Background: Diabetics constitute a significant percentage of hemodialysis (HD) patients with higher mortality, especially among male patients. A machine learning algorithm was used to optimize the prediction of time to death in male diabetic hemodialysis (MDHD) patients.
Methods: This multicenter retrospective study was conducted on adult MDHD patients (2011-2019) from 34 HD centers affiliated with Shiraz University of Medical Sciences. As a special type of machine learning approach, an elastic net penalized Cox proportional hazards (EN-Cox) regression was used to optimize a predictive regression model of time to death. To maximize the generalizability and simplicity of the final model, the backward elimination method was used to reduce the estimated predictive model to its core covariates.
Results: Out of 442 patients, 308 eligible cases were used in the final analysis. Their death proportion was estimated to be 28.2%. The estimated overall one-, two-, three-, and eight-year survival rates were 87.6%, 74.4%, 67.2%, and 53.9%, respectively. The EN-Cox regression model retained 14 (out of 35) candidate predictors of death. Five variables were excluded through backward elimination technique in the next step. Only 6 of the remaining 9 variables were statistically significant at the level of 5%. Body mass index (BMI)<25 kg/m (HR=2.75, <0.001), vascular access type (HR=2.60, <0.001), systolic blood pressure (1.02, =0.003), hemoglobin (11≤Hb≤12.5 g/dL: HR=3.00, =0.028 and Hb<11 g/dL: HR=2.95, =0.021), dialysis duration in each session≥4hour (HR=2.95, <0.001), and serum high-density lipoprotein cholesterol (HDL-C) (HR=1.02, =0.022) had significant effects on the overall survival (OS) time.
Conclusion: Anemia, hypotension, hyperkalemia, having central venous catheter (CVC) as vascular access, a longer dialysis duration in each session, lower BMI and HDL-C were associated with lower mortality in MDHD patients.
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http://dx.doi.org/10.34172/aim.27746 | DOI Listing |
J Agric Saf Health
October 2024
Department of Biological and Agricultural Engineering, University of California, Davis, California, USA.
Highlights: An AI-driven system for predicting and preventing ATV crashes was developed. Machine learning model achieved rollover prediction accuracy of over 99%. The system has the potential to significantly reduce ATV-related injuries and fatalities by enabling preemptive actions.
View Article and Find Full Text PDFCan J Psychiatry
March 2025
Department of Psychiatry, University of Oxford, Oxford, UK.
ObjectiveWe summarize the key steps to develop and assess an innovative online, evidence-based tool that supports shared decision-making in routine care to personalize antidepressant treatment in adults with depression. This PETRUSHKA tool is part of the PETRUSHKA trial (Personalize antidEpressant Treatment foR Unipolar depreSsion combining individual cHoices, risKs, and big datA).MethodsThe PETRUSHKA tool: (a) is based on prediction models, which use a combination of advanced analytics, i.
View Article and Find Full Text PDFJ Acoust Soc Am
March 2025
Department of Information and Communications Engineering, Aalto University, Espoo 02150, Finland.
Vocal intensity is quantified by sound pressure level (SPL). The SPL can be measured by either using a sound level meter or by comparing the energy of the recorded speech signal with the energy of the recorded calibration tone of a known SPL. Neither of these approaches can be used if speech is recorded in real-life conditions using a device that is not calibrated for SPL measurements.
View Article and Find Full Text PDFRadiol Cardiothorac Imaging
April 2025
Department of Radiology, Naval Medical Center San Diego, 34800 Bob Wilson Dr, San Diego, CA 92134-5000.
Purpose To evaluate the accuracy of detection based on level of interpreter experience when reporting coronary artery calcium (CAC) on dual-energy (DE) posteroanterior (PA) and lateral chest radiographs and demonstrate the accuracy of specific imaging clues. Materials and Methods Retrospective review of 45-70-year-old patients who underwent DE PA and lateral chest radiography between March 1, 2021, and June 30, 2021, and also underwent noncontrast chest CT scan within 3 years. Following instruction of DE principles and the appearance of CAC, seven readers interpreted the DE chest radiographs to state if CAC was possibly present, definitely present, or not present; estimate ordinal CAC score; and report imaging clues present.
View Article and Find Full Text PDFHeadache
March 2025
Department of Otolaryngology, Hospital Universitario San Cecilio, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Granada, Spain.
Objectives: To perform a systematic review and meta-analysis to evaluate the effectiveness of machine learning (ML) algorithms in the diagnosis of vestibular migraine.
Background: Due to the absence of defined biomarkers for diagnosing vestibular migraine (VM), it is valuable to determine which clinical, physical, and exploratory information is most crucial to diagnosing this disease. The use of artificial intelligence tools could streamline this process.
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