{
  "abstract": "Background: Early unplanned readmission to critical care units is associated with adverse outcomes and consumes scarce capacity twice, yet the decision to transfer a patient out of the unit is still made without objective criteria. Machine learning may support this decision, but metaheuristic hyperparameter search remains largely unexplored here. Methods: We analyzed 371 patients discharged from the critical care unit of a Chilean public hospital, of whom 41 were readmitted within seven days of transfer to a general ward. Five models (logistic regression, support vector classification, decision tree, random forest, and XGBoost) were trained on clinical and nursing variables available at transfer, using nested stratified five-fold cross-validation with preprocessing fitted within the training folds. Bayesian optimization and a genetic algorithm were compared under identical folds. Results: Under nested cross-validation, discrimination was moderate (AUC 53.3–75.2%), and no single model performed best across all metrics. Logistic regression achieved the highest AUC (75.2%) with the lowest variability across folds, followed by XGBoost (75.0%), whose results varied markedly across folds. The genetic algorithm yielded higher AUC than Bayesian optimization for most tree-based models. Days in the critical care unit, age, and unit of origin ranked highest in impurity-based importance. Conclusions: This proof-of-concept study suggests that routinely collected clinical and nursing information available at transfer may inform early readmission prediction. As the sample was case-enriched and derived from a single center, recalibration to the natural readmission prevalence and prospective external validation are required before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Institute for Health Care Research, Faculty of Nursing, Universidad Andrés Bello, Santiago 8370146, Chile",
    "Transportation and Logistics Center, Department of Engineering Sciences, Universidad Andrés Bello, Santiago 7500735, Chile",
    "Departamento de Ingeniería Industrial, Universidad del Bío-Bío, Concepción 4051381, Chile",
    "Instituto de Estadística, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340031, Chile"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Fredy Barriga-Gallegos",
      "orcid": "0009-0007-5346-3841"
    },
    {
      "affs": [
        2
      ],
      "name": "Gonzalo Ríos-Vásquez",
      "orcid": "0009-0004-7626-8334"
    },
    {
      "affs": [
        2
      ],
      "name": "Antonia Ilabaca",
      "orcid": "0009-0005-9337-9885"
    },
    {
      "affs": [
        3
      ],
      "name": "Hanns de la Fuente-Mella",
      "orcid": "0000-0003-2564-8770"
    },
    {
      "affs": [
        0
      ],
      "name": "Verónica Badilla-Morales"
    },
    {
      "affs": [
        0
      ],
      "name": "Naldy Febré Vergara",
      "orcid": "0000-0002-6885-5801"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7p32xq8cqvw34.bib",
  "cited_by": 0,
  "doi": "10.3390/jcm15218328",
  "fulltext_url": null,
  "id": "7p32xq8cqvw34",
  "issns": [
    "2077-0383"
  ],
  "landing_url": "https://www.mdpi.com/2077-0383/15/21/8328",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7224010542",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/7p32xq8cqvw34.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:01Z",
      "native_id": "10.3390/jcm15218328",
      "source": "crossref",
      "url": "https://www.mdpi.com/2077-0383/15/21/8328"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Predicting Early Unplanned Readmission to Critical Care Units Using Machine Learning and Metaheuristic Hyperparameter Optimization: Evidence from a Chilean Public Hospital",
  "title_text": "Predicting Early Unplanned Readmission to Critical Care Units Using Machine Learning and Metaheuristic Hyperparameter Optimization: Evidence from a Chilean Public Hospital",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:01Z",
  "url": "https://research.draconitic.com/paper/7p32xq8cqvw34",
  "venue": "Journal of Clinical Medicine",
  "versions": []
}