{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T19:06:46Z","timestamp":1790363206115,"version":"4.1.0"},"reference-count":63,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001821","name":"Vienna Science and Technology Fund\/WWTF","doi-asserted-by":"publisher","award":["LS20-030"],"award-info":[{"award-number":["LS20-030"]}],"id":[{"id":"10.13039\/501100001821","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerful tool to identify biomarkers and predict patient-specific risks. However, challenges remain in integrating large-scale, multiomic datasets and translating these findings into actionable clinical tools. Identifying reliable, disease-specific biomarkers and developing explainable ML models that clinicians can trust and understand are key barriers to widespread clinical adoption. In this prospective longitudinal cohort study, we analyzed 1109 liquid biopsy samples from 99 preterm neonates with IVH, collected at up to six timepoints over 13 years. Various explainable ML techniques\u2014including statistical, regularization, deep learning, decision trees, and Bayesian methods\u2014were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. Targeted proteomic analyses were conducted using serum and urine samples through a proximity extension assay capable of detecting low-concentration proteins in complex biofluids. The study identified 41 significant independent protein markers in the 1600 calculated ML models that surpassed our rigorous threshold (AUC-ROC of \u22650.7, sensitivity \u2265 0.6, and selectivity \u2265 0.6), alongside gestational age at birth, as predictive of PHVD development and survival. Both known biomarkers, such as neurofilament light chain (NEFL), and novel biomarkers were revealed. These findings underscore the potential of targeted proteomics combined with ML to enhance clinical decision-making and parental counseling, though further validation is required before clinical implementation.<\/jats:p>","DOI":"10.3390\/ijms251910304","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T04:01:24Z","timestamp":1727236884000},"page":"10304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1369-5150","authenticated-orcid":false,"given":"Gabriel A.","family":"Vignolle","sequence":"first","affiliation":[{"name":"Center for Health & Bioresources, Competence Unit Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9850-7181","authenticated-orcid":false,"given":"Priska","family":"Bauerst\u00e4tter","sequence":"additional","affiliation":[{"name":"Center for Health & Bioresources, Competence Unit Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Sch\u00f6nthaler","sequence":"additional","affiliation":[{"name":"Center for Health & Bioresources, Competence Unit Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christa","family":"N\u00f6hammer","sequence":"additional","affiliation":[{"name":"Center for Health & Bioresources, Competence Unit Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monika","family":"Olischar","sequence":"additional","affiliation":[{"name":"Comprehensive Center for Pediatrics, Department of Pediatrics and Adolescent Medicine, Division of Neonatology, Intensive Care and Neuropediatrics, Medical University of Vienna, 1090 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8775-2405","authenticated-orcid":false,"given":"Angelika","family":"Berger","sequence":"additional","affiliation":[{"name":"Comprehensive Center for Pediatrics, Department of Pediatrics and Adolescent Medicine, Division of Neonatology, Intensive Care and Neuropediatrics, Medical University of Vienna, 1090 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregor","family":"Kasprian","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging and Image-Guided Therapy, Division of Neuro- and Musculosceletal Radiology, Medical University of Vienna, 1090 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5536-6873","authenticated-orcid":false,"given":"Georg","family":"Langs","sequence":"additional","affiliation":[{"name":"Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria"},{"name":"Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6038-5895","authenticated-orcid":false,"given":"Klemens","family":"Vierlinger","sequence":"additional","affiliation":[{"name":"Center for Health & Bioresources, Competence Unit Molecular Diagnostics, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5209-6169","authenticated-orcid":false,"given":"Katharina","family":"Goeral","sequence":"additional","affiliation":[{"name":"Comprehensive Center for Pediatrics, Department of Pediatrics and Adolescent Medicine, Division of Neonatology, Intensive Care and Neuropediatrics, Medical University of Vienna, 1090 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41390-020-0780-2","article-title":"Cranial ultrasound findings in preterm germinal matrix hemorrhage, sequelae and outcome","volume":"87","author":"Parodi","year":"2020","journal-title":"Pediatr. 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