Research on Electronic Medical Records (EMR) and the data stored in the CPCSSN (Canadian Primary Care Sentinel Services Network) data bank. CPCSSN store currently holds 1.6 million patient data shared by primary care physicians from many provinces and territories across Canada and serves as a valuable source of anonymized health data.
Diagnosing Hypertension: Developed a neural network model to predict hypertension from structured medical data from patients' health records. We obtained about 82% accuracy. [Lafreniere et al., 2016]
Diagnosing chronic low back pain: Created a tool for extracting knowledge from doctors chart notes for diagnosing chronic low backpain [Judd et al, 2018]. The tool implements a data analytic pipeline using a 3rd party script for anonymizing medical text data, CTAKES for extracting medical terms, and ensemble learning using a variety of machine learning models for disease diagnosis.
Discovering and diagnosing PTSD (post traumatic stress disorder) from doctors' chart notes: This is a collaborative project among University of Manitoba, Western University and and Queen's University funded by IBM, Mitacs, and CIMVHR. Goals are to develop a gold standard of the epidemiology of PTSD and applying NLP and text mining techniques to identify cases of PTSD in the Electronic Medical Records (EMR) of military veterans and their family members, compute statistics on care quality, prevalence and severity of PTSD in the population, and predict suicidal tendencies.
The three component Proposed Hybrid Model (PHM) [Wang et al., 2012] was implemented to predict daily stock index. Although PHM performed well for weekly stock prices, the results showed that the back propagation neural network (BPNN) model performed better than the other two component models of ARIMA (Auto Regressive Integrated Moving Average) and the ESM (Exponential Smoothing Model).
Query Expansion for Knowledge Extraction: With industry partner Gnowit, developed a query expansion algorithm for expanding query terms to effectively search for matching webpages from large online web documents using DataMuse API and word2vec techniques.
Clustering Large Text Data: Implement text analytics algorithms (particle swarm optimization), SPARK-PSO, on big data analytics platforms such as Apache Spark for fast in-memory distributed analytics of large text data [Sherar et al, 2017].
CAPRI: Extracts frequent, infrequent and rare patterns and association rules from semi-structured mainframe log data files containing complex and hybrid line patterns [Zulkernine et al., 2012].
Topic Modeling: Implement various LSA, LDA, pLSA, and deep belief network [Chanderdhar Sharma and Marwa Chermiti] for the 20-Newsgroup dataset to extract topics and a neural network model to classify the text data based on the topics.
Used evolutionary computing with feed forward neural network to train a vehicle to successfully navigate a track in the shortest time possible [Song et al., 2017]. The fitness criteria included successful completion of the track without hitting any walls at maximum speed.
Created a cognitive data analytic and decision support pipeline for classifying noisy medical data based on schema and ministry specified guidelines using both a rule based and a neural network approach to identify reportable critical diseases [Lucas Rychlo].
Reviewed state-of-the-art speech recognition systems, proposed a taxonomy and developed a speech enabled e-commerce website to increase accessibility of such websites using IBM Watson speech-to-text API and cloud services [Kandhari et al., 2018].
In an exploratory study two undergraduate students (Daisy Barrette and Alex Weatherhead) constructed two different versions of autonomous cars, one using ultrasonic sensors and deep learning models, and another using camera and deep convolutional networks, where the cars were trained to autonomously drive by sensing track boundaries. The model cars were made using raspberry pi with bluetooth connections, arduino uno and commercially available cheap sensors. Trained models were deployed on the cars.
Video Object Tracking: Leveraged the YOLOv2 architecture with a 2-D recurrent LSTM to implement a predictive video object detector and tracker (POD) [Gasmallah et al, 2018].
Fall Detection using Wearable Sensors: A deep learning model was implemented and trained offline using the public MobiAct dataset and later deployed in a streaming IoT data analytics framework for fall detection using MbientLab sensor MetaMotion R [Ajerla et al., 2018].
A Multilevel Streaming Data Analytics Infrastructure: Design and implement a multi-level architecture for high speed real time streaming data analytics using streaming data ingesting and processing engines, and an integrated in-memory data storage and analytics framework. Goal: Enable complex machine learning without choking the streaming data processing pipeline. Funded by IBM and SOSCIP (Southern Ontario Smart Computing Innovation Platform), industry partner Gnowit.
BINARY: Created a framework, BiNARY (A Big Data Integration framework for Adhoc Query Processing) for distributed management of multiple hybrid back-end data sources, and integration and ad-hoc querying of big data [Eftekhari et al., 2016]
Graph Data Management and Analytics: Multi-cluster large graph data management and analytics for online analytical processing to respond to user queries, link distributed data and real time community detection for distributed data management.
CLAaaS: Designed and implemented a Cloud-based Analytics-as-a-Service architecture to provide role-based access to users to a cloud-based data analytics and visualization platform to perform data upload, custom workflow definition and execution, knowledge sharing and visualization without worrying about installing required tools and storage platforms. [Zulkernine at al., 2013]