• Times in EDT

  • 11:00

    Welcome Note

  • AI IN HEALTHCARE: AN INTRODUCTION

  • 11:05

    Improving Efficiency Using AI

  • ARTIFICIAL INTELLIGENCE FOR DIAGNOSTICS

  • 11:30
    Saeed Hassanpour

    AI and Histopathological Characterisation of Microscopy Images

    Saeed Hassanpour - Associate Professor - Hassanpour Lab, Geisel School of Medicine, Dartmouth

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    Artificial Intelligence and Histopathological Characterization of Microscopy Images With the recent expansions of whole-slide digital scanning, archiving, and high-throughput tissue banks, the field of digital pathology is primed to benefit significantly from deep learning technology. This talk will cover several applications of deep learning for characterizing histologic patterns on high-resolution microscopy images for cancerous and precancerous lesions. Also, recent advances and future directions for developing and evaluating deep learning models for pathology image analysis will be discussed.

    Dr. Saeed Hassanpour is an Associate Professor in the Departments of Biomedical Data Science, Computer Science, and Epidemiology at Dartmouth College. His research is focused on the use of artificial intelligence in healthcare. Dr. Hassanpour’s research laboratory has built novel machine learning and deep learning models for medical image analysis and clinical text mining to improve diagnosis, prognosis, and personalized therapies. Before joining Dartmouth, he worked as a Research Engineer at Microsoft. Dr. Hassanpour received his Ph.D. in Electrical Engineering with a minor in Biomedical Informatics from Stanford University and a Master of Math in Computer Science from the University of Waterloo in Canada.

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  • 11:55
    James O'Sullivan

    Associating Genotype and Phenotype in Infectious Disease Pathogens

    James O'Sullivan - Post-Doctoral Researcher - Roche

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    Deep Learning for audio-based sociability assessment in patients with neuro-developmental disorders.

    I was born in Dublin, Ireland. I received my undergraduate degree in Electrical Engineering from UCD, Ireland, my Master's degree in Biomedical Engineering from UCD, and my PhD in Neural Engineering from Trinity College, Ireland. I then moved to New York where I completed a four year postdoc in the lab of Nima Mesgarani at Columbia University. In May 2019 I began a postdoc in Roche, also in New York. In my spare time, I like to go rock climbing and play music.

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  • 12:20

    COFFEE BREAK

  • AI AIDED DRUG DISCOVERY

  • 12:30
    Pablo Cingolani

    Machine Learning Pipelines

    Pablo Cingolani - Principal Scientist - AstraZeneca

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    AI for Immuno Oncology on Liquid Biopsies

    Immuno-Oncology tries to develop treatments and drugs that help the immune system to fight cancer. This approach has gained a lot of attention in recent years due to the success of stories of some cancer immunotherapies and checkpoint inhibitor drugs. Nevertheless, immuno-oncology does not work on every patient. We explore some challenges and opportunities when using ML and AI, particularly in the context of NGS Sequencing from ctDNA (liquid biopsies) to select biomarkers that help us understand which patient would benefit using immuno-oncology drugs.

    Pablo is a leader in computational biology and life sciences with vast experience in algorithms/methods development, engineering, big data analysis, AI/machine learning and cloud/high-performance computing. At AstraZeneca, he applies bioInformatics & AI techniques to improve research and drug development in Oncology.

