• 08:30

    REGISTRATION & LIGHT BREAKFAST

  • 09:15

    Welcome Note

  • AI IN HEALTHCARE: AN INTRODUCTION

  • 09:30

    Improving Efficiency Using AI

  • PREDICTIONS

  • 09:50

    Deep Learning for Disease Prediction

  • ARTIFICIAL INTELLIGENCE FOR DIAGNOSTICS

  • 10:10
    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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  • 10:30
    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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  • 10:50

    COFFEE BREAK

  • 11:20
    Krzysztof Jerzy Geras

    Deep Learning for Audiobased Sociability Assessment in Neuro-Development Disorders

    Krzysztof Jerzy Geras - Assistant Professor - NYU School of Medicine

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    Towards solving breast cancer screening diagnosis with deep learning

    Although deep learning has made a stunning progress in the last few years, both in terms of engineering and theory, its real-life applications remain rather limited. One of the fields that has been anticipated to be revolutionized by deep learning for some time, yet proved to be much harder that many expected, is medical diagnosis. In this talk I will shed some light on my 4-year long journey in developing deep learning methods for medical imaging, in particular, for breast cancer screening. I will explain how we created a deep learning model that can perform a diagnosis with an accuracy comparable to experienced radiologists. To achieve this goal we needed a lot of perseverance, novel neural network architectures and training methods specific to medical imaging. I will also discuss the limitations of our work and what can likely be achieved in the next few years.

    Krzysztof is an assistant professor at NYU School of Medicine and an affiliated faculty at NYU Center for Data Science. His main interests are in unsupervised learning with neural networks, model compression, transfer learning, evaluation of machine learning models and applications of these techniques to medical imaging. He previously did a postdoc at NYU with Kyunghyun Cho, a PhD at the University of Edinburgh with Charles Sutton and an MSc as a visiting student at the University of Edinburgh with Amos Storkey. His BSc is from the University of Warsaw. He also did industrial internships in Microsoft Research (Redmond, working with Rich Caruana and Abdel-rahman Mohamed), Amazon (Berlin, Ralf Herbrich's group), Microsoft (Bellevue) and J.P. Morgan (London).

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  • AI AIDED DRUG DISCOVERY

  • 11:40
    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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  • 12:00
    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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  • PRECISION MEDICINE & PHARMACOGENOMICS

  • 12:20
    Gregory Ryslik

    From Single Cell Genomics to Drug Targets: Leveraging ML & AI to Discover Biological Insights

    Gregory Ryslik - Chief Data Officer - Celsius Therapeutics

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    From Single Cell Genomics to Drug Targets: Leveraging ML & AI to Discover Biological Insights

    The rapid development of single cell genomics allows and the ability to understand expression level differences on a cell by cell level allows for the creation of novel precision therapies targeted toward specific subpopulations. As a tissue sample generates up to 10,000 cells and every cell can provide a read out on up to 20,000 genes, even a few hundred samples can easily generate tens of billions of data points. Identifying the signal within such large datasets requires the use of powerful machine learning algorithms throughout the entire computational biology process. In this talk we will cover the power of combining single cell genomics with machine learning in the drug discovery context and provide several examples of how we are doing this at Celsius Therapeutics. Gregory Ryslik is a statistician, data scientist and artificial intelligence researcher with experience building and leading data initiatives in companies ranging across the biotech, autotech, healthtech and fintech domains. Prior to Celsius Therapeutics, he was vice president of data science at Mindstrong Health, a healthcare company transforming mental health treatment through measurement science and artificial intelligence. Previously, Greg was the senior director and head of data science at Faraday Future, an electric vehicle startup in Los Angeles as well as the leader of the service data science group at Tesla Motors in Palo Alto. Earlier in his career, he performed machine learning research and nonclinical biostatistics research at Genentech.

    Concurrently, Greg holds an adjunct assistant professor position at Pennsylvania State University and has lectured on statistics for artificial intelligence and machine learning at Stanford University Continuing Studies. He is also a fellow of the Casualty Actuarial Society, as well as a member of the American Academy of Actuaries. His research has been published in journals ranging from Nature to BMC Bioinformatics and has led to several software packages on mutational clustering.

    Greg holds a Ph.D. from Yale University in biostatistics, where he researched oncogenic mutation clustering embedded within protein structure. He also holds a master’s degree in statistics from Columbia University and an undergraduate degree in mathematics, computer science and finance from Rutgers University.

