“The innovations we foresee emerging from the Center for Machine Learning and Health at Carnegie Mellon University will inform better healthcare decisions. The CMLH goal is to be at the center of the digital healthcare revolution, with a focus on big data and scientific solutions for managing and improving healthcare through personalized, precision medicine. The researchers at Carnegie Mellon and our partners at UPMC Enterprises are uniquely qualified and determined to turn those ideas into reality.”
Dean, School of Computer Science
Carnegie Mellon University
The Center for Machine Learning and Health (CMLH) at Carnegie Mellon University is one of two centers launched under the umbrella of the Pittsburgh Health Data Alliance, formed in 2015 to unite Carnegie Mellon’s unrivaled applied-computing capabilities, the University of Pittsburgh’s world-class health-sciences research, and UPMC’s clinical care and commercialization expertise. The CMLH supports great science and engineering that can lead to innovative health solutions and new businesses.
The use of data analytics and genomics to provide tailored care to individuals based on their DNA, healthcare history, and environmental, economic, and behavioral factors.
The development of the next generation of medical devices and systems for use by medical staff and patients.
The provision of healthcare services to individuals and populations in hospitals and clinics, at home, and remotely via the internet.
The organizational and business aspects of providing health insurance and healthcare services at scale.
From bench to bedside: the CMLH funds projects that strive to bridge the gap between research and practice. All funded work at CMLH will have a clear line of sight to commercial application. Although many CMLH projects will involve data analytics and machine learning, our approach is technology agnostic. We welcome proposals that involve human-computer interaction, language technologies, information systems, computer graphics, computer vision, artificial intelligence, robotics, electrical engineering, economics, psychology, sociology, public policy, business administration, law, design, and any other disciplines that apply to healthcare.
Funding is available at two levels. Awards for early-stage research projects are typically in the range of $200K – $400K. Significantly larger awards are available for multi-year, follow-on projects that have demonstrated clear commercial potential.
In addition to offering funding, CMLH provides support for funded projects in the form of datasets, access to patients and doctors for empirical validation of new concepts, and entrepreneurial mentorship.
Awards are intended to support research that transforms healthcare from transactional and experience-centered to data-driven. Once selected for funding, CMLH will help identify potential clinical and data-transaction partners and oversee progression to further funding where appropriate.
Proposal submissions are open only to Carnegie Mellon faculty in all units of the university. All proposals should be submitted via email to: firstname.lastname@example.org.
The Clinical Genomics Modeling Platform is an engine for easily building precision-medicine models for various diseases and populations. Triage algorithms, for instance, might help to determine if patients with a certain disease should be sent home with monitoring or sent to the intensive care unit. Carl Kingsford, Ph.D., and Christopher Langmead, Ph.D., both associate professors of computational biology with the School of Computer Science, are leading this development.
Their project focuses on the understanding of the relationship between genetic variation and medical outcomes in a large population, which is key to realizing the vision of personalized medicine. Efforts are now underway to obtain the genome sequences of thousands of individuals, and as the cost of sequencing continues to drop, it will become routine to sequence patients in a medical setting. However, a number of computational and practical challenges remain in the way of using genomic sequencing for clinical decision making.
In this project, Carl and Chris are aiming to increase the usage of predictive systems based on machine learning techniques and genomics through the development and commercialization of a computational system that will:
Readmission after complex cancer surgery is common, with studies reporting between 15% and 50% of patients being readmitted within 30 days of discharge from the hospital. Readmissions are associated with increased healthcare costs, poor clinical outcomes including increased risk of infection and early mortality, and patient and family stress and suffering. Prior research has identified demographic and clinical predictors of readmission, but the role of patient-centered behavioral processes remains relatively unexplored. Identifying behavioral factors associated with readmission, which might include physical activity levels, sleep habits and social contacts, could highlight ways to prevent readmissions and empower patients to take a more active role in their recovery.
Anind Dey, Ph.D., Charles M. Geschke Director of the Human-Computer Interaction Institute at Carnegie Mellon University, and Carissa Low, Ph.D., Assistant Professor of Medicine and Psychology, Biobehavioral Oncology Program, University of Pittsburgh Cancer Institute, are collaborating to take a generalizable and scalable approach that holistically looks at patients’ behavior before and after surgery and identifies routines in this behavior. These routines will form the basis of their understanding of the predictive factors for readmissions, particularly while patients are still in the hospital after surgery or other treatments.