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  • 12:55
    Ola Ajayi

    Predicting Drug Interactions Using Machine Learning

    Ola Ajayi - Senior Data Scientist III - Biogen

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    Predicting Drug Interactions Using Machine Learning Patients taking multiple medications be it prescription or over-the-counter tend to experience more adverse events. A recent survey show that simultaneous use of different products affects about half of the elderly populations with regards to increasing risk of morbidity. The goal of this solution is to develop a predictive model to determine leading indicators of drugs that are likely to interact with other drugs when administered within a specified period. The use of de-identified patient level data including demographics, prescribers, conditions and diseases as well as current and historical medications could be mined and explored to develop a novel solution. The intention is to use a mixture of publicly available data sources and real-world data in this model. Several stratifications and scenarios will be considered in the initial exploratory data analysis prior to using supervised and unsupervised learning model including ensembles to arrive at final suitable model. We will factor in groupings by similar diseases, co-morbidities, consider drug metabolism, route of administration and molecule class. A propensity or risk score will be calculated as an outcome. A data driven method of identifying drug interactions that will enhance efforts at efficacy of drugs and reduce morbidity is the objective here. The benefits include but are not limited to improving patient qualify of life, enhanced understanding of compounds, indications, contraindications for manufacturers. The final model in this solution will have a high measure of accuracy.

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  • 13:20
    Dalton Sakthivadivel

    Creating Personalised Neuromedicine Using Artificial Intelligence and Brain Modelling

    Dalton Sakthivadivel - Neuroscience Researcher - Laboratory for Computational Neurodiagnostics, in Stony Brook University.

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    Creating Personalised Neuromedicine Using Artificial Intelligence and Brain Modelling

    The utility of data in medicine is rapidly increasing, due to better precision and greater availability. To maximise the impact this will have, clinicians and researchers are applying unique analysis methods to these data and translating the results into patient care. One example of this is in personalising medicine, which entails learning about and responding to a patient’s unique condition. In the clinical neurosciences, we can apply modelling insights to patient care, on an individual level, by using artificial intelligence. Through the intelligent analysis of neuroimaging and other diagnostic data we can learn about a patient’s brain, and then simulate a patient by building a personal brain model. This enables clear and correct diagnosis, investigation of treatments, and prediction of outcomes. Outlined through case studies are some recent advances in the field of personalising medicine using computational neurodiagnostics, and how they have been performed. In addition to the state of the art, the importance of this new field will be discussed, as well as how it will affect medicine, and where will it be headed in the future.

    Dalton is a neuroscience researcher affiliated with the Laboratory for Computational Neurodiagnostics, in Stony Brook University. He applies computational methods, primarily based on brain modelling, to answer questions in neuroscience and psychiatry.

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  • 13:45

    ROUNDTABLE DISCUSSIONS

  • 14:05

    COFFEE BREAK

  • 14:15
    Jonathan Stokes

    Antibiotic-Resistance

    Jonathan Stokes - Banting Fellow - Broad Institute of MIT and Harvard

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    To address the antibiotic-resistance crisis, we trained a deep neural network to predict new antibiotics. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub – halicin – that is structurally divergent from conventional antibiotics and displays activity against a wide spectrum of pathogens. Halicin also effectively treated Clostridioides difficile and Acinetobacter baumannii infections in mice. Deep learning approaches have utility in expanding our antibiotic arsenal.

    Jonathan Stokes is a Banting Fellow in the laboratory of James Collins at the Broad Institute of MIT and Harvard. He received his BHSc in 2011, graduating summa cum laude, and his PhD in antimicrobial chemical biology in 2016, both from McMaster University. His research applies a combination of chemical biology, systems biology, and machine learning approaches to develop novel antibacterial therapies with expanded capabilities over conventional antibiotics. Dr. Stokes is the recipient of numerous awards, including the Canadian Institutes of Health Research Master’s Award, the Colin James Lyne Lock Doctoral Award, and was ranked first of just 23 postdoctoral scholars to be awarded the prestigious Banting Fellowship.

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  • 14:40

    Single Cell Genomics to Drug Targets

  • 15:05

    PANEL: The Importance of Machine Learning in Diagnosing & Treating Cancer

  • 15:45
    1:1 Speed Networking

    1:1 SPEED NETWORKING

    1:1 Speed Networking - - NETWORKING SESSION

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    Join a 1-to-1 Speed Networking session to be randomly paired with others with a similar interest for short video calls to expand your network and connect with others.

