Machine Learning Research Fellow - Maternal & Perinatal Health AI (CDC Dataset)

We're looking for a researcher to produce an original, TRIPOD+AI-compliant finding on antenatal prediction of preterm birth or NICU admission, using US national birth-certificate data, validated against a later, non-overlapping birth-year cohort. This is an invited submission to JPMedAI (Journal of Precision Medicine and Artificial Intelligence), a new, open-access, peer-reviewed journal currently assembling its inaugural issue. Article processing charges are waived during this launch phase, meaning there is no cost to you to publish. We're saying that plainly because we want the right fit: this suits researchers who value a genuine, citable publication and hands-on methodological support as much as (or more than) the cash fee, which reflects that context. THE DATASET Training dataset: CDC National Vital Statistics System Natality public-use files, fully open, no registration, individual-level birth-certificate microdata covering roughly 3.6–4 million US births per year, decades of history. External validation: temporal holdout using non-overlapping birth years, a genuinely independent national sample rather than a random split of the same year. The angle: something in identifying, using only information available earlier in pregnancy, which pregnancies are heading toward preterm birth or a NICU stay, explainably and with attention to whether the model works equally well across racial and socioeconomic groups. Most existing research using this dataset is descriptive (rates and trends over time) rather than predictive. We have a specific direction in mind, but haven't fixed it here. Propose your own take in your application. KEY CONSIDERATIONS Be disciplined about excluding delivery-time and gestational-age-derived variables from the feature set. Including them would make the endpoint trivially predictable and clinically useless, since the goal is a score usable earlier in pregnancy, not one that already knows how the pregnancy ended. Check exactly which maternal risk factors are available before and after the 2003 revision of the birth certificate before pooling years; field availability changed. This claim of relative novelty (predictive rather than descriptive) has not been independently stress-tested as rigorously as some other positions in this program. Give real weight to your own first-week literature search here rather than assuming the gap is as open as described. POST-SUBMISSION PEER-REVIEW REVISIONS Following submission, JPMedAI's peer review may request revisions before acceptance. Standard revisions (clarifications, additional citations, supplementary analyses within the existing study design) are expected as a normal part of authorship and are not separately compensated, consistent with standard academic publishing practice. If reviewers request substantially new work (e.g., a new validation dataset or a materially different analysis), a further milestone will be added at that time, sized to the actual scope requested. Response to reviewers should come from you as the named author; we'll support the process editorially throughout. GENERAL EXPECTATIONS FOR THE RESEARCH FELLOW Use a large, freely or quick-access public dataset for model development (training) and at least one genuinely independent dataset for external validation. Report according to TRIPOD+AI (2024), the AI/ML-specific extension of TRIPOD, citing the original TRIPOD statement as the foundational reference. SHAP or an equivalent feature-attribution method should be used for explainability. Report discrimination (AUC), calibration (plots, not just calibration slope/intercept in text), and decision-curve analysis against a clinically meaningful comparator, not against "no model" alone. Explicitly test and report subgroup performance by race/ethnicity and maternal age group rather than a single pooled metric. Before finalising the research question, complete a focused, dated literature search on the exact proposed endpoint/model combination and document the closest 2–3 prior papers and the specific point of difference. Write up the work as a scientific manuscript, 3,000–6,000 words, up to 8 figures, in this structure: Abstract (≤300 words, structured), Keywords (3–6), Introduction, Methods, Results, Discussion, Conclusion, References (Vancouver style). JPMedAI provides free academic writing support to any author who needs it, separate from the project-specific editorial guidance described below. WHAT YOU'LL GET An invited submission pathway to JPMedAI, a new, open-access, peer-reviewed journal building its inaugural issue. Early submissions get direct editorial engagement, not a slot in a large backlog. Hands-on editorial and methodological support, beyond the journal's own free academic-writing service noted above, we'll personally guide you on statistical reporting, clinical framing, and endpoint interpretation to bring your first draft to publishable, TRIPOD+AI-compliant standard. Named byline, full citation credit, ORCID-compatible publication metadata, and a PDF for your portfolio, CV, or dissertation appendix. A $100 honorarium on completion/acceptance. The primary value on offer here is the invited, mentored publication itself. First-mover advantage: be among the first researchers to publish on this specific dataset and question. WHAT THIS IS NOT This is not content writing. You are not ghost-writing. You are an independent researcher producing original work under your own name. The peer-review and editorial process exists to enforce scientific governance standards, not to change your conclusions or direct your inquiry. IDEAL CANDIDATE PROFILE We expect interest from two kinds of applicants, and both are genuinely welcome: Early-career researchers (PhD students, postdocs, recent graduates) who already have at least one preprint or publication and want a fast, well-supported second one. Strong ML / data science practitioners without a formal clinical or health-publication track record who want to break into applied health research. For this group, our editorial support specifically includes guidance on clinical framing, endpoint selection, and TRIPOD+AI reporting norms. Also useful (but not mandatory): background in obstetrics, epidemiology, or maternal-child health; experience with large administrative/vital-statistics datasets; familiarity with calibration plots, decision-curve analysis, or explainability methods. Sharing workload with one or two collaborators who will be coauthors is welcome. IF YOU ARE INTERESTED Please answer the questions below.