AIMC Topic: ROC Curve

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Enhancing automatic diagnosis of thyroid nodules from ultrasound scans leveraging deep learning models.

Scientific reports
The thyroid gland is prone to various diseases, including thyroid nodules. Ultrasound is the primary diagnostic tool, but classification accuracy is often limited by radiologist expertise. Integrating Artificial Intelligence, particularly Deep Learni...

MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.

Scientific reports
Pediatric viral encephalitis is an acute central nervous system infection caused by various viruses, with diverse clinical manifestations and challenges in early diagnosis. The traditional diagnostic methods lack sufficient sensitivity and specificit...

Ability of the hypotension prediction index to predict hypotension in patients with septic shock in the intensive care unit.

Scientific reports
The hypotension prediction index (HPI) is a machine learning-based model for predicting hypotension. It provides good performance for predicting intraoperative hypotension but has rarely been studied in critically ill patients admitted to the intensi...

A novel adaptive sigma KNN model for depression and anxiety detection following the COVID 19 pandemic.

Scientific reports
Mental health disorders, such as depression and anxiety, are increasing, and thus, there is a necessity for accurate and effective detection. K-Nearest Neighbors (KNN) and extensions have been extensively used in disease detection. In this work, Adap...

Optimizing machine learning models for predicting health service access and determinants among pregnant women in rural Ethiopia.

Scientific reports
Pregnant women in rural Ethiopia face substantial barriers to accessing adequate healthcare services, contributing to adverse maternal and neonatal health outcomes. Traditional statistical approaches often fall short in capturing the complex, nonline...

Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.

Scientific reports
About 30% of temporal lobe epilepsy (TLE) cases are negative on MRI, so quantitative diagnosis based on clinical symptoms becomes challenging. There is an urgent need for an accurate and reliable method to differentiate patients with MRI-negative TLE...

Predicting distant metastasis in early-onset kidney cancer using machine learning: a SEER database study with external validation.

Clinical and experimental medicine
Patients with early-onset kidney cancer (EOKC) face a marked decline in prognosis after distant metastasis, yet the accuracy of current predictive methods remains limited. This study aims to develop a predictive model using multiple machine learning ...

Machine learning combined with body composition predicts surgical difficulty in mid-low rectal cancer surgery.

Annals of medicine
BACKGROUND: This study sought to identify critical body composition characteristics associated with surgical difficulty in Laparoscopic Total Mesorectal Excision (LaTME) and to develop and validate an interpretable machine learning model using body c...

Prediction of Postoperative Venous Thromboembolism in Patients With Traumatic Brain Injury: Model Development and Validation Study.

JMIR medical informatics
BACKGROUND: Venous thromboembolism (VTE) remains a critical cause of mortality among patients who are hospitalized. Patients with traumatic brain injury (TBI) are particularly susceptible to VTE due to coagulation abnormalities and immobilization. De...

Evaluating the Accuracy of the Frysian Questionnaire for Differentiation of Musculoskeletal Complaints for Triage of Musculoskeletal Diseases: Algorithm Development and Validation Study.

JMIR medical informatics
BACKGROUND: Inflammatory rheumatic diseases (IRDs) affect 5% of the general population, whereas 35% of the population experiences musculoskeletal concerns. IRDs cause early disability, reduced life expectancy, and considerable health care costs. Earl...