  • 16:00

    END OF DAY 1

  • Times in EDT

  • 11:00

    Welcome Note

  • START UP SESSION

  • 11:05
    Łukasz Kidziński

    Clinical Motion Lab in Your Pocket

    Łukasz Kidziński - Co-Founder - Saliency

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    Clinical Motion Lab in Your Pocket

    Many neurological and musculoskeletal diseases impair movement, which limits people’s function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and highly trained personnel. We developed AI-based algorithms for quantifying gait pathology using commodity cameras. Our methods increase access to quantitative motion analysis in clinics and at home and enable researchers to conduct studies of neurological and musculoskeletal disorders at an unprecedented scale.

    Łukasz Kidziński is a co-founder of Saliency and a research associate in the Neuromuscular Biomechanics Lab at Stanford University, applying state-of-the-art computer vision and reinforcement learning algorithms for improving clinical decisions and treatments. Previously he was a researcher in the CHILI group, Computer-Human Interaction in Learning and Instruction, at the EPFL in Switzerland, where he was developing methods for measuring and improving engagement of users in massive online open courses. He obtained a Ph.D. degree at Université Libre de Bruxelles in mathematical statistics, working on frequency-domain methods for dimensionality reduction in time series.

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  • 11:30

    AI Powered Chatbots to Combat Substance Abuse

  • HEALTH & WELLNESS

  • 11:55
    Jessie Li

    Transfer Learning for the Prediction of Atrial Fibrillation and Sleep Apnea

    Jessie Li - Global Head of Data Science - Jawbone Health

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    Transfer Learning for the Prediction of Atrial Fibrillation and Sleep Apnea

    Inexpensive and highly portable medical sensing technology has the potential to deliver savings for healthcare providers, through screening and early detection. However, the data necessary to support machine learning models on these newly developed sensor modalities - sensor streams together with time-aligned, expert-annotated labels - is often difficult and expensive to obtain. By contrast, high-quality data suitable for machine learning on better-established modalities, such as electrocardiogram (ECG), is abundant and often free. We propose a method allowing for the use of such easy-to-come-by data in building models on photoplethysmography (PPG) sensor modalities. With this method, data requirements are reduced to streams of PPG data time-aligned with readings from ECG together with labelled outcome for ECG. We demonstrate the method by building models for atrial fibrillation and sleep apnea based on data from a wrist-worn PPG sensor, with the only labels coming from publicly available ECG data. We find that the models developed using the transfer learning approach outperform models trained directly on the PPG sensor and are competitive with state-of-the-art ECG-based models.

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  • 12:20

    COFFEE BREAK

  • NLP

  • 12:30
    Amir Tahmasebi

    Natural Language Processing for Healthcare

    Amir Tahmasebi - Senior Director of Machine Learning & AI - CODAMETRIX

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    Natural Language Processing for Healthcare

    With recent advancements in Deep Learning followed by successful deployment in natural language processing (NLP) applications such as language understanding, modeling, and translation, the general hope was to achieve yet another success in healthcare domain. Given the vast amount of healthcare data captured in Electronic Medical Records (EMR) in an unstructured fashion, there is an immediate high demand for NLP to facilitate automatic extraction and structuring of clinical data for decision support. Nevertheless, the performance of off-the-shelf NLP on healthcare data has been disappointing. Recently, tremendous efforts have been dedicated by NLP research pioneers to adapt general language NLP for healthcare domain. This talk aims to review current challenges researchers face, and furthermore, reviews some of the most recent success stories.

    Amir Tahmasebi is the director of machine learning and AI at CODAMETRIX, Boston, MA. He is also a lecturer in Electrical and Computer Engineering Department at Northeastern University, Boston, MA. Prior to joining CODAMETRIX, Dr. Tahmasebi was a Principal R&D Engineer at Disease Management Solutions Business of Philips HealthTech, Cambridge, MA. Dr. Tahmasebi’s research is focused on patient clinical context extraction and modeling through image analysis and Natural Language Processing, outcome analytics and clinical decision support. Dr. Tahmasebi received his PhD degree in Computer Science from the School of Computing, Queen's University, Kingston, Canada. He is the recipient of the IEEE Best PhD Thesis award and Tanenbaum Post-doctoral Research Fellowship award. He has been serving as an industrial Chair for IPCAI conference since 2015. Dr. Tahmasebi has published and presented his work in a number of conferences and journals including AMIA, JDI, IEEE TMI, IEEE TBME, MICCAI, IPCAI, HBM, SPIE, RSNA, and SIIM. He has also been granted more than 10 patent awards.

