Pushing Mobile Inventions Forward Seminar: Fjola Helgadottir, PhD – Director of AI Therapy

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Fjola Helgadottir, PhD

Director of AI Therapy

Vancouver CBT Centre


Translating Clinical Psychology Treatments into Algorithms: Successes and Challenges

Abstract: Computerized therapy has the potential to revolutionize how evidence based psychological interventions are delivered to those who need them. Many of the recent advances in AI, from computer vision to natural language processing, will doubtlessly be integral components of future treatment systems.

There is a wide range of approaches to computerized therapy. Many research projects aim to replicate the face-to-face therapy experience. This seems like a natural approach, given that this is a longstanding and proven model of therapy. For example, these systems make use of avatars and chatbots. However, this approach may be misguided. Computer-based approaches and human therapists are fundamentally different, and designing one to mimic the other may not be optimal. The goal should be to find the most effective methods of targeting the key mechanisms that are paramount to change in mental health.

In this talk Dr. Helgadottir will take a look at computerized therapy from the perspective of a practicing clinical psychologist. She will review some of the advantages that computers have over human therapists, as well as considering limitations of these systems. As a practical example, she will explain how her online “Overcome social anxiety” program works and discuss promising results from recent clinical trials.

Bio: Dr Fjola Dogg Helgadottir is a Director at AI-Therapy, a registered psychologist at the Vancouver CBT Centre and previously a Senior Research Clinician at Department of Psychiatry, University of Oxford in the UK. She is a Chartered clinical psychologist within the British Psychological Society, and a registered psychologist with the UK Health and Care Professions Council and with the British Columbia College of Psychologists. Fjola has completed four degrees in psychology (see more professional qualifications). AI-Therapy grew out of her doctoral research, which was focused on innovative computer-based treatments for anxiety disorders.

Fjola has written extensively about online therapy, both in peer reviewed academic journals and conferences. She is an expert writer for Psychology Today with her open access blog Man vs Machine and is featured frequently in the Icelandic media. See Fjola’s publications for more details.

Fjola has received several major awards for her internationally recognized research, including Australia’s prestigious Tracy Goodall Early Career Award for Research Achievement. In addition, she has trained to the highest level as a clinical psychologist in Australia, and ran a successful private practice in Sydney. She currently provides consulting services on the topic of online psychology and psychiatry for her company AICBT Ltd, which has clients in Sydney, Australia; Oxford and London, UK; and Denver and New York in the USA.

MIDAS Health Sciences Challenge Symposium

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Data-intensive health science is one of the research focus areas that MIDAS supports with its Challenge Awards.  Our long-term goal is to support this research area more broadly, using the Challenge Award projects as the starting point to build a critical mass.  This symposium offers a platform for all participants to explore collaboration opportunities and aims to attract more researchers to our hub.  It will feature in-depth presentations from three Challenge Award teams, and all participants are encouraged to submit posters on data-intensive health science research.


9 am to 12:50 pm: Welcome and Challenge Award Presentations

12:50 pm to 2:15 pm: Lunch, Poster Session and Networking [poster dimensions: up to 6ft wide X 4ft height]

2:15 pm: Panel Discussion: The Future of Data-intensive Health Sciences at U-M.

  • Panelists: Brian Athey (Moderator), Marisa Eisenberg, Jun Li, Brahmajee Nallamothu, Srijan Sen, Kevin Ward

Please register online.  Please submit poster abstracts (< 300 words).  Submission Deadline: April 28.

For questions: midas-research@umich.edu.

