Conference on Health, Inference, and Learning (CHIL)
Conference positioning
Conference on Health, Inference, and Learning (CHIL) is a top computer-science conference venue for machine learning for health, clinical inference, health systems, fairness, robustness, and deployment constraints. It rewards a health ML paper with clinical grounding, validation discipline, and clear claims about use context. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
When to trigger
The author names CHIL / Conference on Health, Inference, and Learning as the target venue.
A manuscript in machine learning for health needs a conference-fit read before being formatted or submitted.
The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.
Scope & topic fit
Core fit: machine learning for health, clinical inference, health systems, fairness, robustness, and deployment constraints.
Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
The paper should explain why the result matters to CHIL's reviewers, not just why it is interesting to the authors' lab or product context.
Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.
Venue-specific calibration
Reviewer lens: Treat CHIL as a AI for health venue whose reviewers expect the scope and evidence to match its own community. Do not submit a generic CS paper until the introduction names the exact subcommunity, contribution type, and proof or empirical standard.
Contribution hook to foreground: the venue-specific contribution bar.
Scope vocabulary to use naturally in the abstract and introduction: machine learning for health, clinical inference, health systems, fairness, robustness, and deployment constraints.
Official anchor domain: www.chilconference.org. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
Use this profile only when the manuscript's central contribution is genuinely in AI for health
and the author can say why CHIL reviewers are the primary audience, not merely a convenient
deadline.
Closest roster neighbors to compare before final routing: conference-on-lifelong-learning- agents (CoLLAs), international-conference-on-automated-machine-learning (AutoML
Conference), machine-learning-for-health (ML4H), acm-sigkdd-conference-on-knowledge- discovery-and-data-mining (KDD). Break ties by contribution type, evidence shape, reviewer
community, and the current official CFP from www.chilconference.org.
CHIL-specific routing detail
Prefer CHIL when the novelty depends on health context: clinical prediction, care delivery, health-system deployment, fairness across patient populations, robustness under clinical shift, or validated inference from health data.
Use KDD/ICML/NeurIPS only when the general machine-learning or data-mining contribution is strong without the clinical setting; CHIL reviewers should see why the health setting changes the method, validation, or failure analysis.
Do not route optimization, scheduling, or operations-research papers here just because they use medical examples. If the central contribution is solver design, resource allocation, or constraint modeling, compare with CPAIOR, ICAPS, INFORMS-facing venues, or health-operations outlets first.
Method & evidence bar
Compare against current strong baselines and explain exactly what changes in the algorithm, objective, data, or inference procedure.
Report ablations that isolate the claimed mechanism; do not rely on aggregate benchmark wins alone.
Document data, compute, hyperparameters, model selection, and failure cases so the result can be reviewed as science rather than demo output.
For CHIL, the evidence must support the venue-specific signature: a health ML paper with clinical grounding, validation discipline, and clear claims about use context.
Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.
Structure & house style
Frame the contribution as a reusable idea: method, theory, benchmark, dataset, system, or socio-technical finding.
Separate main claims from exploratory results; reviewers at top AI venues punish overclaiming and hidden cherry-picking.
Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.
Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
If the live official instructions conflict with this skill, the official instructions win.
Pre-submission self-check
One sentence states why this manuscript belongs at CHIL, using the venue's scope rather than generic "top conference" language.
The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
Related work includes the nearest current-cycle AI for health papers and explains the technical delta.
The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.
Common desk-reject triggers
Leaderboard-only novelty with weak explanation of why the method works.
Unclear data contamination, missing baselines, or evaluation that cannot be reproduced.
Claims about safety, fairness, health, or society without matching evidence and limitations.
Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
A contribution framed for a neighboring field while giving CHIL reviewers too little technical or empirical substance.
Re-routing decision
If the paper misses CHIL's bar, compare against neural-information-processing-systems / international-conference-on-machine-learning / international-conference-on-learning-representations / aaai-conference-on-artificial-intelligence. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.
Output format
[Fit] High / Medium / Low (one-line reason)
[Target] Conference on Health, Inference, and Learning (CHIL)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
