AIMC Topic: Retrospective Studies

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Representativeness of a German AI-enabled data network for secondary epidemiological analysis based on electronic health records.

PloS one
INTRODUCTION: The ongoing digitalization of medicine, increased computing power and low-cost storage capacities enable the use of AI-based algorithms for epidemiological big data analysis of electronic patient records. The aim of this study was to ev...

Factors associated with complication of cranioplasty: CT-based risk assessment for early failure of autologous-bone cranioplasty.

Neurosurgical review
To determine whether preoperative noncontrast CT features predict early revision after autologous bone cranioplasty and to develop a simple CT-based risk framework. We retrospectively studied adults undergoing autologous cranioplasty at a single cent...

Prediction of Respiratory Decompensation in Patients Receiving Home Mechanical Ventilation: Machine Learning Model Development and Validation Study.

JMIR formative research
BACKGROUND: Chronic respiratory diseases often require long-term ventilatory support, leading to a growing number of patients treated with home mechanical ventilation (HMV). Despite advancements in telemonitoring with real-time tracking of noninvasiv...

Artificial intelligence for predicting the axial length response of orthokeratology in myopic children.

Physics in medicine and biology
This study aimed to automate the extraction of local corneal topography (CT) features in myopic children undergoing orthokeratology (OK), evaluate their causal effects on axial length (AL) control, and develop a predictive model for AL progression.We...

Deep learning diagnosis model of spinal tuberculosis based on CT bone window gradient attention mechanism: multi-center study.

Computer assisted surgery (Abingdon, England)
PURPOSE: To develop a deep learning model based on CT bone window images to enhance the accuracy of early diagnosis of spinal tuberculosis.

Predicting Left Ventricular Ejection Fraction Recovery After Percutaneous Coronary Intervention in Patients With Chronic Coronary Syndrome by Using Interpretable Machine Learning Models: Retrospective Study.

JMIR medical informatics
BACKGROUND: Accurately predicting left ventricular ejection fraction (LVEF) recovery after percutaneous coronary intervention (PCI) in patients with chronic coronary syndrome (CCS) is crucial for clinical decision-making.

Systematic Determinants of Global COVID-19 Burden: Longitudinal Time-Series Analysis Using Big Data-Driven Artificial Intelligence.

Journal of medical Internet research
BACKGROUND: The COVID-19 pandemic has transitioned into an endemic phase with heterogeneous resurgences. Despite widespread vaccination and public health measures, the interplay of viral evolution, population immunity, and environmental factors drive...

Developing and external validating a prediction model using machine learning and logistic regression: informing the surgical approach for robotic surgery based on preoperative MRI.

Journal of robotic surgery
BACKGROUND: Preoperative prediction of surgical difficulty in robotic-assisted total mesorectal excision for rectal cancer remains challenging. While pelvic anatomical parameters measured by MRI have been associated with surgical complexity in laparo...

Intelligent glucose management in hospitalized patients: Short-term glucose and adverse events prediction.

PloS one
The management of blood glucose in hospitalized patients is confined to retrospective interventions, preventing healthcare professionals from predicting patients' blood glucose levels and potential adverse events in advance. This study employs a deep...