{
  "abstract": "Background To develop and validate machine learning (ML)-based models to predict serious bacterial infections (SBIs) in febrile infants aged ≤90 days.Methods This retrospective study analysed data from febrile infants (≥38.0℃) aged ≤90 days. The development dataset comprised data from patients who visited the Seoul Asan Medical Center between 2015 and 2021, whereas the validation dataset included data from those who visited the centre from January 2022 to August 2023. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) were used to develop the models for predicting SBIs, which were then compared with traditional rule-based models.Results The study included data from 2860 patients: 2288 (80%) in the development dataset and 572 (20%) in the validation dataset. SBIs were confirmed in 482 patients (21.0%) in the development dataset and 131 (22.9%) in the validation dataset. The XGB and LR models showed excellent performance with areas under the curve of 0.990 and 0.981 in development, and 0.989 and 0.985 in validation datasets. In validation, both models demonstrated superior specificity (82.3–87.0% vs 46.2–72.2%) and positive predictive value (61.5–68.5% vs 34.4–49.8%) compared with traditional rule-based models, while maintaining perfect sensitivity and negative predictive value (both 100% vs 81.7–100% and 92.0–100%, respectively) without any false negatives. Urinalysis, C-reactive protein and procalcitonin were identified as top-tier features in the XGB model.Conclusions The ML-based prediction model demonstrated robust performance, with superior specificity and perfect sensitivity, which may enhance the accuracy of SBI detection and reduce the costs associated with false positives.",
  "authors": [
    {
      "affiliations": [
        "Pediatric Emergency Department, Asan Medical Center Children’s Hospital, Songpa-gu, 서울특별시, Korea (the Republic of)"
      ],
      "name": "Jun Sung Park"
    },
    {
      "affiliations": [
        "Department of Convergence Medicine, Asan Medical Center, Seoul, Korea (the Republic of)"
      ],
      "name": "Reenar Yoo"
    },
    {
      "affiliations": [
        "Pediatric Emergency Department, Asan Medical Center Children’s Hospital, Songpa-gu, 서울특별시, Korea (the Republic of)"
      ],
      "name": "Soo-young Lim"
    },
    {
      "affiliations": [
        "Pediatric Emergency Department, Asan Medical Center Children’s Hospital, Songpa-gu, 서울특별시, Korea (the Republic of)"
      ],
      "name": "Dahyun Kim"
    },
    {
      "affiliations": [
        "Pediatric Emergency Department, Asan Medical Center Children’s Hospital, Songpa-gu, 서울특별시, Korea (the Republic of)"
      ],
      "name": "Min Kyo Chun"
    },
    {
      "affiliations": [
        "Pediatric Emergency Department, Asan Medical Center Children’s Hospital, Songpa-gu, 서울특별시, Korea (the Republic of)"
      ],
      "name": "Jeeho Han"
    },
    {
      "affiliations": [
        "Pediatric Emergency Department, Asan Medical Center Children’s Hospital, Songpa-gu, 서울특별시, Korea (the Republic of)"
      ],
      "name": "Jeong-Yong Lee"
    },
    {
      "affiliations": [
        "Pediatric Emergency Department, Asan Medical Center Children’s Hospital, Songpa-gu, 서울특별시, Korea (the Republic of)"
      ],
      "name": "Seung Jun Choi"
    },
    {
      "affiliations": [
        "Department of Pediatrics, Asan Medical Center Children’s Hospital, Seoul, Korea (the Republic of)",
        "Department of Pediatrics, University of Ulsan College of Medicine, Seou, Korea (the Republic of)"
      ],
      "name": "Seak Hee Oh"
    },
    {
      "affiliations": [
        "Department of Emergency Medicine, Asan Medical Center Children’s Hospital, Seoul, Korea (the Republic of)"
      ],
      "name": "Jong Seung Lee"
    },
    {
      "affiliations": [
        "Department of Pediatrics, Asan Medical Center Children’s Hospital, Seoul, Korea (the Republic of)"
      ],
      "name": "Jina Lee"
    }
  ],
  "title": "Development of a machine learning-based prediction model for serious bacterial infections in febrile young infants",
  "uid": "49e56bd7-6e47-56c8-a152-5ce8dd2ac115"
}