Harnessing technology to monitor these and other behavioral risks before surgery, during inpatient recovery, and during the critical transition from hospital to home will advance the field in a number of ways:
A non-invasive device that analyzes sounds from the intestinal tract could become a powerful new tool to help physicians diagnose and monitor a variety of gastric illnesses, such as acute pancreatitis, bowel obstructions, irritable bowel syndrome, inflammatory bowel disease, and Crohn’s disease.
Carnegie Mellon researchers, collaborating with UPMC gastroenterologists, will be developing, testing and performing clinical research on a wearable sensor array to detect intestinal sounds – a new vital sign for the ‘gut.’ Its impact could be substantial: in the U.S. alone, digestive diseases afflict 60-70 million people annually with treatment costs totaling more than $100 billion.
Using machine learning techniques, the researchers will develop a means of interpreting the acoustic signals to examine the determinants of gut activity and activity-suppression, the ability to identify gastric disorders on the basis of sounds, and the device’s therapeutic potential for enabling gut regulation via biofeedback. The project will also develop classification procedures that use these features to distinguish between the various types of gastrointestinal activity.
The research team includes George Loewenstein, Professor of Economics and Psychology, Department of Social and Decision Sciences and Ali Momeni, Associate Professor of Art, College of Fine Arts, along with Rich Stern, Professor Electrical & Computer Engineering, Max Gsell, Associate Professor Department of Statistics and Valerie Ventura, Associate Professor Department of Statistics.
Professor Kathleen M. Carley of the Institute for Software Research, and Professor L. Richard Carley of the Electrical and Computer Engineering Department are leading a project to determine if coordinated care decisions could be improved by analyzing and monitoring how patients move through the health care system.
They will be focusing on health care trails – time-ordered sequences in which a patient obtains services such as dialysis, blood work or psychological counseling. These trails will be analyzed to identify those that are commonly used by patients with similar conditions and those that most often improve patient conditions. Factors such as a patient’s age, health care coverage and medical condition can affect the flow of these services and differences in flow can be associated with different patient outcomes.
The goal is to develop, test and assess algorithms and interfaces for analyzing hospital admissions data and for providing feedback to doctors and caregivers through automated patient tracking and notification systems. By analyzing the timing of admits, discharges and transfers, the application could help inform care givers about how a patient’s care is being managed and detect when a patient might be at risk because of the health care trail they are on.
A team from Carnegie Mellon, the University of Pittsburgh, and UPMC is working on a project to evaluate the ability of cameras, inertial measurement units and other sensors, in combination with machine learning (ML) algorithms, to assess patients’ movement therapy exercises in the home. The long-term goal is to increase the quality and efficacy of physical therapy by providing patients with automated, in-home monitoring, near real-time feedback on exercise performance and feedback to providers when issues arise outside of the PT setting. This will enable providers to prioritize clinic time for patients whose recovery has stalled while avoiding unnecessary appointments for those who are progressing satisfactorily. Leading the team’s machine learning evaluations from Carnegie Mellon’s School of Computer Science are Jessica Hodgins, Ph.D.; Dan Siewiorek, Ph.D.; Asim Smailagic, Ph.D.; and Andrew Whitford, Ph.D. Dr. Keelan Enseki from the UPMC Center for Rehab Services and Adam Popchak, Ph.D., from the University of Pittsburgh’s Department of Physical Therapy are leading the clinical stages of the project.
The costs of preterm births to the healthcare system and society are exceedingly large, and the incidence of preterm births is on the rise despite medical advances. This has created a need to provide reliable, personalized information to pregnant women about how they can reduce their risk of premature births. In response, Dr. Hyagriv Simhan of Magee-Women’s Hospital and Professors Tamar Krishnamurti and Alexander Davis of Carnegie Mellon University have created the “MyHealthyPregnancy” smartphone app.
The app applies statistical machine learning techniques to comprehensive pregnancy data sets to improve the app’s patient-specific risk predictions of adverse pregnancy outcomes. It also employs decision science techniques to extend the app to provide post-partum education and behavioral feedback to minimize infant mortality outcomes. The team, which has performed pilot studies of the concept, expects this work will result in a product that can be tailored to both the UPMC health system and developed into an off-the-shelf application for the general population of pregnant women.