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  • 12:55
    Enrico Santus

    Making sense of Big Data - Challenges and Opportunities with NLP

    Enrico Santus - Senior Data Scientist - Bayer

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    Making sense of Big Data - Challenges and Opportunities with NLP

    The recent pandemic has shown that despite the incredible amount of available data, we are still unable to turn it into actionable insights. After discussing the current costs of not exploiting this information, I will describe a series of recent works where Natural Language Processing and, more in general, Artificial Intelligence techniques have been utilized to optimize clinical processes, drug development and adverse events monitoring.

    Enrico Santus is a senior data scientist at Bayer. After his PhD at the Hong Kong Polytechnic University, Enrico joined the group of Regina Barzilay at CSAIL, MIT. His academic career includes affiliations with the King's College of London, the University of Pisa, the University of Stuttgart, the Nara Institute of Technology and Harvard. His work touches topics such as NLP in Oncology, Cardiology and Palliative Care. Enrico has also worked on Epidemiology, Fake News Detection, Sentiment Analysis and Lexical Semantics. As of today, Enrico has published over 50 papers, with over 637 citations. He collaborated to the creation of The Prayer (artist: Diemut Strebe), a mouth-shaped robot that pronounces original prayers, generated with Artificial Intelligence, exposed at the Centre Pompidou, in Paris. He was also involved in the creation of Safe Paths, the MIT tracing app.

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  • MEDICAL IMAGING

  • 13:20

    Applying CNN's to Medical Imaging

  • 13:45

    ROUNDTABLE DISCUSSIONS

  • 14:05

    COFFEE BREAK

  • PUBLIC HEALTH

  • 14:15

    Evaluating the Impact of Determinants of Health

  • 14:40
    Salim Samanani

    Using Machine Learning to Predict 30-Day Risk of Overdose Hospitalization, Emergency Visit or Death Among Albertans Who Receive a Dispensation of Prescription Opioids

    Salim Samanani - Founder and Medical Director - OKAKI

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    Using Machine Learning to Predict 30-Day Risk of Overdose Hospitalization, Emergency Visit or Death Among Albertans Who Receive a Dispensation of Prescription Opioids

    Prescribed opioids are a key factor in driving the current opioid crisis in Alberta (AB), a province of Canada, and nationwide. Over 400,000 patients a year currently receive at least one opioid prescription in AB, out of a population of 4.5 million. Currently, no risk calculators are available that quantify the risk of hospitalization or death following a dispensation of prescribed opioids and prescribers must rely on guidelines to assess risk. OKAKI is a public health informatics social enterprise that manages Alberta’s prescription monitoring program. 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 as a clinical decision aid or 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 in clinical or public health practice.

    Dr. Samanani is a physician executive with more than 20 years of clinical, public health, and health system analytics experience. In 2008, he founded OKAKI (Blackfoot meaning "be wise"), a public health informatics social enterprise, headquartered in Alberta, Canada. For over a decade, OKAKI has leveraged IT and data to help address challenges in indigenous health, environmental health, emergency and disaster response, immunization, chronic disease and prescription monitoring. In 2019, OKAKI and its partners launched a precision public health initiative, with an initial focus on predicting and reducing opioid overdoses.

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  • 15:05

    PANEL: The Future of AI in the Health and Pharmaceutical Industry

  • 15:45
    1:1 Speed Networking

    1:1 SPEED NETWORKING

    1:1 Speed Networking - - NETWORKING SESSION

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    Join a 1-to-1 Speed Networking session to be randomly paired with others with a similar interest for short video calls to expand your network and connect with others.

  • 16:00

    END OF EVENT

AI in Healthcare Virtual Summit

AI in Healthcare Virtual Summit

25 - 26 March 2021

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