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Suggested Topics within your search.
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
Published 2021-01-01“…<h4>Introduction</h4>Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). …”
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Dynamic changes and future trend predictions of the global burden of anxiety disorders: analysis of 204 countries and regions from 1990 to 2021 and the impact of the COVID-19 pandemicResearch in context
Published 2025-01-01“…Finally, a Bayesian age-period-cohort (BAPC) model was employed to predict the prevalence trends of anxiety disorders from 2022 to 2050. …”
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A Prospective Observational Comparative study Between qSOFA and SIRS Scores for Early Prediction of Sepsis Outcome in an Emergency Medicine Department , Kozhencherry, South Central Kerala,India
Published 2025-02-01“…The introduction of the quick Sequential Organ Failure Assessment (qSOFA) score has sparked debate about its efficacy compared to the traditional Systemic Inflammatory Response Syndrome (SIRS) criteria in predicting sepsis outcomes. Objective To compare the performance of qSOFA and SIRS scores in early prediction of sepsis outcomes in an Emergency Medicine Department. …”
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A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database
Published 2022-06-01“…This may be an intuitive and practical tool for severe AKI prediction in the CSRU.…”
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Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model
Published 2025-01-01“…Abstract Background Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility. …”
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Effect of Friction Coefficient in Friction Stir Welding of B4C Reinforced AA5083 Metal Matrix Composites and Use of Fuzzy Clustering Technique for Weld Strength Prediction
Published 2024-01-01“…The results on the prediction of strength using the fuzzy clustering technique showed that the technique is effective in predicting the tensile strength values, with the root mean square error (RSME) of TiN, AlCrN, and DLC being 0.0027, 0.0016, and 0.0015, respectively, and the low RSME indicating that the prediction based on the fuzzy subtractive clustering technique is perfect and effective.…”
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Early applicability of retinopathy of prematurity score (ROPScore) and serum insulin growth factor-1 (IGF-1) for predicting treatment-requiring retinopathy of prematurity: a prospective cohort study
Published 2025-01-01“…Abstract Background Retinopathy of prematurity is a leading cause of blindness especially in developing countries where the accessibility to screening may not be available. Therefore, early prediction could help reallocate the resources for those at high risk. …”
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Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A)
Published 2019-01-01“…In silico predictive software allows assessing the effect of amino acid substitutions on the structure or function of a protein without conducting functional studies. …”
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A Clinical Nomogram Based on the Triglyceride-Glucose Index to Predict Contrast-Induced Acute Kidney Injury after Percutaneous Intervention in Patients with Acute Coronary Syndrome with Diabetes Mellitus
Published 2022-01-01“…In conclusion, we constructed and validated the nomogram to predict CI-AKI risk after PCI in patients with ACS and DM. …”
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Integration of single-cell and bulk RNA-sequencing data to construct and validate a signature based on NK cell marker genes to predict immunotherapy response and prognosis in colorectal cancer
Published 2025-02-01“…Abstract We aimed to create a NK cell marker genes-based signature to predict immunotherapy response and prognosis in colorectal cancer. …”
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The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe.
Published 2025-01-01“…The external-validity of the haematology-based COVID-19-predictions on diverse populations are yet to be fully investigated. …”
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Increased Co-Expression of PD-L1 and CTLA-4 Predicts Poor Overall Survival in Patients with Acute Myeloid Leukemia Following Allogeneic Hematopoietic Stem Cell Transplantation
Published 2025-01-01“…Importantly, PD-L1/CTLA-4 was the best combination model for predicting poor OS in AML patients following allo-HSCT, especially combined with minimal residual disease (MRD).Conclusion: High expression of ICs in BM of AML patients following allo-HSCT was related to poor outcomes, and increasing co-expression of PD-L1 and CTLA-4 might be one of the best immune biomarkers to predict outcomes in patients with AML.Keywords: acute myeloid leukemia, allo-HSCT, immune checkpoint, biomarker, prognosis…”
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The clinical value of Serum hyaluronic acid, procollagen III, N-terminal propeptide levels sST2 and cfDNA in predicting the myocardial damage in children with severe pneumonia
Published 2025-01-01“…Background: Severe pneumonia complicated by myocardial damage is a serious condition in children, and early prediction and intervention are crucial for improving outcomes. …”
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Assessment of six insulin resistance surrogate indexes for predicting stroke incidence in Chinese middle-aged and elderly populations with abnormal glucose metabolism: a nationwide prospective cohort study
Published 2025-02-01“…There is a lack of studies comparing the predictive values of different IR surrogate indexes for stroke risk among individuals with abnormal glucose metabolism. …”
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A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC<sub>50</sub> Inhibition Values of FLT3 Tyrosine Kinase
Published 2025-01-01“…This study aimed to develop a robust and user-friendly machine learning-based quantitative structure–activity relationship (QSAR) model to predict the inhibitory potency (pIC<sub>50</sub> values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. …”
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