Despite screening technologies and public health efforts that have improved the detection of early-stage cancers, methods to reliably predict which of these cancers will progress to more aggressive stages, metastasis, and ultimately patient mortality are lacking.
Russell Schwartz, Professor of Biological Sciences and Computational Biology, and Jian Ma, Associate Professor of Computational Biology, both with the School of Computer Science, are working on a suite of software tools that clinicians will use to improve cancer diagnosis and therapeutics based on the molecular signatures of the patient’s tumor genome.
The goal of the project is to develop novel models, algorithms, and software tools to better understand the origin and evolution of tumor cells and how a patient’s tumors are likely to progress. This information would contribute to personalized, precision treatment of cancer.
As health data is increasingly digitized, the need to protect patient privacy is unprecedented. Jean Yang, Ph.D., Matt Fredrikson, Ph.D., and Jian Ma, Ph.D., from Carnegie Mellon’s School of Computer Science, are collaborating on an approach to protect patients’ health records and computations involving sensitive patient data from being leaked in a breach.
The researchers propose a programming model and software framework for managing the privacy of health data. The programming model allows programs to attach privacy policies directly to the data, rather than requiring the programmer to implement the correct privacy checks across code that uses the data. Attaching privacy policies to the data enables programs to be policy-agnostic and to be updated independently of the specification and implementation of privacy policies. The system tracks the flow of data, as well as the policies, so the programmer need not do so. This approach facilitates auditing, as policies may now be centralized and to be implemented only once, instead of as checks across the program. Auditors may also leverage the fact that the framework, rather than the programmer, is now responsible for correctly implementing the checks.
This project is the first step in a long-term plan to improve patient care and promote scientific discovery by allowing sensitive health data to be shared safely. This plan involves 1) applying prior work on policy- agnostic programming for information flow policies in the context of patient health records, 2) extending and combining prior work to support more expressive policies, and 3) building a practical framework that can be used to safely share sensitive data.
Over 150,000 Americans are treated in hospitals each year after cardiac arrest, virtually all of whom are initially comatose. Once their hearts are revived, most such patients eventually die in the hospital from brain injury, but a sizable minority will awaken and have a good neurological recovery. With current technology, no sign, symptom, or test result in the first 72 hours after cardiac arrest is sufficient to preclude a favorable recovery. Prognosis often remains uncertain for days or even weeks thereafter. Daniel Nagin, Ph.D, at Carnegie Mellon University’s Heinz College, along with physicians Jonathan Elmer and Cliff Callaway from the University of Pittsburgh Medical School, are developing technology that will use advanced signal processing and modeling methods to allow accurate neurological prognostication sooner than currently possible.
Joe Marks, Executive Director of CMLH, leads the strategic initiatives, business development, and negotiations for the Center. Joe brings a diverse background to CMLH that includes leadership roles at Disney Research and Mitsubishi Electric Research Labs (MERL), and involvement in multiple start-ups.
He earned his undergraduate and PhD degrees from Harvard University. His publications have focused mostly on topic areas that include computer graphics, human-computer interaction, and artificial intelligence, but he’s happy to work in new areas as the opportunity arises.
Carl Kingsford is an Associate Professor in the Computational Biology Department in the School of Computer Science at Carnegie Mellon University. His research group develops efficient algorithms for analysis of large-scale biological data, particularly high-throughout genomic sequencing experiments. He and his group are the authors of several widely used, open source software packages for genomic analysis. More information about his research can be found at http://kingsfordlab.cbd.cmu.edu. He is the recipient of an NSF CAREER award, a Sloan Research Fellowship in Computational and Molecular Biology, and he is one of 14 Data Driven Discovery Investigators funded by the Gordon and Betty Moore Foundation selected from across all areas of science. He received his PhD in Computer Science from Princeton University.
Marketing and Commercialization Advisor for the CMLH
Distinguished Service Professor of Digital Media and Marketing
Heinz College, Carnegie Mellon University
Andrew Moore, Dean of Carnegie Mellon University’s School of Computer Science, and Joe Marks serve on the CMLH Steering Committee with other members of the School of Computer Science and executives from UPMC. The CMLH Steering Committee includes stakeholders from both CMU and UPMC with a shared vision and goals for the work of the Center. The Steering Committee acts as the ultimate decision maker for organization and management issues for the center’s mission.