(FoRCE): Powering clinical trials research through a secure and integrated data management platform
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.
A parallel algorithm for quantum circuit synthesis
Quantum circuit synthesis is an important step in the process of quantum compilation. Given an arbitrary unitary operation, quantum circuit synthesis is the process that constructs a quantum circuit using only gates from a universal gate set which is either exactly, or approximately equivalent to the original operation. The currently known algorithms for multi-qubit circuit synthesis run in exponential time and rely on the generation of databases many GBs in size to complete the search; synthesis of circuits of more than 3 qubits up to a certain length is infeasible using current methods due to this exponential scaling. We have developed a framework and accompanying software that uses time/memory tradeoffs and parallel collision finding techniques to synthesize circuits. A simple implementation using 16 OpenMP threads found that this approach can increase the speed of synthesis over previous algorithms, as well as synthesize larger circuits. In order to fully reap the benefits offered by this algorithm, we are developing a hybrid OpenMP/MPI algorithm, with the hopes of scaling up this method to thousands of cores or more.
Big cardiac data
We propose to develop a cloud based image centered informatics system powered by newly developed big data analytics from our group for automatic diagnosis and prognosis of heart failure (HF). HF is a leading cause of morbidity and mortality in Ontario and around the world. There is no cure. Therefore early diagnosis and accurate prediction is very critical before the symptoms appears. However, previously lack of efficient image analytics tool has been the major pitfall to have an accurate prognosis and early diagnosis system. The system will greatly improve the clinical accuracy and enables accurate early diagnosis and prediction by intelligently analyzing all associated historical images and clinical reports. The final system will not only greatly reduce the sudden death and irreversible cardiac conditions, but also offers a great optimization of decision system in current healthcare. This project is based on strength built upon one successful SOSCIP project and two OCE projects.
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.
Detecting and Responding to Hostile Information Activities: unsupervised methods for measuring the quality of graph embeddings
The rise in online organized disinformation campaigns presents a significant challenge to Canadian national security. State and non-state hostile actors manipulate users on social media platforms to advance their interests. Patagona Technologies is a Toronto-based software development company started by two Ryerson alumni. The project with Ryerson University is a larger initiative with the Canadian Department of National Defense to address the challenges posed by online hostile actors by analyzing the structure and content of social networks.
HPC cloud analytics / machine learning support for Watson Pepper clinical study
Skin cancer is the most common type of cancer. 80,000 cases of cancer are diagnosed in Canada every year. 5000 of these cases are melanoma, the deadliest form of cancer (Canadian Skin Cancer Foundation). Current prevention efforts to reduce skin cancer focus on educating individuals on preventative actions that they can take to reduce the risk of this cancer. However, research has shown that both communication failure and information overload are significant problems affecting the quality of patient centered care. Social robotics and artificial intelligence have been used effectively to communicate and positively influence behavior, thus this research proposes to develop and test these combined technologies as an intervention for skin cancer prevention education.
The research team and collaborating research partner IBM will integrate IBM Watson cognitive computing applications with Softbank Robotics advanced robotics platform, the Pepper robot. The Watson Pepper prototype will be used as a controlled variable in a randomized controlled clinical trial (N = 200) to assess the efficacy of socially assistive robotics intervention for behavioural change in skin cancer prevention knowledge and practices among medical patients, the first clinically tested implementation of a Watson Pepper robot for healthcare communication. The research proposes commercialization and business implementation of the integrated IBM Watson robot in an expanded scale and scope of healthcare communication applications. To support the achievement of this innovative technology milestone, SOSCIP will provide the critical cloud data analytics and memory capacity to support the analysis and modeling of the large multivariate data sets associated with this project.
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.
Machine Learning Methods for Behavioural Biometrics
The project will focus on creating a Data architecture that will be composed of models, policies, rules and standards that will monitor which data is collected, and how it is stored, arranged and integrated into the system. The task of the professor and the student will be to create a data architecture and a machine learning algorithm that will help build a robust user-profile system that will help extract, store, build and analyse up to 1 million user profiles. This profile system will also be in charge of generating at least 50 behavioral data or 64 bytes of data per second. Simultaneously, the given data architecture will provide over 5 million user-profile recognitions per day through the use of predictive modeling and the given REST API call. This will help detect suspicious activities and anomalies without the use of browser cookies, location, and hardware information. For this project, the professor and the student will be given access to a part of F8th’s repository and must work together as a team to build a user-profile system that will help extract the given user profiles. This data architecture will help tolerate the behavioral data noise caused by the modification of input devices such as a mouse, keyboard, mobile and laptop. As F8th IDaaS is an Identity as a Service Solution (as described above), it is necessary to provide predictions on the given trained models (also known as behavioral biometrics). The market requires the predictions of the models to be done in less than 1 second (something that will be discussed later on when the project is being worked on by the professor and the student). The service must be provided at a reasonable cost and time; hence, it is very important to monitor the data as well as the resource consumption continuously. Lastly, as the project is to be worked on between the professor and the student as a team-constant discussions, comparisons, and decisions regarding the quality and the affordability of the solution will have to be done by the student and the professor.