IOE 899 Seminar Series: Stanley Hamstra, PhD, Milestones Research & Evaluation Accreditation Council for Graduate Medical Education

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Stanley J. Hamstra, PhD

VP, Milestones Research and Evaluation Accreditation Council for Graduate Medical Education


“Learning Analytics in Graduate Medical Education: Realizing the Promise of CBME with Milestones Achievement Data”

Abstract: In 2012, the Accreditation Council for Graduate Medical Education (ACGME) introduced the Next Accreditation System (NAS) for improving postgraduate medical education. An important component of the NAS is a shift towards competency-based medical education (CBME), involving milestones as markers of achievement during training. Since 2015, the ACGME has been collecting milestones achievement data (competency ratings) on all resident and fellow physicians in accredited training programs in the USA (n > 110,000 residents and fellows per year). A critical assumption in CBME is that assessment data regarding any learner (in any form) contains some degree of uncertainty. At the same time, program directors must make finite/binary decisions about learners at the time of graduation, and indeed throughout training. The availability of milestones data, in the context of national trends, gives the program director an additional tool for making the best decisions regarding learner progression (and ultimately graduation). I will briefly review tools we have developed to help program directors make use of milestones data to enhance the quality of their decisions regarding resident progression and graduation. In addition, I will outline an approach to using the data for enhancing national curricula within a specialty.

Bio: Dr. Hamstra is responsible for oversight and leadership regarding research in Milestones and assessment systems that inform decisions around resident physician progression and board eligibility. Dr. Hamstra works with medical subspecialty societies, program director organizations, the American Board of Medical Specialties, and specialty certification boards. His research addresses medical education broadly, including competency assessment for residency training programs, and developing administrative support for educational scholarship within academic health settings. Prior to joining the ACGME, Dr. Hamstra was at the University of Michigan, the University of Ottawa, and the University of Toronto Department of Surgery. He has also worked closely with the Royal College of Physicians and Surgeons of Canada on developing policies regarding competency-based medical education for graduate medical education. Dr. Hamstra received his PhD in sensory neuroscience from York University in Toronto in 1994.

Peers Health and U-M begin research partnership using disability and workers’ comp healthcare data

By | General Interest, Happenings, News, Research

Peers Health and the University of Michigan are starting a two-year research project that will apply advanced learning technologies to a proprietary global database of millions of de-identified disability and workers’ compensation cases. The goals of the project include developing a prescriptive modeling framework to facilitate development of optimal return-to-work plans for injured or ill patients.

Public policy experts have begun to connect patients’ ability to perform their productive endeavors, such as their job, to their state of general health and well-being. The findings from this project, by helping define when someone objectively has returned to health, could inform decision-making in virtually every healthcare episode.

The principal investigators in the project, Dr. Brian Denton and Dr. Jenna Wiens, are both renowned experts in medical machine learning. Dr. Denton, a professor of Industrial and Operations Engineering and Urology, and Dr. Wiens, an assistant professor of Computer Science and Engineering, are both affiliated with the Michigan Institute of Data Science (MIDAS) at U-M.

Peers Health recently announced an expanded partnership with ODG, an MCG company and part of the Hearst Health Network, to aggressively acquire new data to enhance ODG functionality and to fuel this research. Jon Seymour, MD, CEO of Peers, said, “This is a new phase in medical publishing where raw data collection is the editorial function and cutting-edge machine learning is the technology factor. We turned to the University of Michigan due to its impressive data science programs spanning multiple departments, as well as the specific experience of Dr. Denton and Dr. Wiens in medical applications. We’re confident this initiative will attract many new data contributors along the way.”

“The collaboration with Peers Health is exciting because it provides data that can help build a model that will reduce the time — from both a safety and productivity perspective — for people to return to work following sickness or injury,” Denton said. “Streaming data in from existing patients will allow our model to adapt and improve over time.”

Wiens added: “These data contain a particularly interesting training label: days away from work. We hypothesize that this will be a strong signal for the type, timing, and effectiveness of the treatments and therapies.”

The U-M partnership with Peers was established by MIDAS and the university’s Business Engagement Center (BEC).

“This partnership illustrates the power of combining data from the healthcare industry with the data science expertise of U-M faculty,” said Dr. Alfred Hero, professor of Engineering and co-director of MIDAS.

“It is energizing for the BEC to be part of these innovative collaborative relationships that create real impact in the world,” added BEC Director Amy Klinke.