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  • 12:40

    NETWORKING LUNCH

  • 13:40
    Feixiong Cheng

    Networking Medicine and AI in Precision Cardio-Oncology: Learning from Cleveland Clinic Epic Systems

    Feixiong Cheng - Assistant Professor - Cleveland Clinic

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    Network Medicine and AI in Precision Cardio-Oncology:Learning for Cleveland Clinic Epic Systems

    There are over 15.5 million cancer survivors in the United States (U.S.) alone; furthermore, cardiovascular disease is a leading cause of death and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the U.S. Comorbidity between cardiovascular disease and cancer suggests an underlying shared disease etiologies, including genetic and environmental. One critical issue is that comorbidity is typically associated with various cancer treatments, termed cancer therapy-related cardiac dysfunction (CTRCD). However, there are no guidelines in terms of how to prevent and treat the new CTRCD in cancer survivors. In this talk, I will introduce a clinically relevant, network-based methodology for a comprehensive, unbiased network analysis of over 4,600 longitudinal cancer patients using clinical, laboratory and echocardiographic variables from our institutional, large-scale electronic medical records. Via network analysis, we identified four distinct subgroups who are statistically significantly correlated with incidence of CTRCD and patients’ mortality. Analysis of longitudinal patient-patient networks (20 years’ follow-up) reveals dosing-time-dependent (‘chronopharmacology’) CTRCD. Using clinical variable network analysis, we identified several clinically relevant predictors (i.e., Troponin-T and NT-proBNP) that are significantly associated with patients’ mortality. Compared to traditional machine learning approaches, network methodologies are more interpretable, visualizing the clinical decision boundary of cancer patients with CTRCD.

    Feixiong Cheng, PhD, is a principal investigator with Cleveland Clinic’s Genomic Medicine Institute. Dr. Cheng is a computational and systems biologist by training, with expertise in analyzing, visualizing, and mining data from real world (e.g., electronic health records, and health care claims) and experiments that profile the molecular state of human cells and tissues by interactomics, transcriptomics, genomics, proteomics, and metabolomics for precision medicine drug discovery and patient care. Dr. Cheng is working to develop computational and experimental network medicine technologies for advancing the characterization of disease heterogeneity, thereby approaching the goal of coordinated, patient-centered strategies to innovative diagnostics and therapeutics development. The primary goal of Dr. Cheng’s lab is to combine tools from genomics, network medicine, bioinformatics, computational biology, chemical biology, and experimental pharmacology and systems biology assays (e.g., single cell sequencing and iPS-derived cardiomyocytes), to address the challenging questions toward understanding of various human complex diseases (e.g., cardio-oncology, pulmonary vascular diseases, and Alzheimer’s disease), which could have a major impact in identifying novel real-world data-driven diagnostic biomarkers and therapeutic targets for precision medicine. From 2013 to 2017, Dr. Cheng was trained as Postdoctoral Research Fellow in the field of pharmacogenomics and network medicine across Vanderbilt University Medical Center, Northeastern University, and Dana-Farber Cancer Institute. During 2017-2018, Dr. Cheng was promoted to Research Assistant Professor working with two of the world’s leading experts in the field of network medicine, Drs. Albert-Laszlo Barabasi and Joseph Loscalzo, with dual appointment at Northeastern University and Harvard Medical School. Dr. Cheng has received several awards, including NIH Pathway to Independence Award (K99/R00), SCI highly cited papers reward, and Vanderbilt Postdoc of the Year Honorable mention.

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  • 14:00
    Niha Beig

    Keeping Gender in Mind: Applications of Machine Learning in Neuro-Oncology

    Niha Beig - PhD Candidate - Case Western Reserve University

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    Keeping gender in mind: Applications of Machine Learning in Neuro-Oncology

    Investigation of the sexually dimorphic expression of genes has reported that the molecular-level effects of gender influence treatment response and prognosis in various cancers. Hence, there is a need to develop “gender-specific” models that are prognostic of patient outcome to potentially assist in building comprehensive and patient-centric treatment plans. In the field of neuro-oncology, robust and consistent evidence shows that males are almost twice as likely to develop Glioblastoma (GBM), a rapidly fatal primary brain tumor, compared to females (1.5:1). Even though the visual appearance of different male and female tumor phenotypes on MRI look similar, there nonetheless might be subtle sub-visual cues reflective of the differences in the micro-architectural appearance, that are not visible to the naked human eye. Radiomics can provide a surrogate mechanism to non-invasively characterize these GBM tumor by capturing sub-visual cues of morphologic diversity (e.g. roughness, image homogeneity, regularity and edges) on routine MRI scans. It is also critical to identify the molecular associations of these radiomic features in gender controlled cohorts with underlying signalling pathways that drive different biological processes, via radiogenomic analysis. Such cross-scale associations using radiomics and radiogenomics in GBM could allow for designing gender-specific personalized treatments.

