(FoRCE): Powering clinical trials research through a secure and integrated data
Critical care units are one of the most data-rich environments in clinical settings, with data being generated by advanced patient monitoring, frequent laboratory and radiologic tests, and around-the-clock evaluation. There are substantial opportunities in linking data that are collected as a part of such clinical practice with data collected in a research setting, such as genome wide studies or comprehensive imaging protocols. However, security and privacy issues have historically been a significant barrier to the storage, analysis, and linkage of such biomedical data. Further, disparate technologies hinder collaboration across teams, most of which lack the secure systems required to enable federation and sharing of these data. This is particularly true when clinical practice or research designs require close to real time analysis and timely feedback, such as when dealing with streamed medical data or output from clinical laboratories. Current commercial and research solutions often fail to integrate different data types, are incapable of handling streaming data, and rely solely on the security measures put in place by the organizations that deploy them.
This proposal seeks to build FoRCE (Focus on Research and Clinical Evaluation), a scalable and adaptable add-on module to the existing Indoc Informatics platform that will address the critical gaps in cybersecurity and privacy infrastructure within shared clinical and research settings, while fulfilling important unmet needs for both the clinical and research communities. FoRCE will provide the secure architecture and processes to support the collection, federation and sharing of data from distributed clinical settings, including critical care units, clinical laboratories, and imaging facilities. The proposed platform will address several key issues including security considerations, infrastructure and software requirements for linkage, and solutions for handling streaming real time medical data, and ensuring regulatory and ethics compliance when linking diverse medical data modalities in a clinical setting.
FoRCE will be designed and developed with broad applicability in mind, and will therefore allow the different data types from numerous technologies and across multiple disease states to utilize the platform. The long term impact of FoRCE on improving the health of Ontarians is of course dependent on its utilization within research and clinical settings. An initial project which will utilize the platform as part of the testing and validation of FoRCE includes Dr. Maslove’s integrated approach to merging genomic and physiologic data streams from the ICU in the context of clinical research. FoRCE will enable Dr. Maslove’s team of critical care researchers to move beyond predictors of survival to focus on predictors of response to therapy, so that clinical trials in the ICU can be optimized to produce actionable evidence and personalized results. This will lead to better allocation of ICU resources, which in Canada cost nearly $3,000 per patient per day – $3.72 billion per year.
A dynamic and scalable data cleaning system for Watson analytics
Poor data quality is a serious and costly problem affecting organizations across all industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate values. It is estimated that poor data quality cost organizations between 15% and 25% of their operating budget. Existing data cleaning solutions focus on identifying inconsistencies that do not conform to prescribed data formats assuming the data remains relatively static. As modern applications move towards more dynamic search analytics and visualization, new data quality solutions that support dynamic data cleaning are needed. An increasing number of data analysis tools, such as Watson Analytics, provide flexible data browsing and querying abilities. In order to ensure reliable, trusted and relevant data analysis, dynamic data cleaning solutions are required. In particular, current data quality tools fail to adapt to: (1) fast changing data and data quality rules (for example as new datasets are integrated); (2) new data governance rules that may be imposed for a particular industry; and (3) utilize industry specific terminology and concepts that can refine data quality recommendations for greater accuracy and relevance. In this project, we will develop a system for dynamic data cleaning that adapts to changing data and rules, and considers industry specific models for improved data quality.
Active learning for automatic generation of narratives from numeric financial and supply chain data
Large enterprises compile and analyze large amounts of data on a daily basis. Typically, the collected raw data is processed by financial analysts to produce reports. Executive personnel use these reports to oversee the operations and make decisions based on the data. Some of the processing performed by can be easily automated by currently available computational tools. These tasks mostly make use of standard transformations on the raw data including visualizations and aggregate summaries. On the other hand, automating some of the manual processing requires more involved AI techniques. In our project, we aim to solve one of these harder to automate tasks. In fact, analyzing textual data using NLP is one of the standardized methods of data processing in modern software tools. However, vast majority of NLP methods primarily aim to analyze textual data, rather than generate meaningful narratives. Since generation of text is a domain dependent and non-trivial task, automated generation of narratives requires novel research to be useful to an enterprise environment. In this project, we focus on using numerical financial and supply chain data to generate useful textual reports that can be used in executive level companies. Upon successful completion of this project, financial analysts will spend less time on repetitive tasks and have more time to focus on reporting tasks requiring higher level data fusion skills.
