Publications by authors named "Wendi Qu"
Article Synopsis
- The study aimed to analyze PTSD symptoms, psychiatric comorbidities, treatments, healthcare utilization, and costs before and after a PTSD diagnosis among U.S. adults.
- It examined data from 26,306 adults who received PTSD-related medication, noting that a high percentage experienced symptoms and comorbidities in the 6 months prior to diagnosis.
- Findings indicated that while symptoms and healthcare costs increased immediately after diagnosis, they tended to decrease within 6 to 12 months, highlighting the need for ongoing patient monitoring.
Background: Because kidney transplant recipients may be at increased risk for deep vein thrombosis (DVT) following transplantation, we investigated the incidence, risk factors, treatments and outcomes of early DVT among kidney transplant recipients.
Methods: An observational, single-centre cohort study was conducted among adult kidney transplant recipients from Jan. 1, 2005, to Dec.
Background: Attention-deficit/hyperactivity disorder (ADHD) is a common neurobehavioral disorder affecting approximately 10.0% of children and 6.5% of adolescents in the United States (US).
View Article and Find Full Text PDFQuantifying uncertainty in machine learning classifiers for medical imaging.
Int J Comput Assist Radiol Surg
April 2022
Purpose: Machine learning (ML) models in medical imaging (MI) can be of great value in computer aided diagnostic systems, but little attention is given to the confidence (alternatively, uncertainty) of such models, which may have significant clinical implications. This paper applied, validated, and explored a technique for assessing uncertainty in convolutional neural networks (CNNs) in the context of MI.
Materials And Methods: We used two publicly accessible imaging datasets: a chest x-ray dataset (pneumonia vs.
Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging.
Int J Comput Assist Radiol Surg
December 2020
Purpose: Machine learning (ML) algorithms are well known to exhibit variations in prediction accuracy when provided with imbalanced training sets typically seen in medical imaging (MI) due to the imbalanced ratio of pathological and normal cases. This paper presents a thorough investigation of the effects of class imbalance and methods for mitigating class imbalance in ML algorithms applied to MI.
Methods: We first selected five classes from the Image Retrieval in Medical Applications (IRMA) dataset, performed multiclass classification using the random forest model (RFM), and then performed binary classification using convolutional neural network (CNN) on a chest X-ray dataset.