AIMC Topic: ROC Curve

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Prediction of hospitalization due to heart diseases by supervised learning methods.

International journal of medical informatics Oct 16, 2014
BACKGROUND: In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that n...
Humans Artificial Intelligence Likelihood Functions Boston ROC Curve Algorithms Logistic Models Electronic Health Records Bayes Theorem Hospitalization Heart Diseases Risk Assessment
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A natural language processing pipeline for pairing measurements uniquely across free-text CT reports.

Journal of biomedical informatics Sep 6, 2014
OBJECTIVE: To standardize and objectivize treatment response assessment in oncology, guidelines have been proposed that are driven by radiological measurements, which are typically communicated in free-text reports defying automated processing. We st...
Area Under Curve Radiology Radiographic Image Interpretation, Computer-Assisted Data Mining Radiography, Abdominal Medical Oncology Radiology Information Systems Reproducibility of Results ROC Curve Computational Biology Natural Language Processing Machine Learning Software Algorithms Tomography, X-Ray Computed Humans
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Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease.

European journal of trauma and emergency surgery : official publication of the European Trauma Society Jun 14, 2014
PURPOSE: Mortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored a...
Aged Aged, 80 and over Adult Male Prognosis Humans Female Neural Networks, Computer Reproducibility of Results Predictive Value of Tests Outcome Assessment, Health Care Middle Aged Risk Factors Area Under Curve Risk Assessment Peptic Ulcer Perforation Comorbidity ROC Curve
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Classifier calibration using splined empirical probabilities in clinical risk prediction.

Health care management science Feb 21, 2014
The aims of supervised machine learning (ML) applications fall into three broad categories: classification, ranking, and calibration/probability estimation. Many ML methods and evaluation techniques relate to the first two. Nevertheless, there are ma...
Myocardial Infarction Probability Calibration Process Assessment, Health Care Humans Artificial Intelligence England Algorithms Hospital Mortality Risk Assessment ROC Curve
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Study on predicting breast cancer Ki-67 expression using a combination of radiomics and deep learning based on multiparametric MRI.

Magnetic resonance imaging Sep 1, 2025
PURPOSE: To develop a multiparametric breast MRI radiomics and deep learning-based multimodal model for predicting preoperative Ki-67 expression status in breast cancer, with the potential to advance individualized treatment and precision medicine fo...
Reproducibility of Results Aged Adult Sensitivity and Specificity Humans Female ROC Curve Image Processing, Computer-Assisted Contrast Media Magnetic Resonance Imaging Predictive Value of Tests Breast Neoplasms Radiomics Middle Aged Deep Learning Multiparametric Magnetic Resonance Imaging Breast Image Interpretation, Computer-Assisted Ki-67 Antigen Diffusion Magnetic Resonance Imaging
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Nutritional and lifestyle predictors of rectal bleeding in functional constipation: A machine learning approach.

International journal of medical informatics Sep 1, 2025
BACKGROUND: Rectal bleeding among young adults is an increasingly common clinical concern often linked with chronic constipation and unhealthy lifestyle habits. Early identification of at-risk individuals through machine learning models-based approac...
Adolescent ROC Curve Body Mass Index Constipation Dietary Fiber Humans Young Adult Life Style Gastrointestinal Hemorrhage Machine Learning Adult Female Male Risk Factors Surveys and Questionnaires
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Application of Fourier transform infrared (FTIR) spectroscopy in liquid biopsy to predict the response to the first-line immunotherapy in non-small-cell lung cancer (NSCLC) patients.

Biochemical and biophysical research communications Jul 22, 2025
The direction of anticancer therapies has changed in recent years, including the increasing use of immunotherapy. However, around 50 % of non-small-cell lung cancer (NSCLC) patients do not respond to immunotherapy. Therefore, it is important to find ...
Carcinoma, Non-Small-Cell Lung Spectroscopy, Fourier Transform Infrared Liquid Biopsy ROC Curve Lung Neoplasms Immunotherapy Treatment Outcome Aged Humans Male Female Middle Aged
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Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors.

Breast cancer research and treatment Jul 1, 2025
PURPOSE: Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist treatment strategies. The aim of this study was to explore the application...
Ultrasonography, Mammary Chemotherapy, Adjuvant Aged Adult Breast Neoplasms Radiomics Antineoplastic Combined Chemotherapy Protocols Treatment Outcome Humans Neoadjuvant Therapy Machine Learning Middle Aged Prognosis ROC Curve Female
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A Meta-Analysis of the Diagnostic Test Accuracy of Artificial Intelligence for Predicting Emergency Department Revisits.

Journal of medical systems Jun 16, 2025
The revisit of the emergency department (ED) is a key indicator of emergency care quality. Various strategies have been proposed to reduce ED revisits, including the use of artificial intelligence (AI) models for prediction. However, AI model perform...
Sensitivity and Specificity ROC Curve Emergency Service, Hospital Patient Readmission Humans Artificial Intelligence Machine Learning
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A deep learning model could screen for coronary heart disease from a "pseudo-normal" electrocardiogram.

Medicine Jun 13, 2025
BACKGROUND: This study aimed to develop a deep learning model (DLM) for rapid screening of coronary heart disease (CHD) using "pseudo-normal" electrocardiograms (ECGs), particularly focusing on patients who present with normal or near-normal ECGs at ...
Humans Deep Learning ROC Curve Electrocardiography Mass Screening Coronary Disease Female Aged Male Middle Aged
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