Agile real time radio signal processing
Canadian VLBI capability has been missing for a decade. Jointly with Thoth Technology Inc we propose to restore domestic and international VLBI infrastructure that will be commercialized by Thoth Technology Inc. This project will implement and optimize multi-telescope correlation and analysis software on the SOSCIP BGQ, Agile and LMS platforms. The resulting pipeline package will allow commercial turnkey VLBI delivery by Thoth Technology Inc to domestic and international customers into a market of about $10 million/year
An economics-aware autonomic management system for big data applications
Recent advancements in software technology, including virtualization, microservices, and cloud computing, have created novel challenges and opportunities on developing and delivering software. Additionally, it has given rise to DevOps, a hybrid team responsible for both developing and managing the software system, and has led to the development of tools that take advantage of the enhanced flexibility and enable the automation of the software management cycle. In this new world characterized by volatility and speed, the Business Operations (BizOps) team is lagging behind and still remains disconnected from the DevOps team. BizOps views software as a product and is responsible for defining the business and economic strategy around it.
The goal of the proposed project is to imbue DevOps tools and processes with BizOps knowledge and metrics through formal models and methods. Currently, BizOps receives the software system or service as a finished product, a black box, on which a price has to be put and be offered to clients. The price and the marketing strategy are usually defined at the beginning of a sales cycle (e.g. a year) and remain the same for the entirety of the cycle. However, this is in contrast to the great volatility of the service itself. In most cases, the strategies are based on the instinct of managers with high acumen and experience and broad marketing surveys or one-to-one negotiations with clients, information that can easily change and may remain disconnected from the software development. The end product of this project is a set of economic and performance models to connect the DevOps and BizOps processes during the software’s life cycle and eventually incorporate them in automated tools to adapt and scale the system in production and enable continuous development, integration and delivery.
Big data analysis and optimization of rural and community broadband wireless networks
Rural broadband initiative is happening in a big wave across the world. Canada, being a diverse country has a specific Internet reachability problem due to population being sparse. It is economically not viable to bring fiber to each and every house in Canada. It is not economically viable to connect every household through satellites either. Broadband Internet over wireless networks is a good option where Internet is brought over fiber to a point of presence and moved to houses over wireless.
EION is actively working in Ontario and Newfoundland to make rural broadband a possibility. Wireless networking in rural areas in Canada is a challenge in itself due to weather, terrain and accessibility. Real-time constraints such as weather, water and foliage do alter the maximum capacity of the wireless pipe. In addition the usage pattern of the houses, especially real-time video that require fast response time, require adequate planning.
This is becoming very critical as almost 80% of the traffic seems to be video related due to popularity of applications such as Netflix, Youtube and Shomi. Intelligence in wireless rural broadband networks are a necessity to bring good quality voice, video and data reliably. Optimization in system and network level using heuristics and artificial intelligence techniques based on big data analysis of video packets is paramount to enable smooth performing broadband rural networks.
In this project, we will be analyzing the big data of video packets in rural broadband networks in Ontario and Newfoundland and design optimized network design and architecture to bring reliable video services over constrained rural broadband wireless networks.
Cloud‐based data analytic platform for real‐world evidence generation
Randomized controlled trials (RCTs) are considered the gold standard for supportive clinical evidence, however RCTs rarely describe the effectiveness of an intervention in real life practice. As such, regulators, payers, and health‐care providers are turning towards real world data (RWD) to understand how well an intervention performs in clinical practice.
The best source of RWD is source data – that is, data that are collected at the interface of the patient and the health care system, as well patient monitoring and self‐reported data captured directly within the patients’ home environment. Unfortunately, these data are stored in different and distinct systems that are not well integrated and in formats that do not allow for rapid analytic capabilities. The University of Waterloo in partnership with Roche Canada, are therefore proposing to develop the “CARE” (Clinical Analytics for Real‐World Evidence) platform.
