Projects

FMRI-based Diagnosis & Treatment

Principal Investigator:
Russ Greiner

Problem we’re trying to solve

Current methods of diagnosing neurological and psychological disorders often rely on the subjective assessment of a patient’s symptoms by a clinical psychiatrist. These assessments can differ between psychiatrists, leading to different recommendations for a treatment plan. We aim to provide these psychologists with tools that provide objective criteria for diagnosis and the assessment of symptom severity in order to provide psychiatrists data-driven methodologies for assessing patients.

How will this help someone / an industry?

Our goal with this project is to use machine learning techniques to produce clinical tools that could assist medical doctors in providing faster, more effective treatment for neurological and psychiatric illnesses. We are exploring the use of brain imaging to diagnose mental disorders earlier and more accurately, to predict symptoms and their severity, and to predict which combination of drug therapies will work best for a given patient.

Partners

IBM Research

Projects

Patient-Specific Survival Prediction

Principal Investigator:
Russ Greiner

Problem we’re trying to solve

Prognostic modeling is an integral component in the treatment and management of patients. Currently being developed for the field of oncology, PSSP predicts individual survival distributions for patients from their electronic health record, significantly reducing the prediction error compared to the standard approach of using only the cancer site and stage.

How will this help someone / an industry?

More accurate survival time prediction can improve medical decision making (for example, by deciding whether a treatment option is cost-effective based on it’s added survival time, or by helping determine when a patient should be referred for end-of-life care.) The tool can be used more generally for any task that involved predicting a life-cycle (customer churn, diagnosing machine faults, etc).

Projects

Intelligent Diabetes Management

Principal Investigator:
Russ Greiner

Problem we’re trying to solve

The current method of determining insulin dosages requires a patient to manually track their insulin levels multiple times a day, collect data over a certain period of time, present that data to a diabetologist, and have their dosage adjusted after weeks of using the wrong dose. This is delaying our ability to optimize treatment, depends on the patient’s commitment to tracking data, and requires a diabetologist to personally evaluate each case.

How will this help someone / an industry?

Machine learning is able to use patient data to adjust insulin levels in real time, making their treatment personalized, more accurate, and more affordable. It also increases the capacity for diabetologists to see more patients and help more people.

Partners

Alberta Diabetes Institute; top rated diabetologist from Alberta

Type of MI used

Reinforcement learning