Machine Learning For Health

Machine Learning for Health (ML4H)

Conference positioning

Machine Learning for Health (ML4H) is a top computer-science conference venue for healthcare machine learning, clinical prediction, biomedical data, deployment, and responsible evaluation. It rewards a health-focused ML paper whose evidence respects medical data leakage, confounding, and deployment risks. 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 ML4H / Machine Learning for Health as the target venue.
• A manuscript in healthcare machine learning 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: healthcare machine learning, clinical prediction, biomedical data, deployment, and responsible evaluation.
• 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 ML4H'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 ML4H 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: healthcare machine learning, clinical prediction, biomedical data, deployment, and responsible evaluation.
• Distinctive fingerprint for reviewer calibration: healthcare, machine, learning, clinical, prediction, biomedical, data, deployment, responsible, evaluation, venue-specific, contribution, health, mlforhc.
• Official anchor domain: www.mlforhc.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 ML4H reviewers are the primary audience, not merely a convenient deadline.
• Closest roster neighbors to compare before final routing: international-conference-on- automated-machine-learning (AutoML Conference), conference-on-health-inference-and- learning (CHIL), acm-sigkdd-conference-on-knowledge-discovery-and-data-mining (KDD), ieee-international-conference-on-data-mining (ICDM). Break ties by contribution type, evidence shape, reviewer community, and the current official CFP from www.mlforhc.org.

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 ML4H, the evidence must support the venue-specific signature: a health-focused ML paper whose evidence respects medical data leakage, confounding, and deployment risks.
• 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.

Official-cycle checklist

• Open the live official venue page: https://www.mlforhc.org/
• 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 ML4H, 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 ML4H reviewers too little technical or empirical substance.

Re-routing decision

If the paper misses ML4H'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] Machine Learning for Health (ML4H)
[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>

Source: brycewang-stanford/Awesome-Journal-Skills → Computer-Science-Conference-Skills/skills/machine-learning-for-health/SKILL.md