The “CARE” platform will be a holistic cloud‐based data analytic solution featuring a large central repository for consolidating data obtained from disparate data systems (including data from electronic medical records, lab data, physician transcriptions, and patient monitoring devices and self‐reported surveys). The “CARE” platform will serve as a central hub for researchers that includes integrated and sophisticated data analytics, access control, security and study management tools in order to curate data for research and clinical purposes. This project begins the initial steps to address relevant research objectives in lung cancer by providing the tool and infrastructure required to process and analyze the enormous amount of scattered oncology data within an institution and across multiple institutions. Ultimately, this work will make it possible to mine currently siloed and/or unstructured data across the system and produce data‐driven insights in order to deliver the right care to the right patient at the right time through scientific innovation and research excellence.
Computational high-throughput screening of catalyst materials for renewable fuel and feedstock generation
According to a World Energy Council Report, population growth and rising standards of living across the world will at least double global energy demand by 2050. Simultaneously, carbon dioxide emissions must be reduced significantly to prevent a catastrophic rise in global temperatures. Clean and abundant renewable energy sources are available; unfortunately, the intermittency of solar and wind power is a prevailing problem which is limiting the potential for widespread use. Our project seeks to address both of these issues through development of novel catalysts to electrochemically convert CO2 captured from power plants into fuels and other higher value chemical feedstocks using renewable electricity. This innovative strategy will (1) provide a long term storage solution by converting renewable electricity into a stable chemical fuel, (2) provide a means to intelligently recycle CO2 rather than storing it in deep underground aquifers, and (3) provide a cleaner and cheaper pathway for production of industrial chemical feedstocks and fuels. This could be a truly disruptive technology which would allow Canadian led manufacturing of high value chemicals and fuels in a low-cost and low-carbon fashion. Additionally, there are large benefits to Canada’s energy sector by facilitating the dispatchability of renewable power.
Distributed and scalable search in enterprise databases
Google search, and other search engines such as Bing and Yahoo!, provide a convenient way to find Webpages that contain various keywords or are related to particular topics. For the purposes of searching, Webpages are essentially loosely structured paragraphs of text. However, much of the world’s high-quality enterprise data are structured into well defined tables containing sets of well-defined columns.
One consequence of structured database design is that information about a single entity may be scattered across many columns in many tables, and must be stitched together in a meaningful way when answering user queries. This turns out to be significantly more difficult than finding Webpages or text documents containing various keywords.
As Dr. Surajit Chadhuri (a Distinguished Scientist at Microsoft Research) recently argued in a keynote talk at the IEEE Data Engineering conference, search over structured databases has fallen behind search over unstructured data. In the proposed research, we will develop a powerful and intuitive search system, akin to Web keyword search, for structured enterprise data. Our system will empower nontechnical users to explore enterprise databases and turn big data into actionable insight, just as Google search has empowered society to explore the Web.
Integrated platform for distributed analytics of biometric data
Avertus is a company focused on providing tools for physicians to provide better patient care and treatment and researchers who are seeking new and improved treatments. Its first platform is a high frequency wireless brain activity monitor with a first application for seizure pattern detection. This will help physicians assess types and causes of seizures as well as response to drugs and other treatments and longer term help deliver future treatment technologies. With support from the SOSCIP and University of Toronto researchers, we will also implement the first commercially available high performance distributed computing platform for easy to use biomarker discovery tools and both batch and real time big data analytic capability for clinicians and researchers.
Intelligent emergency response using the internet of things (IoT)
Smart mobile devices and wireless Internet access are allowing emergency responders to access and share valuable emergency-related information coming from data repositories, citizens, surveillance cameras, and many other sources. However, because there is no accurate and energy-efficient indoor positioning solution, emergency responders cannot translate that wealth of information into a much-needed situational awareness. This project addresses this unique challenge by using data analytics and the Internet of Things (IoT). More specifically, this project devises an accurate, scalable, energy efficient, robust, and resilient indoor positioning solution using Bluetooth Low Energy (BLE) beacons.
