CHIL 2024 : 5th AHLI Conference on Health, Inference, and Learning

The AHLI Conference on Health, Inference, and Learning (CHIL) solicits work across a variety of disciplines at the intersection of machine learning and healthcare. CHIL 2024 invites submissions focused on artificial intelligence and machine learning (AI/ML) techniques that address challenges in health, which we view broadly as including clinical healthcare, public health, population health, and beyond.

Specifically, authors are invited to submit 8-10 page papers (with unlimited pages for references) to one of 3 possible tracks:
1. Models and Methods;
2. Applications and Practice;
3. Impact and Society.


Track 1: Models and Methods - Algorithms, Inference, and Estimation

Advances in machine learning are critical for a better understanding of health. This track seeks technical contributions in modeling, inference, and estimation in health-focused or health-inspired settings. We welcome submissions that develop novel methods and algorithms, introduce relevant machine learning tasks, identify challenges with prevalent approaches, or learn from multiple sources of data (e.g. non-clinical and clinical data).

Our focus on health is broadly construed, including clinical healthcare, public health, and population health. While submissions should be primarily motivated by problems relevant to health, the contributions themselves are not required to be directly applied to real health data. For example, authors may use synthetic datasets to demonstrate properties of their proposed algorithms.

We welcome submissions from many perspectives, including but not limited to supervised learning, unsupervised learning, reinforcement learning, causal inference, representation learning, survival analysis, domain adaptation or generalization, interpretability, robustness, and algorithmic fairness. All kinds of health-relevant data types are in scope, including tabular health records, time series, text, images, videos, knowledge graphs, and more. We welcome all kinds of methodologies, from deep learning to probabilistic modeling to rigorous theory and beyond.


Track 2: Applications and Practice - Investigation, Evaluation, Interpretation, and Deployment

The goal of this track is to highlight works applying robust methods, models, or practices to identify, characterize, audit, evaluate, or benchmark ML approaches to healthcare problems. Additionally, we welcome unique deployments and datasets used to empirically evaluate these systems are necessary and important to advancing practice. Whereas the goal of Track 1 is to select papers that show significant algorithmic novelty, submit your work here if the contribution is describing an emerging or established innovative application of ML in healthcare. Areas of interest include but are not limited to:

Datasets and simulation frameworks for addressing gaps in ML healthcare applications
Tools and platforms that facilitate integration of AI algorithms and deployment for healthcare applications
Innovative ML-based approaches to solving a practical problems grounded in a healthcare application
Surveys, benchmarks, evaluations and best practices of using ML in healthcare
Emerging applications of AI in healthcare

Introducing a new method is not prohibited by any means for this track, but the focus should be on the extent of how the proposed ideas contribute to addressing a practical limitation (e.g., robustness, computational scalability, improved performance). We encourage submissions in both more traditional clinical areas (e.g., electronic health records (EHR), medical image analysis), as well as in emerging fields (e.g., remote and telehealth medicine, integration of omics).


Track 3: Impact and Society - Policy, Public Health, and Social Outcomes

Algorithms do not exist in a vacuum: instead, they often explicitly aim for important social outcomes. This track considers issues at the intersection of algorithms and the societies they seek to impact, specifically for health. Submissions could include methodological contributions such as algorithmic development and performance evaluation for policy and public health applications, large-scale or challenging data collection, combining clinical and non-clinical data, as well as detecting and measuring bias. Submissions could also include impact-oriented research such as determining how algorithmic systems for health may introduce, exacerbate, or reduce inequities and inequalities, discrimination, and unjust outcomes, as well as evaluating the economic implications of these systems. We invite submissions tackling the responsible design of AI applications for healthcare and public health. System design for the implementation of such applications at scale is also welcome, which often requires balancing various tradeoffs in decision-making. Submissions related to understanding barriers to the deployment and adoption of algorithmic systems for societal-level health applications are also of interest. In addressing these problems, insights from social sciences, law, clinical medicine, and the humanities can be crucial.