AIMC Topic: ROC Curve

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Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models.

Radiology
Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. While some studies report superlative accuracy of radiomic machine learning (ML) models, the published ...

Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.

BMC nephrology
BACKGROUND: Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and ...

Using artificial intelligence in a primary care setting to identify patients at risk for cancer: a risk prediction model based on routine laboratory tests.

Clinical chemistry and laboratory medicine
OBJECTIVES: To evaluate the ability of an artificial intelligence (AI) model to predict the risk of cancer in patients referred from primary care based on routine blood tests. Results obtained with the AI model are compared to results based on logist...

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVES: We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques.

Machine Learning Models for Slope Stability Classification of Circular Mode Failure: An Updated Database and Automated Machine Learning (AutoML) Approach.

Sensors (Basel, Switzerland)
Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development...

An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study.

Breast cancer research : BCR
BACKGROUND: The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the bre...

A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System.

Sensors (Basel, Switzerland)
As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most cu...

RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction.

Journal of chemical information and modeling
The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational methods have been proposed in the past. However, the majority of the past methods are time consuming and...

A machine learning method for predicting the probability of MODS using only non-invasive parameters.

Computer methods and programs in biomedicine
OBJECTIVES: Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients However, existing methods are invasive, easily affected by artifacts and can be difficult to perform...