These battery-operated beacons will be deployed in buildings as part of emergency preparedness strategy. When an emergency happens, the communication between these beacons and the smartphones and tablets of emergency responders will give emergency responders the necessary situational awareness at the right time and the right location. This positioning solution is based on a thorough analysis of signal propagation and coverage measurements using machine-learning tools. This solution will then be used to prototype a Location-Based Service (LBS). This software application gives emergency responders necessary and sufficient situational awareness while responding to building emergencies. This LBS is designed and developed based on a comprehensive analysis of buildings’ information, emergency response plans, and emergency records.
This combined repository of information is processed using text mining to identify points of interest at any building and during any emergency and give a statistical model for the workflow of emergency responders between these points. Deploying BLE beacons at these points will help expedite the preparation and execution of emergency response plans, and hence enhance the situational-awareness, efficiency, and effectiveness of emergency responders. This is the first project to combine data analytics and IoT in the public safety domain. Its findings will have a ripple effect on the design and development of software solutions for emergency responders.
Multi-sensor miner data using smart computing platforms
The mining industry is a significant contributor to the Canadian economy, requiring close to 100,000 people to be hired by 2020. However, mining remains the second most dangerous job across the planet with 46.9 fatalities per 100,000 workers. Until the recent MINER act, there is little interest in providing communication services in mines. The MINER act made tracking all coal miners mandatory. In this project, we aim to develop a smart computing platform that combines data from digital wireless radio, thermal and video images in real time. This platform will incorporate tracking algorithms to aid in precisely locating miners, explosives and vehicles in mines continuously. This is very difficult due to the very nature of the mines. Extremely harsh, irregularly confined, and rough mine environments make radio wave propagation unpredictable. Furthermore, low visible light conditions prevent effective surveillance using cameras. Increasing number of sensors helps accurately locate targets, but will also increase the complexity of the algorithms. Using a variety of sensors will require several algorithms to run simultaneously to extract and fuse useful information from noisy data gathered from the mine in real-time. Especially identification of moving object in low-light video and thermal imaging is computationally very intensive. Hence, such a platform can only be implemented on a supercomputer, or a network of very fast computers. For this purpose, our work will rely on SOSCIP’s extensive computational capabilities. The result of this work will lead to significant improvements in safety of the mine personnel and better resource allocation and management in the mine
Multilevel streaming data analytics infrastructure for predictive analytics
Predictive Analytics for digital media processing is facing the challenge of handling an increasing volume, velocity and variety of big data and there has been an enormous drive lately in the area of streaming data analytics. We are rapidly moving towards the Internet of Things (IoT) where predictive analytics will need to analyze and integrate streaming data from many different devices and digital media sources including structured data from the traditional relational databases and unstructured data from the recent big data storage systems. Therefore, we need an infrastructure to enable long-term multilevel knowledge extraction where 1) the 1st level analytics performed by a stream processing engine will identify important data components from multiple data streams and move them into a memory buffer. 2) Then an in-memory data analytics engine will be used to perform the 2nd and the subsequent levels of analytics for knowledge extraction and integration with other big data sources. 3) Finally, only the important data stream components and the extracted knowledge can be stored for future analytics into a big data store. We propose to develop an infrastructure to facilitate complex multilevel predictive analytics and to streamline the process of knowledge extraction and integration for both streaming and non-streaming data. A variety of open source stream processing engines exist today. However, none support such multilevel analytics. We will use open source streaming and in-memory data analytics engines and SOSCIP’s cloud and big memory systems. The infrastructure will be validated using streaming financial and business news and social media data analytics for identifying business growth, stress and risk signals. It will contribute to Canada’s economy by leveraging predictive analytics for decision support in areas such as cybersecurity, health and e-government.
