{
  "abstract": "Objective:. To develop and validate a machine learning–based model for early prediction of in-hospital mortality among preterm neonates using routinely available perinatal and neonatal clinical variables. Methods:. This retrospective single-center cohort study included 1612 live-born preterm neonates delivered between 26 and 37 weeks of gestation. Sixteen clinical maternal, perinatal, and neonatal variables were collected, including APGAR scores (appearance, pulse, grimace, activity, respiration) , need for cardiopulmonary resuscitation (CPR), gestational age, birth weight, fetal presentation, in vitro fertilization, and obstetric history. Data preprocessing included anomaly handling, multivariate imputation by chained equations (MICE), one-hot encoding/standardization, and class imbalance management using adaptive synthetic sampling and cost-sensitive learning applied to the training dataset. Model selection compared multiple classifiers, with the final model being a Random Forest identified through the Tree-Based Pipeline Optimization Tool (TPOT) automated machine learning (AutoML) pipeline. Model performance on a held-out test set was assessed using area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, likelihood ratios, and confusion matrix analysis. Feature importance was assessed using Chi-square/Cramér’s V, logistic regression coefficients, and model-based importance from Random Forest, gradient-boosted trees, and XGBoost. SHapley Additive exPlanations were used to determine the direction and magnitude of feature effects. Overfitting was mitigated through cost-sensitive learning, and consistency between training and test performance indicated minimal overfitting. Results:. The model achieved an AUC-ROC of 0.99, accuracy of 0.98, sensitivity of 0.98, specificity of 0.99, PPV of 0.99, NPV of 0.96, and F1-score of 0.988. The most influential predictors of in-hospital mortality were need for CPR, 1-minute APGAR score, gestational age < 28 weeks, 5-minute APGAR score, birth weight < 1 kg, and fetal anomalies. Conclusion:. A Random Forest–based AutoML model can accurately predict in-hospital mortality among preterm neonates using routinely available clinical variables. This data-driven tool may support earlier risk stratification, informed clinical decision-making, and improved allocation of neonatal intensive care resources in maternal-fetal and perinatal care settings.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Metropolitan College, Boston University, Commonwealth Ave, Boston MA 02215, USA",
    "Department of Obstetrics and Gynecology, Iran University of Medical Sciences, Tehran 14496-14535, Iran",
    "Department of Electrical and Computer Engineering, Semnan University, Semnan 35131-19111, Iran",
    "Department of Nursing and Midwifery, Ardabil University of Medical Sciences, Ardabil 5618985991, Iran",
    "Department of Clinical Translational Science, Tufts University, Boston MA 02111, USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Vahid Monfared"
    },
    {
      "affs": [
        1
      ],
      "name": "Ladan Haghighi"
    },
    {
      "affs": [
        2
      ],
      "name": "Afkham Hashemi"
    },
    {
      "affs": [
        3
      ],
      "name": "Neda Farzizadeh"
    },
    {
      "affs": [
        4
      ],
      "name": "Zahra Najmi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6cttm1sbnvrhq.bib",
  "cited_by": 0,
  "doi": "10.1097/fm9.0000000000000366",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13706451/?tool=EBI",
  "id": "6cttm1sbnvrhq",
  "issns": [
    "2096-6954",
    "2641-5895"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13706451",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RG1-991"
    ],
    [
      "openalex",
      "3605|Artificial Intelligence in Healthcare"
    ]
  ],
  "openalex": "W7165110913",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13706451/pdf/?tool=EBI",
  "pmcid": "PMC13706451",
  "pmid": null,
  "published": "2026-06-18",
  "publisher": "Wolters Kluwer Health",
  "ris": "https://research.draconitic.com/paper/6cttm1sbnvrhq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:51Z",
      "native_id": "PMC:PMC13706451",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13706451"
    },
    {
      "first_seen": "2026-10-30T13:43:58Z",
      "native_id": "b27236533836496d973187822fac53e2",
      "source": "doaj",
      "url": "https://doi.org/10.1097/fm9.0000000000000366"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RG1-991",
      "subfield": "reproductive-child"
    }
  ],
  "title": "Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning",
  "title_text": "Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:58Z",
  "url": "https://research.draconitic.com/paper/6cttm1sbnvrhq",
  "venue": "Maternal-fetal medicine (Wolters Kluwer Health, Inc.)",
  "versions": []
}