Dear Colleagues,
We are pleased to invite you to participate in the
AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications (https://spaca.weebly.com/),
which is scheduled to be held on March 22-24, 2021 at Stanford University in Palo Alto, California, USA.
We are also seeking related submissions in the form of extended abstracts (2-4 pages) for poster sessions or full papers (4-6 pages, excluding references) for position, review, and work-in-progress pieces. Papers with previously published results will be considered. Authors are advised to format the submissions according to the AAAI template (https://www.aaai.org/Publications/Templates/AuthorKit21.zip) and submit through the EasyChair site (https://easychair.org/conferences/?conf=sss21). The submission deadline is November 1. For details, please see the following Call for Participation and the attached flyer.
Call for Participation
Symposium URL: https://spaca.weebly.com/
Submission URL: https://easychair.org/conferences/?conf=sss21
Author Kit: https://www.aaai.org/Publications/Templates/AuthorKit21.zip
A survival analysis model estimates the time until a specified event will happen in the future (or related survival measure), for an individual. The event of interest could be the time to death or relapse of a patient, or time until an employee leaves a company or until the failure of a mechanical system. The key challenge in learning effective survival models is that this time-to-event is censored for some observations, which limits the direct use of standard regression techniques. This has led to a wide range of survival models, that each use the features of an instance (such as a patient), available at the start time, to produce some survival measure, which might be a risk score, the probability of survival to a specific future time (such as 1 year), or the survival probability over all future times.
This symposium focuses on approaches for learning models that estimate survival measures from survival datasets, which include censored instances. Its objective is to push the state-of-the-art in survival prediction algorithms and address fundamental issues that hinder their applicability for solving complex real-world problems. We anticipate this will foster interdisciplinary collaborations and create new research directions
Topics
We seek submissions that discuss the following topics.
Novel Algorithms - new static or dynamic machine-learning frameworks for survival prediction, algorithms to compute survival measures from multimodal and/or longitudinal datasets.
Evaluation Metrics - limitations of the data (for example, high censoring) and evaluation metrics (for example, c-index), provide new directions for comparing survival models, address model calibration and discrimination issues, and discuss model comparison strategies.
Foundational Issues - issues such as competing risks, causality, counterfactual reasoning, comorbidities, multimorbidities, and uncertainty quantification.
Applications - in medicine, healthcare, manufacturing, engineering, finance, economics, law enforcement.
Submission Instructions
Interested participants should submit either extended abstracts for the poster sessions (2-4 pages) or full papers (4-6 pages, excluding references) for position, review, and work-in-progress pieces. Note we will also consider papers that include results that have already been published (with appropriate acknowledgment).
The Program Committee will review all submissions and communicate the acceptance decisions to the authors via email. Submissions should be formatted according to the AAAI template and submitted through the AAAI Spring Symposium EasyChair site. Accepted and camera-ready papers will be published on the open-access proceedings site, CEUR-WS.
Organizing Committee:
Russ Greiner (Symposium Chair), University of Alberta (rgreiner@ualberta.ca)
Neeraj Kumar, University of Alberta (neeraj.kumar@ualberta.ca)
Thomas A. Gerds, Universtiy of Copenhagen (tag@biostat.ku.dk)
Mihaela van der Schaar, Turing Institute, Cambridge and UCLA (mv472@cam.ac.uk)
For questions please send an email to survivalprediction2021@gmail.com
------------------------------
Ilbin Lee
Assistant Professor
University of Alberta, School of Business
Edmonton AB
------------------------------