Next generation radio interferometry for astronomy, geodesy and radar ranging
Determining the properties of a pulsar and the interstellar medium through which its pulses of radio emission travel may appear to be an endeavor that is rather different from using a radar to localize a satellite and determine its properties. But both share the short bursts of radio emission and while in one case the pulses are refracted and in the other reflected, the analysis of the received radio emission turns out to require very similar techniques. Recognizing this synergy, this project launches a new dimension in the industrial academic collaboration between Thoth Technology Inc. and the University of Toronto, to jointly develop technology for using transient radio signals to localize sources and determine their properties, both to better understand pulsars and the interstellar medium and to enable Thoth to provide and commercialize unique radar capabilities. The project plans to first develop a common interface for the Thoth Algonquin Radio Observatory facilities, enabling the production of data compliant with the VDIF digital media standard, even at very high data rates, and to select and combine parts from the streams at will for rapid transmission and quick-look visualization. Next, we use BGQ resources to develop and optimize the data analysis, following new insights on how bursty signals in particular should allow simpler retrieval of properties of sources.
Personalized predictive risk for medical imaging radiation exposure
This project will build the expert team and create the tools required to understand the long-term effects of low dose radiation exposure from medical imaging on populations and facilitate the adoption of best practices to decrease the impact of imaging related radiation exposure. Although this project will focus on low dose radiation exposure from medical imaging, the tools and approaches that will be developed will be transferrable to other specialties in the health care field.
To achieve these goals, we propose to:
(1) Develop a standard-based, extensible data model for a provincial platform to reconcile radiation dose with patient medical information;
(2) Develop generic data mining tools that can be customized to query Big Data repositories for use with major modalities, including Electronic Patient Records and Electronic Medical Records across the province;
(3) Investigate algorithmic approaches to extract and structure data from reports and text in DICOM headers, and from free text in notes and summaries in electronic patient records;
(4) Develop intelligent terminology mapping and quality assurance mechanisms to ensure data quality; and,
(5) Develop a feedback mechanism for decision support at the point of care related to imaging procedures.
Short-term prediction of border crossing times for trucks
International borders, where trucks are subject to immigration, customs and security inspection, can impose extreme unpredictability on delivery times. This is a particular problem for trucks serving just-in-time supply chains where goods must arrive within narrow time windows. For example, at automotive assembly plants numerous components must arrive shortly before they are needed on the assembly line – even one component arriving late could stop the line. Providing short-term crossing time predictions can help alleviate this problem. If a truck can be warned of a major delay a few minutes or hours before it gets to the border, it will be possible for supply chain managers to initiate actions such as redirecting the truck to an alternative crossing, dispatching a substitute shipment from a different location, or warning the receiving facility of the delay so that contingency plans can be implemented. Traffic conditions at the border can change rapidly, so trucks need predictions, rather than current reports, of crossing times to help them plan ahead. No such predictions, however, are currently available. In our proposed project, researchers at the University of Windsor will team with mobile application developer Innovex Inc. to develop a smartphone application that will provide accurate predictions of crossing times for trucks over a range of time intervals from 10 minutes to two hours, and deliver notification of likely schedule delay to shippers, dispatchers, drivers, receivers and other supply chain participants.
Smart analytics for smart grid
How we use energy reveals vital information about our everyday behaviour – from our work and school schedules to when we are likely to be sleeping, eating and performing simple chores such as laundry or washing dishes. It even reveals when we are on vacation. While this information in the wrong hands could put us at risk, it also plays an important role in reducing our carbon footprint and ensuring the cost of energy is kept relatively low. Power generation companies use information derived from microclimate, small areas within general climate zones, to accurately predict power generation needs. Microclimate information is captured from smart meters, which wirelessly transmits data from our homes to utility companies. Considering that more than one million smart meters have been installed in Ontario homes since 2010 and that number continues to grow, it’s necessary to find a way to ensure the data captured by smart meters is protected and secure from threat.
