AIMC Topic: Nomograms
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Construction of prediction models for novel subtypes in patients with arteriosclerosis obliterans undergoing endovascular therapy: an unsupervised machine learning study.
Journal of cardiothoracic surgery
Jun 25, 2024
BACKGROUND: Arteriosclerosis obliterans (ASO) is a chronic arterial disease that can lead to critical limb ischemia. Endovascular therapy is increasingly used for limb salvage in ASO patients, but the outcomes vary. The development of prediction mode...
Identifying novel circadian rhythm biomarkers for diagnosis and prognosis of melanoma by an integrated bioinformatics and machine learning approach.
Aging
Jun 20, 2024
Melanoma is a highly malignant skin tumor with poor prognosis. Circadian rhythm is closely related to melanoma pathogenesis. This study aimed to identify key circadian rhythm genes (CRGs) in melanoma and explore their potential as diagnostic and prog...
Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors.
Ultrasound in medicine & biology
Jun 19, 2024
PURPOSE: A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses.
A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.
Head & neck
Jun 18, 2024
BACKGROUND: To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyng...
Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning.
Clinical and experimental medicine
Jun 18, 2024
Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,8...
A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.
Frontiers in endocrinology
Jun 17, 2024
OBJECTIVE: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.
Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors.
Frontiers in endocrinology
Jun 17, 2024
OBJECTIVES: To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs).
A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.
Frontiers in immunology
Jun 14, 2024
BACKGROUND: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced comput...
Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.
Computer assisted surgery (Abingdon, England)
Jun 11, 2024
BACKGROUND: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study ...
A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission.
BMC medical informatics and decision making
Jun 7, 2024
BACKGROUND: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive ca...
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