Journal of the Chinese Medical Association : JCMA
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Comparative Study
Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.
Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors. ⋯ In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.
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Donor lymphocyte infusion (DLI) is effective for managing patients with hematologic malignancies after allogeneic hematopoietic stem cell transplant (HSCT). However, few studies have explored its optimal use in pediatric populations. Herein, we report our single-center experiences of DLI and factors for predicting its outcomes. ⋯ Patients' disease status before HSCT and DLI may help predict EFS. The use of DLI as a prophylactic and preemptive modality leads to a favorable 5-year EFS. To safely deliver DLI in children, clinicians must maintain vigilant monitoring and prepare patients in advance when escalating the dose to ≥5 × 10 7 CD3 + cells/kg.
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Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. ⋯ This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.
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The number of mature oocytes retrieved plays a significant role in determining embryo development and pregnancy outcomes of in vitro fertilization (IVF). However, studies investigating factors predictive of the efficacy of mature oocyte production (EMOP) after dual-trigger controlled ovarian stimulation (COS) are rare. This study aims to identify key predictors of EMOP during dual-trigger COS with a gonadotropin-releasing hormone (GnRH) antagonist protocol for IVF. ⋯ Stimulation duration combined with total oocyte count was identified as the most important factor associated with the EMOP during dual-trigger COS in IVF using a GnRH antagonist protocol.
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Observational Study
Serum brain-derived neurotrophic factor levels as a predictor for Alzheimer disease progression.
Brain-derived neurotrophic factor (BDNF) has been implicated in the pathophysiology of Alzheimer's disease (AD), and decreased peripheral levels of this protein are associated with an increased risk of developing the disease. This study focuses on whether serum BDNF levels could be used as a predictor of AD progression. ⋯ BDNF is a protective factor against AD progression and likely plays a role in establishing a link between AD pathology and clinical manifestations.