International Workshop
on AI in Health:
Transferring and Integrating Knowledge for Better Health


9:00 AM - 6:30 PM (Pacific Daylight Time). APRIL 19, 2021

Confirmed Speakers and Panelists

Jimeng Sun

University of Illinois Urbana-Champaign

Parminder Bhatia


Jure Leskovec

Stanford University

Brett Beaulieu-Jones

Harvard Medical School

Summers, Ronald

NIH Clinical Center

Mary Regina Boland

University of Pennsylvania

Jiajie Zhang

University of Texas Health Science Center

R. Nick Bryan

The University of Texas at Austin

Beau Norgeot

Anthem, Inc

Li Li


Marina Sirota

UC San Francisco

Keshav Pingali

Katana Graph

Karandeep Singh

University of Michigan

Kim Branson


Keynote 1 - Jure Leskovec

Mobility network models of COVID-19 explain inequities and inform reopening

ABSTRACT: The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread. In this talk we will introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of "superspreader" POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.

Jure Leskovec is an associate professor of Computer Science at Stanford University, the Chief Scientist at Pinterest, and an Investigator at the Chan Zuckerberg Biohub. He co-founded a machine learning startup Kosei, which was later acquired by Pinterest. Leskovec's research area is machine learning and data science for complex, richly-labeled relational structures, graphs, and networks for systems at all scales, from interactions of proteins in a cell to interactions between humans in a society. Applications include commonsense reasoning, recommender systems, social network analysis, computational social science, and computational biology with an emphasis on drug discovery. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter at @jure.


In April 2004, President Bush set a national goal that most Americans should have electronic health records (EHR) within a decade, with President Obama’s signing of the Health Information Technology for Economic and Clinical Health (HITECH) as part of the American Recovery and Reinvestment Act in February 2009, a whopping 96% of US hospitals have EHRs even back to 2016. Web technologies have played a critical role for this paradigm shift that EHRs can be transferred and communicated via the Web. The humongous healthcare data not only poses new challenges on data semantics to enable seamless communications among health professionals, but also creates huge potentials to allow Artificial Intelligence (AI) to empower doctors to provide better care. Data exchange within and across different healthcare organizations require explicit and shared semantics that biomedical ontologies and controlled vocabularies (e.g., SNOMED CT, ICD9, LOINC, RxNorm) have been widely implemented in the clinical decision-making systems. Aggregating heterogeneous healthcare data becomes doable which is essential for evidence-based care. Doctors can now make evidence-based care decision by not only having the current medical measures and the previous history of medical records of a patient, but also the EHR records of other similar patients from the current health organization and even other hospitals. Computational biomarkers can be identified by mining integrated EHR data using cutting edge machine learning and deep learning algorithms. With the benefits of formal data semantics and rich knowledge encoded in the biomedical ontologies, healthcare has entered a new era of personalized precision medicine.

The Web Conference gathers top notch experts in data management, data analytics, web technologies, semantic web, artificial intelligence, computer vision, and applied areas. This topic is important to the Web Conference as it is addressing a fundamental issue and an applied field related to intelligent data representation, mining, and application. Recent breakthroughs in artificial intelligence and machine learning have demonstrated the promising potential of machine intelligence, especially the combination of machine and human intelligence, which can lead to a paradigm shift in healthcare industry in the near future. This workshop aims to explore this timely topic with the audience from the Web Conference because the Web has become the essential infrastructure to acquire, disseminate, and create data, information, and knowledge. The Web Conference has a broad audience from both the technical and the social sides of science. This unique combination makes the Web conference an ideal forum for this workshop because healthcare is deeply rooted in science and also social science and humanity.

The rich medical concepts connected by semantic relationships integrate EHR data into knowledge graphs to enable knowledge-intensive discoveries. But it is still an open field with lots of challenges. For example, data cannot be easily shared across different hospital systems due to privacy, security, and policy issues. Especially, EHRs are embedded in different commercial vendor systems which makes the integration of EHRs extremely troublesome. But the recent development of FHIR and FAIR standards tackled this problem from a different angle that data can be communicated, integrated and analyzed simultaneously not only for physicians, but also available at the patient side. Biomedical ontologies and semantic web technologies can empower knowledge-driven discovery in healthcare to enable better cohort identification for clinical trials, risk prediction, precision diagnosis, and efficient clinical decision support workflows. Even though the dramatic increase of healthcare data offers unprecedented opportunities for evidence-based care, the interoperability of EHRs and mining the integrated EHRs are still open to innovative solutions. In this workshop, we will welcome researchers from various domains to discuss and share latest progresses related to knowledge representation, semantic annotation, semantic mining, automatic reasoning, and semantic data management to promote innovative semantic approaches to address pressing needs in healthcare.

Artificial Intelligence is revolutionizing every aspect of our lives. It also sneaks into the radiology reading rooms to build a new paradigm for precision diagnosis. Health innovations applying machine learning (ML) and deep learning (DL) in radiology account for more than half of the total innovations in health. The shortage of radiologists and burnout of physicians create the urgent demand for immediate solutions. A radiologist reads about 20,000 images a year, roughly 50-100 per day and the number is increasing. US each year produces 600 billion images and 31% of American radiologists have experienced at least one malpractice claim, often missed diagnoses. Building automatic or semi-automatic approaches on medical imaging diagnosis becomes the unavoidable next step. The combination of deep learning and prior knowledge of physicians organized as knowledge graphs can provide a powerful and yet unified framework for clinical decision support. It will open a new door to the potential of auto-annotating medical images by using AI and knowledge graph powered approaches. It can abruptly increase the annotated medical images at a much lower cost so that better CNN models can be trained, therefore better diagnosis models can be obtained. It can increase the interpretability of AI solutions by locating the abnormalities as the visual evidence in medical images which can build the trust between doctors and patients. In this workshop, we will welcome researches from various domains of computer vision, deep learning, knowledge graph, deep graph mining, and natural language processing to share latest developments of AI powered medical imaging diagnosis and move the research agenda to visual question answering to enable interpretability and precision in care.


(Pacific Daylight Time, April 19)

The workshop will be open for the whole conference. Each submitted paper will be evaluated by three reviewers from the aspects of novelty, significance, technique sound, experiments, and presentations. The reviewers will be program committee members or researchers recommended by the members.

All papers submitted should have a maximum length of 8 pages and demo papers should be no more than 4 pages. All must be prepared using the ACM camera-ready template. Authors are required to submit their papers electronically in PDF format.

Please submit your papers at

Topics (not limit to)

Knowledge representation and reasoning on healthcare data

Data integration, ontology and standards for healthcare data

Knowledge graph construction on healthcare data

Deep graph mining to address precision care

Biomedical ontology and Semantic Web technological applications in healthcare

Computer vision in medical imaging diagnosis

Auto-annotation of medical images

NLP for medical diagnosis notes

Multimodal deep learning models for advanced diagnosis

Interpretability of AI in health

Fairness of AI in health

AI applications in healthcare

Important Dates

(all deadlines are midnight Ljubljana Time)

Feb 15, 2021

Submissions due

Feb 22, 2021

Acceptance notifications

March 1, 2021

Camera-ready submission

April 19, 2021

Workshop date


Ying Ding

University of Texas at Austin

James Hendler


Benjamin Glicksberg

Icahn School of Medicine at Mount Sinai

Guoqiang Zhang

University of Texas Health Science Center

Yifan Peng

Cornell University

Mark Musen

Stanford University

Fei Wang

Cornell University

Marinka Zitnik

Harvard University