CHEPS Seminar: Sung Won Choi, MD, MS, University of Michigan

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Sung Won Choi, MD, MS

Associate Professor, Pediatrics

Inaugural Edith S. Briskin / Shirley K Schlafer Research Professor of Pediatrics

Michigan Medicine

The University of Michigan


“Multi-dimensional, Highly Time-resolved Big Data Approach for Disease Prevention”

Abstract: Individualized prediction of disease (and disease‐related events) is a major unmet challenge, yet is essential for realizing the full potential of personalized medicine. Underlying the prediction problem is the fact that disease processes, and the human hosts in which they occur, represent complex dynamical systems comprised of large numbers of components that interact in non‐linear ways over time. A key insight from complexity science is that accurate long‐term prediction in such systems is usually not feasible, but short‐term predictions can be successful if multi‐parameter, highly time‐resolved data can be collected and integrated using computational methods. Complex science indicates that prediction of disease needs to be done on an ongoing basis, in near “real‐time”, because complex dynamical processes tend to proceed non‐linearly. There are “windows of opportunity” when signal begins to exceed background noise and the disease process is early enough for intervention to be successful. Please join Dr. Choi as she discusses how she and her collaborators, including Dr. Wiens (Computer Science/Machine Learning), Dr. Tewari (Medical Oncology), Dr. Kurabayashi (Mechanical Engineering), and Dr. Li (Computational Biology) are using the blood and marrow transplantation setting as an ideal model system to prototype such an approach for disease prediction that is consistent with the highly complex nature of human disease.

Bio: Sung Won Choi, MD, MS trained as a pediatric resident at New York University and later as a fellow in pediatric hematology‐oncology at the University of Michigan. Through an NIH K23 award, Sung received additional training in Clinical Research Design and Statistical Analysis through the University of Michigan School of Public Health. She is currently an Associate Professor in the Department of Pediatrics, and in 2017, she was named the inaugural Edith S. Briskin / Shirley K Schlafer Research Professor of Pediatrics. Sung specializes in the field of blood and marrow transplantation (BMT) and is recognized for her work in translating the use of histone deacetylase inhibition in BMT patients for prevention of a devastating complication known as graft-versus‐host disease (GVHD). She enjoys translatitional research initiatives that include the use of novel, non‐steroidal therapeutics both in the prevention and treatment of GVHD. Her research efforts focus on: 1) providing an improved understanding of clinical BMT through translation of experimental studies 2) exploring clinical outcomes in BMT patients alongside laboratory correlates; and 3) leveraging novel tools, such as information technology, to support patient‐ and caregiver‐centered care in her clinical and translational research efforts in BMT.

The seminar series “Providing Better Healthcare through Systems Engineering” is presented by the U‐M Center for Healthcare Engineering and Patient Safety (CHEPS): Our mission is to improve the safety and quality of healthcare delivery through a multi‐disciplinary, systems‐engineering approach.

New private insurance claims dataset and analytic support now available to health care researchers

By | General Interest, Happenings, HPC, News

The Institute for Healthcare Policy and Innovation (IHPI) is partnering with Advanced Research Computing (ARC) to bring two commercial claims datasets to campus researchers.

The OptumInsight and Truven Marketscan datasets contain nearly complete insurance claims and other health data on tens of millions of people representing the US private insurance population. Within each dataset, records can be linked longitudinally for over 5 years.  

To begin working with the data, researchers should submit a brief analysis plan for review by IHPI staff, who will create extracts or grant access to primary data as appropriate.

CSCAR consultants are available to provide guidance on computational and analytic methods for a variety of research aims, including use of Flux and other UM computing infrastructure for working with these large and complex repositories.

Contact Patrick Brady (pgbrady@umich.edu) at IHPI or James Henderson (jbhender@umich.edu) at CSCAR for more information.

The data acquisition and availability was funded by IHPI and the U-M Data Science Initiative.