Trade Promotion Forecasting and Optimization
This project addresses two specific challenges related to promotional planning in the consumer-packaged goods sector. The first output will be to develop and evaluate models that statistically predict the impact of Unilever promotions on category and product share across different retailers. This effort will lead to the creation of trade promotion optimization techniques that enables planning of promotional activities. The second output is to simulate potential outcomes of various combinations of promotional programs across different retailers in such a way as to inform investment decisions early in the planning process. Finally, the project aims to develop a systematic approach for allocation of promotional spend that permits continuous adjustment as market and competitor conditions change. These three objectives will be developed using a proof of concept approach: therefore, specific objectives are as follows: 1. Develop a POC that provides models, simulation results and a systematic model for allocation of promotional spend using limited data from a few product categories and customers. 2. Expand the POC to address a larger set of categories and customers. 3. Build on objective two to create an automated model and process.
Ubiquitous robotics to support older adults with dementia
Dementia affects more than 35 million people worldwide, a number that is expected to almost double every 20 years. It is such a significant challenge that the G7 “Global Action Against Dementia” has declared providing support to people with dementia as a global challenge that must be solved immediately. The G7, supported by significant research and clinical findings, stated that technology will be one of the solutions in supporting this demographic. Robotics has recently been seen as a potential solution in supporting older adults with dementia. Robots have been developed that can provide remote monitoring, support during common self-care activities, socialization capabilities, and cognitive stimulation. However, we have yet to see a truly effective robotic system be made available on the market.
Previous research by our team and others in the field have identified key functions that technology, including robotics, should include in order to best support an older adult with dementia in their own homes. These include: 1) the ability to communicate with healthcare providers and family; 2) the ability to monitor the completion of common activities of daily living and provide prompts and reminders as necessary; and 3) the ability to monitor the safety of the older adult, and automatically detect adverse events such as falls. Currently, it is nearly impossible to include all of these functions on a single robotic platform due to the increase need for computational capabilities that will result in a very complex system, and more importantly, result in a system that is too costly to purchase. We will work with our industry partner CrossWing, Inc. to advance the systems that we have developed and to integrate them into one system delivered on CrossWing’s new robotic platform with the three capabilities described previously—1) tele-medicine/presence; 2) activity monitoring and prompting; 3) safety monitoring and emergency response. We will overcome the aforementioned limitations faced by other robotics initiatives through the novel approach of using the SHARCNET’S Cloud Analytics Platform to add these applications to CrossWing’s platform without the need for additional extensive computing resources located locally on the robot. If successful, this project will result in the first commercially available, multi-functional mobile robotic platform that includes the applications that have been identified as necessary in supporting older adults with dementia in their own homes and communities.
Social media analytics for early detection of foodborne disease
Foodborne disease has emerged as a serious and underreported public health problem with high health and financial costs. The World Health Organization (WHO) identifies foodborne illness outbreaks as a major global public health threat in the twenty-first century. Traditional surveillance systems such as Canadian Notifiable Disease Surveillance System capture only a fraction of the estimated 4 million annual cases of foodborne illness in Canada. They rely on the collection of numerous indicators including clinical symptoms, virology laboratory results, hospital admissions and mortality statistics resulting in a median delay of 6.5 days between case report from clinicians to the health departments. Public health decision-makers consider the delayed notification as a barrier to investigating foodborne disease, as it can potentially distribute geographically across great distances. Early detection of foodborne disease can reduce the number of exposed individuals by removing contaminated product from retail and foodservice outlets, increasing public awareness, and offering a more timely preventative and therapeutic measures to exposed individuals. We propose that social media data should be exploited as a complementary component of the traditional surveillance systems. The enormity and high variance of the information that propagates through large user communities presents the opportunity to mine the data for signals of foodborne disease activity; analyze illness patterns qualitatively and quantitatively; and to predict future outbreaks. We propose a host of social media-based predictive models to characterize and detect upcoming foodborne illness outbreaks through ambient tracking and monitoring over users’ conversations in social media. The objective is to advance research on foodborne disease detection from non-traditional sources to supply health decision makers with situational awareness.
Industry Partner(s): AdaptCore
PI & Academic Institution: Ebrahim Bagheri, Ryerson University
# of HQPs: 1
Platform: Cloud
Focus Areas/Industry Sector: Digital Media, Health
Technology: Artificial Intelligence, Internet of Things, Real-Time Analytics