Background: Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital.
Methods: We conducted a longitudinal study on preterm and term born neonates with serial measurements of total serum bilirubin in the first two weeks of life. An ensemble, that combines a logistic regression with a random forest classifier, was trained to discriminate between the two classes phototherapy treatment vs. no treatment.
Results: Of 362 neonates included in this study, 98 had a phototherapy treatment, which our model was able to predict up to 48 h in advance with an area under the ROC-curve of 95.20%. From a set of 44 variables, including potential laboratory and clinical confounders, a subset of just four (bilirubin, weight, gestational age, hours since birth) suffices for a strong predictive performance. The resulting early phototherapy prediction tool (EPPT) is provided as an open web application.
Conclusion: Early detection of clinically relevant hyperbilirubinemia can be enhanced by the application of machine learning. Existing guidelines can be further improved to optimize timing of bilirubin measurements to avoid toxic hyperbilirubinemia in high-risk patients while minimizing unneeded measurements in neonates who are at low risk.
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http://dx.doi.org/10.1038/s41390-019-0384-x | DOI Listing |
Port J Card Thorac Vasc Surg
January 2025
Department of Biomedicine - Unit of Anatomy, Faculty of Medicine, University of Porto; RISE@Health, Porto, Portugal.
Background: Aortoiliac disease (AID) is a variant of peripheral artery disease involving the infrarenal aorta and iliac arteries. Similar to other arterial diseases, aortoiliac disease obstructs blood flow through narrowed lumens or by embolization of plaques. AID, when symptomatic, may present with a triad of claudication, impotence, and absence of femoral pulses, a triad also referred as Leriche Syndrome (LS).
View Article and Find Full Text PDFDis Esophagus
January 2025
Department of Digestive and Oncological Surgery, Claude Huriez Hospital, Chu Lille, Lille, France.
Background: Malnutrition is common with esophagogastric cancers and is associated with negative outcomes. We aimed to evaluate if immunonutrition during neoadjuvant treatment improves patient's health-related quality of life (HRQOL) and reduces postoperative morbidity and toxicities during neoadjuvant treatment.
Methods: A multicenter double-blind randomized controlled trial (RCT) was undertaken.
BMC Infect Dis
January 2025
Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.
Background: The prognostic value of Chlamydia pneumoniae (Cpn) infection in postoperative lung cancer patients remains unclear. This study aimed to evaluate the association between Cpn infection and survival in lung cancer patients.
Methods: This study included 309 newly diagnosed primary lung cancer patients from three hospitals in Fuzhou, China.
BMC Nurs
January 2025
Nursing Department, Hamad Medical Corporation, Doha, P.O. Box 3050, Qatar.
Background: Artificial Intelligence (AI) is increasingly applied in healthcare to boost productivity, reduce administrative workloads, and improve patient outcomes. In nursing, AI offers both opportunities and challenges. This study explores nurses' perspectives on implementing AI in nursing practice within the context of Jordan, focusing on the perceived benefits and concerns related to its integration.
View Article and Find Full Text PDFBMC Public Health
January 2025
School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, No.13, Hangkong Road, Qiaokou District, Wuhan City, 430030, China.
Objective: Understanding healthcare-seeking propensity is crucial for optimizing healthcare utilization, especially for patients with chronic conditions like hypertension or diabetes, given their substantial burden on healthcare systems globally. This study aims to evaluate hypertensive or diabetic patients' healthcare-seeking propensity based on the severity of symptoms, categorizing symptoms as either major or minor. It also explores factors influencing healthcare-seeking propensity and examines whether healthcare-seeking propensity affects healthcare utilization and preventable hospitalizations.
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