Day 1 - 30th SeptemberWednesday, 30 September 2020

Leading researchers and medical professionals have been at the forefront of discovering, identifying, isolating and finding a cure for Covid-19. However, there is more to healthcare beyond the pandemic spanning from personalized drug discovery to genome sequencing to image diagnostics to physician scheduling to asset management – use cases that deliver enhanced patient outcomes using the power of AI.

HPE & Nvidia are market leaders in high-performance computing & AI respectively have been busy delivering joint solutions in the Healthcare segment. We have recently launched a suite of AI enabled healthcare solutions that may of interest to you:

• Optimized infrastructure for Cryo-EM & Genomics
• AI-as-a-Service for Researchers
• Accelerated Image Diagnostics
• Hospital Operations, Scheduling & Patient Safety

Join us in this session to learn more on how you can accelerate the adoption of AI within your organization as well as mitigate the cash flow challenges in deploying these solutions.

• Will personalised medicine be achievable without clear regulatory guidance? How can we get state of the art gene therapies into the market as soon as possible?
• Machine learning for drug target interaction prediction
• Matching suitable patients for clinical trials through deep learning – what partnerships and stakeholders need to be involved here? How to overcome data accessibility challenges associated.

• Clinical decisions need to be made quickly, often with little information and no specific guidelines.
• Using our Electronic Health Record from 13 of our hospitals, we gathered demographic, laboratory, clinical, and treatment data of over 14,000 COVID-19 admitted patients
• Models to predict mortality and assess disease severity, predict need for intubation and survival post-intubation, methods for continuous and remote monitoring, as well as NLP on medical notes, will be presented
• New, COVID-19 specific, challenges and considerations for this effort will be discussed

Artificial intelligence (AI) is the catalyst to design new drugs for the most challenging targets. Using AI, medicinal chemists can now virtually screen new molecules for interactions with proteins of interest before pursuing predicted hits as potential drug candidates.

New AI-platforms can screen at an order of magnitude of tens of billions of molecules -- with speed, scale, and success not seen before. With the need for accelerated drug discovery accentuated by the COVID-19 pandemic, many are hopeful these new AI-platforms can play a role. Atomwise, the startup that invented convolutional neural networks for small molecule drug discovery, is currently partnering with fifteen global research teams to pursue broad-spectrum therapies to not only target this pandemic, but potential future outbreaks.

Join Atomwise CEO Dr. Abe Heifets for a Q&A session with University of Manitoba Faculty of Science Professor Jörg Stetefeld and University of Connecticut Professor Dr. James Cole to discuss their collaboration in exploring broad-spectrum therapies for COVID-19 and other coronaviruses, the use case of AI in drug discovery and how access to technology can catalyze innovation in research.

• Computational Phenomics - the systematic study of human phenotypes and diseases models, are crucial to the success of data-driven clinical development
• The future of data-driven drug discovery relies on combining trial-level and individual-level patient data with predictive analytics to expedite and minimize the time in trials by knowing the performance of historical clinical trials.
• Preparing multiple different existing data sources to feed machine intelligence models to identify various “drivers” of parameters associated clinical trial enrollment lifecycle

Alberta, a province in Canada of 4.5 million people, has among the highest rates of opioid prescribing in the world and opioid prescriptions were a key factor in driving the current opioid crisis. Professional bodies have released clinical practice guidelines for safe opioid prescribing. Prescription monitoring programs like Alberta’s Triplicate Prescription Program (TPP), which are mandated to ensure the appropriate use of opioids and reduce their risk, currently use metrics based on these prescribing guidelines.

Currently, no risk calculators are used in practice that quantify the actual risk of hospitalization or death following a dispensation of prescribed opioids. OKAKI is a public health informatics social enterprise that manages Alberta’s prescription monitoring system. OKAKI and its partners undertook a study to use machine learning (ML) with the province’s large health administrative datasets to develop and test a model to predict the 30-day risk of hospitalization, emergency department visit (ED) or death, at the time of an opioid dispensation. A model that performed better than current approaches could potentially be used to support province-wide monitoring and interventions. The presentation will summarize the study context, methods and results, as well as key challenges in implementing this type of prediction model into public health practice.