{
  "abstract": "Background: Long-term care prediction models often rely on large multidomain assessments, which can limit interpretability and reuse across multiple service outcomes.\n\nObjective: To determine whether a common reduced predictor set could support prediction of 20 later recorded long-term care service-use outcomes while quantifying performance loss relative to the full set.\n\nMethods: This retrospective cohort study linked initial Care Management Evaluation Form assessments to administrative service records for 9,297 adults from southern Taiwan. The development cohort included 8,367 participants, and the fixed-holdout re-evaluation cohort included 930. We trained logistic regression, decision tree, random forest, extreme gradient boosting, and a shallow artificial neural network. Development-only recursive feature elimination ranked 378 processed predictors and produced nested sets of 31 and 16 predictors. Performance was assessed using the area under the receiver operating characteristic curve, area under the precision-recall curve, Brier score, and log loss.\n\nResults: With all 378 predictors, macro area under the receiver operating characteristic curve ranged from 0.711 to 0.772. The corresponding ranges were 0.706 to 0.745 with 31 predictors and 0.685 to 0.702 with 16 predictors. The 31-predictor set incurred smaller losses than the 16-predictor set. Performance varied by algorithm and service. The artificial neural network did not consistently outperform logistic regression, random forest, or extreme gradient boosting.\n\nConclusion: The shared 31-predictor set provided the strongest balance between parsimony and discrimination and emerged as the preferred reduced configuration for subsequent geographic, temporal, and prospective evaluation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung, Taiwan",
    "Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan",
    "National Center for Geriatrics and Welfare Research, National Health Research Institutes, Yunlin, Taiwan",
    "Graduate Institute of Biomedical Sciences, China Medical University, Taichung City, Taiwan",
    "Department of Risk Management and Insurance, Tamkang University, New Taipei City, Taiwan",
    "School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shuo-Chen Chien"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu-Hung Chang"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Chia-Ming Yen"
    },
    {
      "affs": [
        4
      ],
      "name": "Ying-Erh Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Chia-Chun Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu-Ping Hsiao"
    },
    {
      "affs": [
        1
      ],
      "name": "I-Chien Wu"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Chih-Cheng Hsu"
    },
    {
      "affs": [
        1,
        5
      ],
      "name": "Hung-Yi Chiou"
    },
    {
      "affs": [
        1
      ],
      "name": "Ren-Hua Chung",
      "orcid": "0000-0002-9835-6333"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/69cmna93j19x5.bib",
  "cited_by": 0,
  "doi": "10.1177/20552076261491128",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13724865/",
  "id": "69cmna93j19x5",
  "issns": [
    "2055-2076"
  ],
  "landing_url": "https://doi.org/10.1177/20552076261491128",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research Article"
    ],
    [
      "openalex",
      "3600|Geriatric Care and Nursing Homes"
    ]
  ],
  "openalex": "W7213416563",
  "pdf_url": null,
  "pmcid": "PMC13724865",
  "pmid": "42973740",
  "published": "2026-09-16",
  "publisher": "SAGE Publications",
  "ris": "https://research.draconitic.com/paper/69cmna93j19x5.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:23:22Z",
      "native_id": "PMC13724865",
      "source": "pmc",
      "url": "https://doi.org/10.1177/20552076261491128"
    }
  ],
  "subjects": [],
  "title": "Public-oriented personalized long-term care service recommendation using machine learning",
  "title_text": "Public-oriented personalized long-term care service recommendation using machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:21:15Z",
  "url": "https://research.draconitic.com/paper/69cmna93j19x5",
  "venue": "Digital Health",
  "versions": []
}