must first categorize prostate cancer patients on the basis of certain commonalities. When a new patient comes in, algorithms can figure out which group he is most similar to. In turn, this guides the direction of treatment for that patient.

Given the high stakes consequences involved with patient care, the complexity that must be sorted out when making diagnoses and the ongoing monitoring of interventions against outcomes, machine learning development in health care is risk-mitigating and cost-effective.

For more about The Machine Learning and Inference Lab and the health care pilot projects they are working on, see the original article here.

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DARPA Sets Stage for Giant Leap Forward in Machine Learning

Probabilistic Programming for Advanced Machine Learning
Courtesy of DARPA.mil

As the new frontier in computing. machine learning brings us software that can make sense of big data, act on its findings and draw insights from ambiguous information.

Spam filters, recommendation systems and driver assistance technology are some of today’s more mainstream uses of machine learning.

Like life on any frontier, creating new machine learning applications, even with the most talented of teams, can be difficult and slow for a lack of tools and infrastructure.

DARPA (The Defense Advanced Research Projects Agency) is tackling this problem head on by launching the Probabilistic Programming for Advanced Machine Learning Program (PPAML).

Probabilistic programming is a programming paradigm for dealing with uncertain information.

In much the same way that high level programming languages spared developers the need to deal with machine level issues, DARPA’s focus on probabilistic programming sets the stage for a quantum leap forward in machine learning.

More specifically, machine learning developers using new programming languages geared for probabilistic inference will be freed up to deliver applications faster that are more innovative, effective and efficient while relying less on big data, as is common today.

For details, see the DARPA Special Notice document describing the specific capabilities sought at http://go.usa.gov/2PhW.