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.


DARPA Sets Stage for Giant Leap Forward in Machine Learning

Probabilistic Programming for Advanced Machine Learning
Courtesy of

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

Machine Learning Software Grades Essays and Gives Students Feedback—Instantly

EdX, a nonprofit enterprise founded by Harvard and the Massachusetts Institute of Technology, will release automated software that uses artificial intelligence to grade student essays and short written answers.
EdX, a nonprofit enterprise founded by Harvard and the Massachusetts Institute of Technology, will release automated software that uses artificial intelligence to grade student essays and short written answers.
Courtesy of Gretchen Ertl for The New York Times

John Markoff at the New York Times reports on a fast-moving, back-and-forth exchange where students submit their essays online, receive a grade almost immediately, and  improve their grades based on system-generated feedback.

EdX, a nonprofit consortium of Harvard and the Massachusetts Institute of Technology that offers courses on the Internet,  has developed the automated essay-scoring software powering this new reality.

While controversy rages over the reliability of artificial intelligence to grade essays, EdX software is free to any institution that wants to offer its courses online. So far, the program has been adopted by 12 prestigious universities and it is spreading rapidly worldwide.

Proponents of the software argue that instant feedback is an invaluable learning aid to students versus waiting weeks for professor-graded feedback. Moreover, students find it engaging in much the same way as video games and claim they learn better from the process.

Critics counter that even with the best machine learning algorithms in place; computers cannot perform the essentials of assessing written communication. Les Perelman, a researcher at MIT, has tricked such grading systems into awarding high grades with nonsensical submissions.

A group of educators to which he belongs known as Professionals Against Machine Scoring of Student Essays in High-Stakes Assessment, has collected nearly 2,000 signatures and makes the case that “Computers cannot ‘read.’ They cannot measure the essentials of effective written communication: accuracy, reasoning, adequacy of evidence, good sense, ethical stance, convincing argument, meaningful organization, clarity, and veracity, among others.”

The EdX program has human graders assess the first 100 essays or essay questions. From then on, the system uses various machine-learning algorithms to train itself automatically. Once trained, it can grade any number of essays or answers in near real time. The software lets the teacher create the scoring system based on letter grades or numerical rankings.

Dr. Anant Agarwal, president of EdX, believes the program is approaching the capability of human graders. Skeptics point out how formal studies comparing the system against qualified human graders have not been done. Nevertheless, Dr. Agarwal claims the quality of EdX grading is as consistent as that found from one instructor to another.

Instant, automated feedback has its adherents elsewhere as well, including start-ups Coursera and Udacity. Both are funded by Stanford faculty members as part of their mission to create “massive open online courses,” or MOOCs.

Coursera founder, Daphne Koller, believes instant feedback turns learning into a game students feel compelled to master where they resubmit their work until they achieve a certain level of proficiency.

So, if automated grading is possible in academic settings, the general idea of assessing new written content based on previous human assessments of existing content is sure to explode over the next few years.

Applications that mine blogs, social media and forum postings to understand markets and communities come to mind.

What do you see happening in your field once automated interpretation of extended passages of text goes mainstream?