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New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model
ObjectiveThe high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic...
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Identification of neurological complications in childhood influenza: a random forest model
BackgroundAmong the neurological complications of influenza in children, the most severe is acute necrotizing encephalopathy (ANE), with a high...
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A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia
BackgroundPostoperative pain is one of the most common complications after surgery. In order to detect early and intervene in time for moderate to...
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Construction and evaluation of a column chart model and a random forest model for predicting the prognosis of hydrodistention surgery in BPS/IC patients based on preoperative CD117, P2X3R, NGF, and TrkA levels
ObjectiveThis study seeks to investigate independent risk factors affecting the prognoses of patients with bladder pain syndrome/interstitial...
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Development and validation of a random forest model to predict functional outcome in patients with intracerebral hemorrhage
ObjectiveTo develop and validate a machine learning (ML)–based model to predict functional outcome in Chinese patients with intracerebral hemorrhage...
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Prediction of HER2 status via random forest in 3257 Chinese patients with gastric cancer
The accurate evaluation of human epidermal growth factor receptor 2 (HER2) is crucial for successful trastuzumab-based therapy in individuals with...
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Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections
PurposeSigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide....
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To identify important MRI features to differentiate hepatic mucinous cystic neoplasms from septated hepatic cysts based on random forest
ObjectiveTo identify important MRI features to differentiate hepatic mucinous cystic neoplasms (MCN) from septated hepatic cysts (HC) using random...
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A prediction model based on random survival forest analysis of the overall survival of elderly female papillary thyroid carcinoma patients: a SEER-based study
ObjectivePapillary thyroid carcinoma (PTC) is a common malignancy whose incidence is three times greater in females than in males. The prognosis of...
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Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction
BackgroundMachine learning tools such as random forests provide important opportunities for modeling large, complex modern data generated in...
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Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy
BackgroundBreast cancer (BC) is the most common malignant tumor around the world. Timely detection of the tumor progression after treatment could...
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Comparison of LASSO and random forest models for predicting the risk of premature coronary artery disease
PurposeWith the change of lifestyle, the occurrence of coronary artery disease presents a younger trend, increasing the medical and economic burden...
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Construction of a combined random forest and artificial neural network diagnosis model to screening potential biomarker for hepatoblastoma
PurposeThe purpose of our study is to identify potential biomarkers of hepatoblastoma (HB) and further explore the pathogenesis of it.
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Using random forest to identify correlates of depression symptoms among adolescents
PurposeAdolescent depression is a significant public health concern, and studying its multifaceted factors using traditional methods possess...
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A 3-Gene Random Forest Model to Diagnose Non-obstructive Azoospermia Based on Transcription Factor-Related Henes
Non-obstructive azoospermia (NOA) is one of the most severe forms of male infertility, but its diagnosis biomarkers with high sensitivity and...
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Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness
BackgroundThe Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries....
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Novel gene signatures predicting and immune infiltration analysis in Parkinson’s disease: based on combining random forest with artificial neural network
BackgroundParkinson’s disease (PD) ranks as the second most prevalent neurodegenerative disorder globally, and its incidence is rapidly rising. The...
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Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury
BackgroundTreatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care...
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A prediction model of elderly hip fracture mortality including preoperative red cell distribution width constructed based on the random survival forest (RSF) and Cox risk ratio regression
SummaryAn independent correlation between pre-RDW and 1-year mortality after surgery in elderly hip fracture can be used to predict mortality in...
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Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models
ObjectiveBody fluid mixtures are complex biological samples that frequently occur in crime scenes, and can provide important clues for criminal case...