AIMC Topic: Female

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Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PURPOSE: The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).

Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study.

BMC nephrology
BACKGROUND: Acute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study's primary objective is to help identify which ICU patients are at high ris...

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

Journal of medical Internet research
BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a l...

Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination.

Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients.

International journal of medical sciences
Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications a...

Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep-learning algorithm that performs differential diagnosi...

An interdisciplinary team-training protocol for robotic gynecologic surgery improves operating time and costs: analysis of a 4-year experience in a university hospital setting.

Journal of robotic surgery
Main aim of this study is to assess the effect of a structured, interdisciplinary, surgical, team-training protocol in robotic gynecologic surgery, with the gradual integration of an advanced nurse practitioner. Data from all robotic surgical procedu...

Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.

Archives of gynecology and obstetrics
PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating algorithm to identify these patients without bile acid measurements.

Translating amyloid PET of different radiotracers by a deep generative model for interchangeability.

NeuroImage
It is challenging to compare amyloid PET images obtained with different radiotracers. Here, we introduce a new approach to improve the interchangeability of amyloid PET acquired with different radiotracers through image-level translation. Deep genera...