{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:53:10Z","timestamp":1791265990496,"version":"4.1.0"},"reference-count":36,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52178080"],"award-info":[{"award-number":["52178080"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Care Science"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Fungal infections (FIs) are common and severe complications in hospitalized patients, with early identification of high\u2010risk patients being critical for improving clinical outcomes. Although previous studies have attempted to predict FI using traditional methods, the accuracy and clinical applicability of existing models still need improvement. The aim of this study is to develop and validate an interpretable machine learning (ML) model for predicting FI risk in hospitalized patients, enhancing the precision of clinical decision\u2010making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We utilized real\u2010world inpatient data from Zhongnan Hospital of Wuhan University. Feature selection was performed using least absolute shrinkage and selection operator, random forest, and support vector machine recursive feature elimination methods. We then developed ML models, including logistic regression, random forest, decision tree, extreme gradient boosting, and light gradient boosting machine, K\u2010nearest neighbors and histogram gradient boosting trees, which were validated on both the training set and an internal testing set. During the model development process, the Shapley additive explanations (SHAPs) method was employed to enhance the clinical interpretability of the models. Additionally, an online prediction tool based on Streamlit was developed, integrating the model into the clinical workflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The study cohort comprised 806,761 hospitalized patients after data cleaning. Baseline characteristics were well balanced between the two cohorts (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&gt;\u20090.05), including demographics, disease types, and key clinical variables. The extreme gradient boosting model demonstrated excellent predictive performance, with an area under the curve of 0.952 (0.951\u20130.953) on the training cohort, and 0.952 (0.951\u20130.952) on the testing cohort, showing good internal validity. The model also exhibited favorable sensitivity, specificity, and accuracy (\n                      <jats:italic>F<\/jats:italic>\n                      <jats:sub>1<\/jats:sub>\n                      score: 0.605). SHAP analysis revealed key predictive features such as antimicrobial use, mechanical ventilation, and bacterial infections, which contributed significantly to the model's predictions. An interactive Streamlit\u2010based web tool was developed, providing real\u2010time risk assessment, SHAP\u2010based interpretability, and risk\u2010stratified clinical recommendations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study successfully developed and validated a ML\u2010based model for FI prediction, demonstrating good predictive accuracy and strong clinical interpretability. Given its performance characteristics, the model is well\u2010suited for targeted clinical scenarios where confirming high\u2010risk patients is the priority, rather than for broad universal screening. Within this appropriate context, the model can assist clinicians in early identification of high\u2010risk patients and in making personalized intervention decisions, holding significant clinical value.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hcs2.70099","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T06:27:40Z","timestamp":1789021660000},"page":"473-486","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Machine Learning Model for Fungal Infection Prediction: A Real\u2010World Study"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8838-5587","authenticated-orcid":false,"given":"Jinru","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Radiation and Medical Oncology Zhongnan Hospital of Wuhan University Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqi","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Public Health Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Orthopedics Zhongnan Hospital of Wuhan University Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5824-2614","authenticated-orcid":false,"given":"Bingzheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Medical Administration Zhongnan Hospital of Wuhan University Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8230-526X","authenticated-orcid":false,"given":"Bilong","family":"Feng","sequence":"additional","affiliation":[{"name":"Hubei Engineering Center for Infectious Disease Prevention, Control and Treatment Wuhan China"},{"name":"Nursing Department Zhongnan Hospital of Wuhan University Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2094-9347","authenticated-orcid":false,"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Engineering Center for Infectious Disease Prevention, Control and Treatment Wuhan China"},{"name":"Department of Infection Management Zhongnan Hospital of Wuhan University Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(23)00692-8"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.3004404"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addr.2023.114775"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1183\/16000617.0153-2024"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.idc.2016.07.008"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2024.08.046"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1128\/CMR.00053-10"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2024.1256158"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-024-07341-7"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciaa1751"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-022-03915-3"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-01284-x"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2025.1575502"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2024.103048"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-024-05138-0"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-021-03724-0"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1177\/20552076251326226"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-021-00946-z"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(23)00177-2"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.905601"},{"key":"e_1_2_13_22_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2025-102448"},{"key":"e_1_2_13_23_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-022-07125-8"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.2147\/IDR.S544469"},{"key":"e_1_2_13_25_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378"},{"key":"e_1_2_13_26_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505"},{"key":"e_1_2_13_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-024-09109-2"},{"key":"e_1_2_13_28_1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.55386"},{"key":"e_1_2_13_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.intimp.2020.106649"},{"key":"e_1_2_13_30_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12941-022-00539-x"},{"key":"e_1_2_13_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00520-013-1844-1"},{"key":"e_1_2_13_32_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13123557"},{"key":"e_1_2_13_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3049630"},{"key":"e_1_2_13_34_1","unstructured":"\u201cFungal Infection Prediction System \u201d Streamlit published April 18 2026 https:\/\/fiprediction.streamlit.app\/."},{"key":"e_1_2_13_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2025.3556464"},{"key":"e_1_2_13_36_1","doi-asserted-by":"publisher","DOI":"10.2147\/idr.s488933"},{"key":"e_1_2_13_37_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.11710"}],"container-title":["Health Care Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hcs2.70099","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/hcs2.70099","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hcs2.70099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:17:29Z","timestamp":1791263849000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/hcs2.70099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["10.1002\/hcs2.70099"],"URL":"https:\/\/doi.org\/10.1002\/hcs2.70099","archive":["Portico"],"relation":{},"ISSN":["2771-1749","2771-1757"],"issn-type":[{"value":"2771-1749","type":"print"},{"value":"2771-1757","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]},"assertion":[{"value":"2026-03-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}