We are reaching out to share two quick updates wrt the upcoming
*AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges,
and Applications*
*1. It will be completely "virtual" -- ie, on-line.*
*2. This allows us to give a one-month extension: We will accept
submissions until *
* Tues 1/Dec.*
Please let us know if you have any questions.
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Call for Participation
Symposium URL: *https://spaca.weebly.com/* <https://spaca.weebly.com/>
Submission URL: https://easychair.org/conferences/?conf=sss21
<https://sites.google.com/utexas.edu/ml4nav/>
*Author Kit: https://www.aaai.org/Publications/Templates/AuthorKit21.zip
<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
<https://www.aaai.org/Publications/Templates/AuthorKit21.zip> and submitted
through the AAAI Spring Symposium EasyChair site
<https://easychair.org/conferences/?conf=sss21>. Accepted and camera-ready
papers will be published on the open-access proceedings site, CEUR-WS
<http://ceur-ws.org/>.
Organizing Committee:
Russ Greiner
<https://sites.google.com/view/drrussellgreiner/home?authuser=0> (Symposium
Chair), University of Alberta (rgreiner@ualberta.ca) Neeraj Kumar
<https://neerajkumarvaid.weebly.com/>, University of Alberta (
neeraj.kumar@ualberta.ca)
Thomas A. Gerds <https://biostat.ku.dk/staff_/?pure=en/persons/323237>,
University of Copenhagen (tag@biostat.ku.dk) Mihaela van der Schaar
<https://www.vanderschaar-lab.com/>, Turing Institute, Cambridge and UCLA (
mv472@cam.ac.uk)
For questions please send an email to survivalprediction2021@gmail.com .
Best,
Neeraj, Russ, Thomas, Mihaela
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