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Unlabeled data contain both positive and negative examples. Studies show that positive examples of (secondary) diagnoses, and clinical conditions, such as sepsis, are present in unlabeled hospital administrative data, potentially distorting hospital reimbursement systems, and negatively affecting hospitals\u2019 revenue and profitability. We investigate whether PU learning is suitable for improving the quality of hospital administrative data. We train three models on 313,434 hospital cases using hospital cost features: two based on the two-step \u201cspy\u201d approach and one using a robust PU learning method. For model evaluation, we rely exclusively on positive examples due to the PU setting. To further assess model performance, we perform an external validity check: We relabel unlabeled sepsis cases, derive new sepsis rates, and compare them to those reported in medical record review studies. All models identify true positives well in unseen data. External validity checks show, however, that only the robust PU learner effectively discriminates between positives and negatives in the unlabeled data, yielding new sepsis rates within the range of sepsis rates reported in medical record review studies. PU learning can improve the quality of hospital administrative data, but its effectiveness depends strongly on the choice of learning approach and classifier. The output of a PU learner can potentially improve hospital reimbursement systems, hospital revenue and profitability management, and sensitivity analyses in healthcare management science, health economics, health services research, and disease surveillance.<\/jats:p>","DOI":"10.1007\/s10729-025-09733-7","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T11:32:22Z","timestamp":1761651142000},"page":"787-805","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Positive and unlabeled learning from hospital administrative data: a novel approach to identify sepsis cases"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2605-8994","authenticated-orcid":false,"given":"Justus","family":"Vogel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0083-5182","authenticated-orcid":false,"given":"Johannes","family":"Cordier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"9733_CR1","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1109\/TMM.2018.2871421","volume":"21","author":"J Zhang","year":"2019","unstructured":"Zhang J, Wang Z, Meng J et al (2019) Boosting positive and unlabeled learning for anomaly detection with multi-features. 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According to Swiss law, the study is therefore exempted from ethical approval and informed consent. Reference is made to the Human Research Act, Article 2, paragraph 2, letter c (https:\/\/fedlex.data.admin.ch\/eli\/cc\/2013\/617) and Human Research Ordinance, Article 25 (https:\/\/fedlex.data.admin.ch\/eli\/cc\/2013\/642).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"None.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}