Call for Papers

The main focus area of our workshop will be on applications of machine learning in pharmaceutical and healthcare settings. It will be a platform for researchers working in industry and academia on various applications of machine learning in these fields.

This is the 4th. consecutive iteration of PharML. You can visit our previous meetings (2020, 2021, 2022) to learn more about the themes and topics we address in the workshop. 

Topics of Interest

• Causal inference and learning

  • Predicting the impact of interventions and discovering causal relationships from observational data.

• Domain adaptation

  • Techniques for adapting models to new domains, to learn relationships between different domains, and to improve model generalization.

• Learning on Graphs

  • Node and link prediction on molecular graphs

  • Modelling dynamic graphs

  • Biological pathway mining for cancer biomarker discovery

• Machine learning for survival analysis

  • Methods for learning from time-to-event data to build prognostic or treatment-predictive models.

• Multimodal and data fusion

  • Leveraging multiple modalities including images, molecular data, text, and sensor data

• Applied machine-learning

  • Drug Design and Discovery

  • Translational Medicine

  • Earlier Diagnosis and Differential Diagnosis

  • Personalized Healthcare and Precision Medicine

  • Patient Safety and Monitoring, Pharmacovigilance, and Toxicity Prediction

  • Medical Devices and Digital Endpoints

  • Clinical Decision Support

Important dates

• Submission Deadline: June 26, 2023 (AoE) (deadline extended) June 19, 2023

• Notification:  July 21, 2023 July 19, 2023

• Workshop date: Friday, September 22, 2023  (in-person)

Paper submission guidelines

We invite authors to submit their work in either an abstract or extended format. 

• 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• Long papers: present mature work, published or unpublished.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.

Authors of accepted papers will be notified whether their paper is selected for oral presentation or poster session.

Additional guidelines

• All manuscripts will be peer-reviewed and the review process is strictly double-blind. 

• All submissions must be anonymous. Make sure the document(s) you upload does not contain names, affiliations, acknowledgements or any information that can be used to directly identify you. Replace the author information with "Anonymous".  Avoid including URLs of GitHub repositories linked to you. 

• Manuscripts 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 Conference Management Toolkit (CMT) page (link). Follow these steps to submit your manuscript:

  1. In the “Submissions” tab, make sure the selected conference is “ECMLPKDDworkshop2023”.

  2. When you click the button “+ Create new submission”, select “PharML, Machine Learning for Pharma and Healthcare Applications” as the track.

Paper publication

• The PharML workshop will partner again this year with Springer to publish the workshop proceedings (See link for 2022 proceedings)

• Only unpublished work or extended versions of the abstracts are eligible for publication in the workshop proceedings.

• Important note from the Conference Organizers: accepted manuscripts at the workshop without a full registration of one of the authors or in-presence presentation will not be included in the workshop proceedings.