    Niha Beig is currently a PhD researcher in Brain Image Computing Laboratory at Case Western Reserve University, Cleveland Ohio, USA. Her research focus is on developing assistive diagnostic tools in the field of neuro-oncology using data science and machine learning techniques. She has been working on drawing insights from quantitative medical images and genomic sequences to help distinguish various subtypes of cancers in adult and pediatric gliomas. She has over 30 peer-reviewed abstracts and publications in the field of radiomics and radiogenomics.

    Niha is currently an elected member of the Sigma Xi: Scientific Research Honor Society and has also served as an area chair for Women in Machine Learning Workshop in 2018 and 2019.

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  • 14:20
    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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  • 14:40

    COFFEE BREAK

  • 15:00

    How AI Discovered a Drug Candidate at Deep Genomics

  • COMPLIANCE & ETHICS

  • 15:40

    Scalable and Accurate Machine Learning with Electronic Health Records

  • 16:20

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

  • 17:00

    NETWORKING SESSION

  • 18:00

    END OF DAY 1

  • 08:30

    DOORS OPEN

  • 09:00

    Welcome Note

  • START UP SESSION

  • 09:15
    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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  • 09:35

    AI Powered Chatbots to Combat Substance Abuse

  • 09:55

    Programming Living Cells for Next Generation Therapeutics

  • 10:15

    Data Mining Medical Records with Machine Learning

  • 10:35

    COFFEE BREAK

  • MEDICAL IMAGING

  • 11:05
    Kyung Hyun Sung

    Applications of MRI-based Deep Learning Models to Prostate Cancer Care

    Kyung Hyun Sung - Assistant Professor - UCLA

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    Quantitative MRI-Driven Deep Learning

    Deep Learning (DL) has recently garnered great attention because of its superior performance in image recognition and classification. One of the main promises of DL is to replace handcrafted imaging features with efficient algorithms for hierarchical feature extraction. Many studies have shown DL is a powerful engine for producing “actionable results” in unstructured big data. We present deep learning methods to effectively distinguish between indolent and clinically significant prostatic carcinoma using multi-parametric MRI (mp-­MRI). The main contributions include i) constructing DL frameworks to avoid massive learning requirements through pre-trained convolutional neural network (CNN) models and ii) applying the proposed DL framework to the computerized analysis of prostate multi-parametric MRI from improved cancer classification.

    Dr. Sung received the M.S and Ph.D. degrees in Electrical Engineering from University of Southern California, Los Angeles, in 2005 and 2008, respectively. From 2008 to 2012, he finished his postdoctoral training at Stanford in the Departments of Radiology and joined the University of California, Los Angeles (UCLA) Department of Radiological Sciences in 2012 as an Assistant Professor. His research interest is to develop fast and reliable MRI methods that can provide improved diagnostic contrast and useful information. In particular, his group (http://mrrl.ucla.edu/meet-our-team/sung-lab/) is currently focused on developing advanced quantitative MRI techniques for early diagnosis, treatment guidance, and therapeutic response assessment for oncologic and cardiac applications

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  • 11:25

    Applying CNN's to Medical Imaging

  • HEALTH & WELLNESS

  • 11:45
    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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  • ELECTORNIC MEDICAL RECORDS

  • 12:05
    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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  • 12:25

    NETWORKING LUNCH

  • 13:20
    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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  • PUBLIC HEALTH

  • 13:40
    Margarita Sordo

    Evaluating the Impact of Determinants of Health

    Margarita Sordo - Senior Medical Informatician - Brigham and Women's Hospital, Massachusetts General Hospital

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    Evaluating the Impact of Determinants of Health

    There are known individual, social, economic, medical and environmental factors, known as determinants of health (DOH)1,2 that influence overall wellbeing, and physiological decline. Identification and evaluation of this impact on wellbeing may provide insights for early prevention and intervention to, not only as an adjuvant to health, but to prevent and reduce poor outcomes associated with physiological decline. We aim to identify and assess interrelated conditions and factors that influence health and wellbeing of population over time. Agent-Based Models (ABM)3, an Artificial Intelligence technique provides the mechanisms for dynamically modelling the impact of DOH on the general population to gather insights into the cumulative effects of DOH on the overall wellbeing over time.

    Dr. Sordo is a Senior Medical Informatician at the Brigham and Women’s Hospital and Massachusetts General Hospital, and an Instructor of General Internal Medicine and Primary Care at Harvard Medical School. Her research and scholarly writing include elicitation and representation of clinical knowledge, artificial intelligence and machine learning techniques in medicine and healthcare to further advance clinical decision support. Current work focuses on the application of complex adaptive systems to evaluate the impact of determinants of health and public health policies on individual health.

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  • 14:00
    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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  • 14:20

    PANEL: How can we Ensure a ‘Do No Harm’ policy When Applying AI to Healthcare?

  • 15:00

    END OF EVENT

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