Machine Learning Methods for Behavioural Biometrics
The project will focus on creating a Data architecture that will be composed of models, policies, rules and standards that will monitor which data is collected, and how it is stored, arranged and integrated into the system. The task of the professor and the student will be to create a data architecture and a machine learning algorithm that will help build a robust user-profile system that will help extract, store, build and analyze up to 1 million user profiles. This profile system will also be in charge of generating at least 50 behavioural data or 64 bytes of data per second. Simultaneously, the given data architecture will provide over 5 million user-profile recognitions per day through the use of predictive modelling and the given REST API call. This will help detect suspicious activities and anomalies without the use of browser cookies, location, and hardware information. For this project, the professor and the student will be given access to a part of F8th’s repository and must work together as a team to build a user-profile system that will help extract the given user profiles. This data architecture will help tolerate the behavioural data noise caused by the modification of input devices such as a mouse, keyboard, mobile and laptop. As F8th IDaaS is an Identity as a Service Solution (as described above), it is necessary to provide predictions on the given trained models (also known as behavioural biometrics). The market requires the predictions of the models to be done in less than 1 second (something that will be discussed later on when the project is being worked on by the professor and the student). The service must be provided at a reasonable cost and time; hence, it is very important to monitor the data as well as the resource consumption continuously. Lastly, as the project is to be worked on between the professor and the student as team-constant discussions, comparisons, and decisions regarding the quality and the affordability of the solution will have to be done by the student and the professor.
Mobilizing ‘Big energy data’ for building conservation to socialize sustainability
This project will establish the first customer-centric universal tool using mobile/cloud technology to deliver & share energy information electronically with energy customers to stimulate social change, encourage conservation & help manage/reduce GHG emissions.
A global desire to manage climate change is here. There needs to be a joint effort to help the energy user gain access to electronic information, so the data can be used to better manage conservation, sustainability and technology integration. Innovation in the energy sector continues to create data silos, as new technologies used don’t allow for the data to freely flow out of the systems to other solutions. Customers require easy access & a simplified understanding of electric information (not print or PDFs). For this to happen, the end customer requires data to be managed, simplified & shared, so information can be “open” to innovation and electronic data to be standardized.
The market needs Green practices and conservation to flourish in the private sector and public sector. Internationally and in Ontario, there are regulatory and political requirements to provide end-user access to electronic data, but without a simplified roadmap to interconnectivity how will it happen? The market needs linkages to the “Internet of Things” to learn, inform and conserve. In a few short years, there will be more than 25 billion devices generating data on every topic imaginable. The energy customer and building owner needs simplified data from multiple sources. Screaming has an opportunity to deliver this information cost-effectively to the utility, government & customer.
Parallel programs for autocorrelation problems (PAAP)
Autocorrelation problems are a rich source of extremely hard computational challenges for which conventional parallel computing has been the only reasonably successful approach. Sequences with constant autocorrelation are called complementary and have a wide range of applications, including: Coding Theory, Telecommunications, Image Compression, and Wireless Communication Protocols.
Over the past decade, the PI has been able to find several new complementary sequences in a series of papers with his collaborators in Canada, the United States, Australia and Europe. It has become apparent, though, that the current algorithms have reached a point of saturation, in that they are unsuitable for producing new results. The advent of hardware accelerator technologies such as the GPU is a promising new direction. In some problems GPU‐enabled algorithms have been reported to exhibit a 2000‐fold speedup, which is quite significant. Therefore, it is clear that hardware accelerator technologies provide vast opportunities for innovation in scientific computing. It is a fortuitous coincidence that our partner, Maplesoft, based in Waterloo Ontario, has recently devoted a large part of their efforts in producing parallel versions of Maple, the flagship Canadian mathematical software product. Our proposal will benefit and complement the efforts of Maplesoft.
Prototyping and analysis of engines for integrated cryptography and compression
This project will use the Blue Gene/Q platform and the Agile Computing platform to prototype and analyze hardware and software implementations (engines) of novel algorithms for integrated cryptography and compression. Security analysis (cryptanalysis) is computationally very demanding. The high degree of parallelism and computing power offered by the Blue Gene/Q platform will enable the creation and cryptanalysis of powerful new cryptographic algorithms. The Agile Computing platform will provide a platform for prototyping implementation of novel algorithms that combine cryptography and compression on combined CPU+FPGA systems and thereby facilitate collaboration between IBM and the project participants.