Projects

Diagnosing Tuberculosis

Principal Investigator:
Yutaka Yasui

Problem we’re trying to solve

Inexpensive, timely and accurate diagnosis of potential cases of Tuberculosis is of critical importance in regions of the world where resources are limited. Standard methods of diagnosis are often too expensive or resource intensive to be deployed in the very regions where tuberculosis is a significant problem. This leads to poor patient outcomes due to delayed treatment and undiagnosed illness.

How will this help someone / an industry?

The machine learning component of this work developed a new automated diagnostic method using image analysis which enables diagnosis that is more efficient and lower cost than standard methods, and because it is automated, poses lower biohazard risk to the technicians processing samples.

Partners

TV/HIV Research Foundation (Thailand)

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

Legal Reasoning

Principal Investigator:
Randy Goebel

Problem we’re trying to solve

A great deal of human resources are used in order to prepare for (or render a verdict in) a legal proceeding. The tools we are developing for information extraction and visualization in the legal domain extract legal concepts from text, identify chains of legal reasoning and answer questions by using textual entailment. Developing automated tools for information extraction and knowledge discovery will reduce the amount of effort and time needed for legal matters.

How will this help someone / an industry?

This project seeks to develop techniques for legal case reasoning, legal summarization and legal question answering in order to allow law practitioners to redirect their attention to tasks that require creativity or more complex reasoning.

Partners

National Institute for Informatics (Japan)

Type of MI used

Natural Language Processing, Information Extraction.

Projects

Ana – Automated Nanny Agent

Principal Investigator:
Osmar Zaïane

Problem we’re trying to solve

Ana, a conversational software agent (ie chatbot), is designed to converse with the elderly living at home to answer general questions and remind them of specific events. Ana is able to extract from conversations named entities (ie places, people, prescriptions, recipe names, etc.) as well as relationships (ie family tied, professions, activities, temporal events, etc.) Ana extracts information from text obtained from a speech-to-text converter; from this, it builds a personalized knowledge base that allows it to answer personal questions. Ana can also answer impersonal questions from sources on the Internet.

How will this help someone / an industry?

In addition to developing a speech interface for human-machine interaction, Ana seeks to improve elderly home care by providing a personal assistant and a digital companion. Ana helps with social needs (through questions and answers) and assists with simple home healthcare needs (ie. prescription reminders).

Type of MI used

Information Extraction, Natural Language Processing.

Projects

Advanced Analytics for Curling

Principal Investigator:
Michael Bowling

Problem we’re trying to solve

The Computer Curling Research Group focuses on deep analytics for the sport of curling for player analysis and system analysis, and to create tools that can translate AI discovered insights into improvements to human decision making.

How will this help someone / an industry?

The ultimate goal of this project is to develop tools and models that enable player/team assessment, strategic game modeling, and analytics for broadcast television.

Type of MI used

Deep learning, Search and Planning.

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