AIMC Topic: ROC Curve

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Development and Validation of a Novel Model to Discriminate Idiosyncratic Drug-Induced Liver Injury and Autoimmune Hepatitis.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIM: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.

Constructing a Predictive Model for Psychological Distress of Young- and Middle-Aged Gynaecological Cancer Patients.

Journal of evaluation in clinical practice
BACKGROUND: Cancer patients experience substantial psychological distress which causes the reduction of the quality of life. However, the risk of psychological distress has not been well predicted especially in young- and middle-aged gynaecological c...

Identification of DNA damage repair-related genes in sepsis using bioinformatics and machine learning: An observational study.

Medicine
Sepsis is a life-threatening disease with a high mortality rate, for which the pathogenetic mechanism still unclear. DNA damage repair (DDR) is essential for maintaining genome integrity. This study aimed to explore the role of DDR-related genes in t...

Machine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome.

The Journal of clinical endocrinology and metabolism
CONTEXT: Artificial intelligence research in the field of neuroendocrinology has accelerated. It is possible to develop noninvasive, easy-to-use and cost-effective procedures that can replace invasive procedures for the differential diagnosis of adre...

A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators.

Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.

Investigative ophthalmology & visual science
PURPOSE: Descemet membrane endothelial keratoplasty (DMEK) has emerged as a novel approach in corneal transplantation over the past two decades. This study aims to identify predisposing risk factors for post-DMEK ocular hypertension (OHT) and develop...

Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.

Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.

Frontiers in endocrinology
OBJECTIVE: To develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).

Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.

Clinical interventions in aging
BACKGROUND: Delirium superimposed on dementia (DSD) is a severe complication in older adults with dementia, marked by fluctuating cognition, inattention, and altered consciousness. Detection is challenging due to symptom overlap, yet it contributes t...