Submission Guidelines

Papers should present original work that has not been published or accepted for publication in any peer-reviewed journal or conference.  Acceptance of a paper requires at least one of the co-authors to register and present the study at the PRHA 2023.

• Paper Formatting Instructions

  Full papers: Please follow the ACML conference track submission guidelines for templates. Papers should not exceed 6 pages, including references. 

  Posters: Undergraduate and graduate students are welcome to submit a single-page pdf in poster format. Posters should not be text-heavy and contain the following details:

• Title, author names and affiliations

•  Motivation, method, results, and conclusion sections

• Email of the corresponding author

Full papers will be submitted through Easy Chair on or before the submission deadline. 

Posters will be emailed to prha.submission@gmail.com.

The submission deadline is at 23:59 GMT+3.

Aim and scope

A vast amount of digital health data has become accessible to clinical and machine learning researchers. Thus, healthcare has become a prominent field that benefits from data-driven techniques. In parallel, machine learning for healthcare has emerged to develop models to assist physicians and clinical researchers in solving complex healthcare tasks and clinical decision-making. Machine learning and deep learning-based approaches have been successfully applied to various healthcare tasks, such as risk prediction and diagnosis prediction. However, digital patient data retain multiple challenges. For instance, high-dimensional, non-linear, temporal, distributed, and sensitive patient data pose additional requirements while designing machine learning models. The 2nd PRHA workshop aims to showcase the emerging challenges in bioinformatics and digital health with their latest solutions in machine learning and provide an outlet for interdisciplinary collaborations. The workshop's scope entails but is not limited to

●      bioinformatics,

●      patient phenotyping and subtyping,

●      patient monitoring, machine learning in pervasive healthcare,

●      multi-modal learning for disease prediction and treatment effects,

●      temporal modeling for disease progression,

●      interpretable models for clinical decision support, and

●      privacy-preserving techniques for distributed and sensitive patient data.

Committees

Organizing committee

• Bert Arnrich, Connected Healthcare at the joint Digital-Engineering Faculty of Hasso Plattner Institute (HPI) and the University of Potsdam, Potsdam, Germany

• Arzucan Özgür, Bogazici University, Istanbul Turkey

• İnci M. Baytaş, Bogazici University, Istanbul Turkey

Contact

All questions about submissions should be emailed to inci.baytas@boun.edu.tr