PharML 2021: Machine Learning for Pharma and Healthcare Applications
ECML-PKDD 2021
September 13-17, 2021
We invite contributions from both industry and academia to share their research and experience in using artificial intelligence and machine learning methods in pharmaceutical and healthcare research and development. PharML will be held at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) on 13th - 17th of September 2021 (virtually).
Submission GuidelinesWe invite authors to submit their work in either one of the following two formats:
Research abstracts: describe preliminary results and are meant to foster discussion of emerging topics. Maximum of 6 pages.
Long papers: present mature work, published or unpublished. Maximum of 16 pages.
Additional guidelines:
All manuscripts will be peer-reviewed, double-blinded.
Authors should commit to present their work at the workshop in case it is accepted for an oral presentation.
Abstracts and papers should be in PDF format, following the Springer Lecture Notes in Computer Science style (see link for LaTeX template)
Submissions should be done via the workshop EasyChair page (follow link).
Paper publication:
Only unpublished work or extended versions of the abstracts are eligible for publication in the workshop proceedings.
The proceedings of the workshop will be published either by Springer as a Lecture Notes volume or by CEUR in their workshop proceedings series.
Survival Machine Learning (thematic session 1)
Deep Learning and Survival Modelling
Random Forest-based Survival Modelling
Regularized Regression for Survival
Longitudinal Survival Modelling
Causal Inference (thematic session 2)
Subgroup discovery and targeted learning.
Estimating treatment effect in randomized and/or observational studies.
Causal structure learning from real world data.
Domain Adaptation and Domain Generalization (thematic session 3)
Unsupervised Domain Adaptation
Supervised/Semi-supervised Domain Adaptation
Domain Generalization
Generalizability between models for Real World Data and Randomized Control Trials
Generalizability across diseases and demographic groups
Other applications for better generalizability
Federated Learning (thematic session 4)
Centralized Federated Learning
De-centralized Federated Learning
Differential privacy and encryption
Other topics
Multimodal Machine Learning (e.g. combining genomics, pathology reports, clinical data)
Medical Imaging
Natural Language Processing for health records.
Medical decision support
Digital biomarker development
Machine Learning for Personalized Healthcare
Generative Chemistry and Machine Learning for Drug Discovery
Learning on Graphs: Generative models, modelling dynamic graphs, transfer learning in graphs, limitations of traditional node/edge/graph embeddings.
The workshop will feature:
Invited keynote speakers
From academia: Lee Cooper (Northwestern University) and Vince Calhoun (Georgia Tech)
From industry: David Ohlssen (Novartis) and Ryan Copping (Roche).
Presentations of long papers and short abstracts.
The full programme will be posted on the workshop's website in due course.
Important datesSubmission Deadline: July 2nd, 2021
Notification: August 2nd, 2021
Workshop date: Monday 13th of September or Friday 17th of September (tbd)
Workshop format: Virtual (online) event
Lee Cooper (Northwestern University, USA)
Naghmeh Ghazaleh (Roche, Switzerland)
Jonas Richiardi (Lausanne University Hospital and University of Lausanne, Switzerland)
Damian Roqueiro (ETH Zurich, Switzerland)
Diego Saldana (Roche, Switzerland)
Konstantinos Sechidis (Novartis, Switzerland)
Michael Adamer (ETH Zurich, Switzerland)
Laura Azzimonti (IDSIA, Switzerland)
Mark Baillie (Novartis, Switzerland)
Christian Bock (ETH Zurich, Switzerland)
Sarah Brüningk (ETH Zurich, Switzerland)
Matias Callara (Roche, Switzerland)
Thibaud Coroller (Novartis, US)
Giovanni d'Ario (Roche, Switzerland)
Christoph Freyre (Novartis, Switzerland)
Geoffrey Fucile (University of Basel, Switzerland)
Marius Garmhausen (Roche, Switzerland)
Lasse Hansen (Aarhus University, Denmark)
Max Horn (ETH Zurich, Switzerland)
Juliane Klatt (ETH Zurich, Switzerland)
Valeria De Luca (Novartis, Switzerland)
Matteo Manica (IBM, Switzerland)
Joseph Mellor (University of Edinburgh, UK)
Michael Mitchley (Roche, Switzerland)
Pooya Mobadersany (Janssen, Johnson and Johnson, US)
Georgiana Neculae (BenevolentAI, UK)
Nikolaos Nikolaou (AstraZeneca, UK)
Marilena Oita, (Simply Vision, Switzerland)
Jon Parkinson (University of Manchester, UK)
Konstantinos Pliakos (KU Leuven, Belgium)
Maria Giulia Preti (EPFL, Switzerland)
Jonathan Rafael-Patino (CHUV, Switzerland)
Bastian Rieck (ETH Zurich, Switzerland)
Elizaveta Semenova (AstraZeneca, UK)
Cameron Shad (UCL, UK)
Mohamed Amgad Tageldin (Northwestern University, US)
Grigorios Tsoumakas (Aristotle University of Thessaloniki, Greece)
Lukas A. Widmer (Novartis, Switzerland)
Visit the official website of the workshop for additional details.
If you have questions, please send us an e-mail to: pharml2021 [at] easychair [dot] org