Call for Papers

TOPICS

• Data: data collection and algorithms designed for the challenges of real-world data that capture features shaping our health, collection and feature generation from internet/mobile, environmental or other outside-clinic datasets, privacy and security challenges related to public health data and tasks, ascertainment of data to measure and define factors related to health disparities

• Methods: Methods for combining non-clinical and clinical data for public and population health applications, algorithms for public health goals, modeling multi-sectoral data with respect to health outcomes, model transport across contexts and domains, algorithmic fairness and causal inference in public health settings

• Policy and Implementation: ML approaches for mitigating disparities, identifying methodological assumptions that fail in public health settings, human and ML interaction in the public health context

• Health Topics: ML integration in infectious disease models, improving non-communicable disease surveillance and prediction using ML

INSTRUCTIONS

Submission types

• Short papers (4 pages, unlimited appendices are allowed)

4-page submissions will be eligible for oral or poster presentation. We will not publish archival proceedings, however these should not have been previously published in any other archival venue.

• Problem statements and extended abstracts (1 page)

One page submissions will be presented as posters. Furthermore, extended abstract submissions that are under review or have been recently published in a conference or a journal are allowed for submission. Authors should clearly state any overlapping published or submitted work at the time of submission, and must ensure that they are not violating any other venue dual submission policies. Such submissions would typically be for laying out an important problem, a call to action or showcasing preliminary results -- and are good ways to generate conversation and get feedback.

Submission format

Contributions should be blinded and submitted using the NeurIPS template .

Submission link: https://cmt3.research.microsoft.com/MLPH2020

Contact: ml.pubhealth@gmail.com with questions.