The Pediatric infectious disease journal
Jul 26, 2024
BACKGROUND: Timely diagnosis of neonatal sepsis is challenging. We aimed to systematically evaluate the diagnostic performance of sophisticated machine learning (ML) techniques for the prediction of neonatal sepsis.
Leukemia is a type of blood cell cancer that is in the bone marrow's blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemia...
BACKGROUND: The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC).
The American journal of gastroenterology
Jul 25, 2024
INTRODUCTION: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.
BACKGROUND: Current diagnostic methods cannot effectively distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (ATB). This study aims to explore novel non-invasive diagnostic biomarkers for LTBI and to elucidate possible m...
Journal of cancer research and clinical oncology
Jul 25, 2024
PURPOSE: Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.
Respiration; international review of thoracic diseases
Jul 24, 2024
INTRODUCTION: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (A...
AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up.
The purpose of this study was to develop a machine learning model for predicting 30-day readmission after bariatric surgery based on laboratory tests. Data were collected from patients who underwent bariatric surgery between 2018 and 2023. Laboratory...
INTRODUCTION: Treatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models to predict inpatient mortality in a...