AI & Health

13:30-17:00, January 27 @ 3BC

Track / Overview

The global adoption of the Internet, mobile phones and related technologies has resulted in an unprecedented amount of digital data that have been used in studying health and disease. Specifically, digital data have been used to study how infectious diseases spread, sentiments towards public health interventions (such as, vaccines), risk factors for chronic conditions (e.g., smoking, alcohol abuse and unhealthy diets), and how public health information, and misinformation diffuses through social networks. Organizations such as the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) have used data from digital sources for the identification of disease outbreaks, and the 2005 International Health Regulations recognized event-based informal surveillance as an important source for epidemic intelligence.

Additionally, advances in machine learning and artificial intelligence (AI) are creating unique opportunities for developing new solutions and tools to improve health globally. Applications of machine learning and AI have enabled rapid processing of large amounts of data from digital and non-digital sources to identify previously undiscovered patterns of
health and disease, forecast disease trends and understand how inequalities in the built environment impacts health outcomes. Given the rapidly growing interest in applying machine learning and AI to public health, it is imperative for researchers and practitioners to gather and discuss advances, opportunities and challenges.

The AI & Health track aims to bring together public health practitioners and researchers working on data mining, crowdsourcing, social media, and AI applications for relevant discussions on the opportunities and ethics on the use of AI in public health. Contributed and invited talks will focus on novel applications of AI/machine learning to digital and non-digital data to improve health and control disease in communities. We will also aim to have representation of speakers from low, and middle income countries.

Track / Schedule

Developing Digital Measures from Person-Generated Health Data

With Luca Foschini

Faster reaction to epidemics: toward predicting outbreaks in Burkina Faso

With Maroussia Roelens

Predicting women’s preferences for caesarian delivery from online forum discussions

With Michele Tizzoni

Statistical learning on period app data to advance personalized health care for women

With Laura Symul

Reducing patient wait times - A critical lifesaver

With Harmen Boers

AI and ML toward scaling Twitter data analysis for digital epidemiology of birth defects

With Graciela Gonzalez-Hernandez

Break

Improving the quality of child consultations in Burkina Faso using ML: detecting and addressing Health Workers’ diagnostic mistakes

With Aziza Merzouki

Mapping medical vocabulary terms with word embeddings

With Pekka Tiikkainen

Tracking health trends using automated real-time social media text classification

With Martin Müller

Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms

With Daniela Paolotti

Neural Network Models for Neighborhood Effects Research

With Tolga Tasdizen

Beyond lesion count: machine learning based imaging markers in multiple sclerosis

With Jonas Richiardi

Track / Speakers

Sunil Mallya

Principal Deep Learning Scientist, AWS

Martin Müller

PhD Candidate, EPFL

Elaine Nsoesie

Assistant Professor, Boston University School of Public Health

Luca Foschini

Co-founder & Chief Data Scientist, Evidation Health

Michele Tizzoni

Research Leader, ISI Foundation

Tolga Tasdizen

Professor Electrical and Computer Engineering, University of Utah

Graciela Gonzalez-Hernandez

Associate Professor of Informatics, University of Pennsylvania

Daniela Paolotti

Research Leader, ISI Foundation

Asif Jan

Group Director, PHC Data Science, Roche

Pekka Tiikkainen

Senior Safety Data Analyst, Roche

Aziza Merzouki

Data Scientist, University of Geneva

Maroussia Roelens

Data Scientist, Institute of Global Health

Jonas Richiardi

Clinical Research Lead, Lausanne University Hospital

Harmen Boers

Team Lead AI & Machine Learning, Atos

Laura Symul

Postdoctoral Fellow, Stanford University

Gloria Macia

Data Scientist, F. Hoffmann-La Roche

Track / Co-organizers

Elaine Nsoesie

Assistant Professor, Boston University School of Public Health

AMLD EPFL 2020 / Tracks & talks

AI & Climate Change

Lynn Kaack, Nikola Milojevic-Dupont, Nicholas Jones, Felix Creutzig, Buffy Price, Slava Jankin, Olivier Corradi, Liam F. Beiser-McGrath, Marius Zumwald, Eniko Székely, Max Callaghan, Soon Hoe Lim, Mohamed Kafsi, Daniel de Barros Soares, Matthias Meyer, Chris Heinrich, Emmanouil Thrampoulidis, Marta Gonzalez, Kristina Orehounig, David Dao, Bibek Paudel

13:30-17:00 January 2709:00-12:30 January 285ABC

AI & Humanitarian Action

Neil Davison, Max Tegmark, Carmela Troncoso, Alessandro Mantelero, Michela D'Onofrio, Francois Fleuret, Amina Chebira, John C. Havens, Marc Brockschmidt, Helen Toner, Dustin Lewis, Subhashis Banerjee, Rebeca Moreno Jimenez, Netta Goussac, Volkan Cevher, Anika Schumann, Nadia Marsan, Massimo Marelli, Anja Kaspersen

09:00-17:00 January 283A

AI & Cities

Konstantin Klemmer, Shin Alexandre Koseki, Eun-Kyeong Kim, Nicholas Jones, Kamil Kaczmarek, Kiran Zahra, Roger Fischer, Doori Oh, Ran Goldblatt, Martí Bosch, Roman Prokofyev, Cristina Kadar, Dmitry Kudinov, Camille Lechot, Ellie Cosgrave, Javier Pérez Trufero, Layik Hama, Hoda Allahbakhshi, Marta Gonzalez, Valery Fischer, Emmanouil Tranos, Jens Kandt, Yussuf Said Yussuf, Nyalleng Moorosi, Nick Lucius

09:00-17:00 January 281BC

AMLD / Global partners