View clinical trials related to Machine Learning.
Filter by:Early assessment of pancreatic exocrine insufficiency (PEI) is crucial for determining appropriate chronic pancreatitis (CP) treatment plans, thereby avoiding unnecessary suffering and further complications in patients. A total of 504 patients with CP who underwent fecal elastase-1 test and contrast-enhanced CT at Changhai Hospital between January 2018 and April 2023 were enrolled in this study. The investigators aim to establish a fully automated workflow to establish a PEI classification model based on radiomic features, semantic features and deep learning features on enhanced CT images for evaluating the severity of PEI.
In this study, elderly patients with gastric cancer who underwent radical gastrectomy in Union Hospital Affiliated to Fujian Medical University from 2012 to 2018 were included as a derived cohort, and the training set and internal validation set were randomly divided by 4:1. Machine learning strategies of random forest, decision tree and support vector machine are used to construct survival prediction model. Each model was tested in an internal validation set and an external validation set consisting of patients from two other large medical centers.
Prediction of risk factors for adverse events after head and neck vascular recanalization surgery based on machine learning models
The study aims to develop a prediction model of premature treatment termination in psychosomatic hospitals using a machine learning approach.
Spleen laceration is a lethal abdominal trauma and usually be diagnosed by medical images such as computed tomography. Deep learning had been proved its usage in detect abnormalities in medical images. In this trial, we used de-identified registry databank to develop a novel deep-learning based algorithm to detect the spleen trauma and to identify the injury locations.
This is a prospective cohort study of women enrolled early in pregnancy, with randomization to determine the timing of three follow-up visits in the second and third trimester. At each of these follow-up visits, investigators will assess gestational age with the FAMLI technology and compare that estimate to the known gestational age established early in pregnancy.
To investigate the health effects of a new mobile application (app) for prevention and personalized treatment in people with chronic cardiovascular pathologies associated with body composition.
Malposition of an endotracheal tube (ETT) may lead to a great disaster. Developing a handy way to predict the proper depth of ETT fixation is in need. Deep convolutional neural networks (DCNNs) are proven to perform well on chest radiographs analysis. The investigators hypothesize that DCNNs can also evaluate pre-intubation chest radiographs to predict suitable ETT depth and no related studies are found. The authors evaluated the ability of DCNNs to analyze pre-intubation chest radiographs along with patients' data to predict the proper depth of ETT fixation before intubation.
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality. The researcher here developed several prediction models based on machine learning technique to allow early identification of patients who at the high risk of unfavorable kidney outcomes. The retrospective study comprised 2108 consecutive patients who underwent cardiac surgery from January 2017 to December 2020.
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality. The investigatorshere developed several prediction models based on machine learning technique to allow early identification of patients who at the high risk of unfavorable kidney outcomes. The retrospective study comprised 2108 consecutive patients who underwent cardiac surgery from January 2017 to December 2020.