AIMC Topic: Aged

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Differences across various ideal lumbar lordosis measurement formulas for patient-specific sagittal alignment goals.

Neurosurgical focus
OBJECTIVE: Multiple studies in the past have developed equations to determine the ideal lumbar lordosis (ILL) in the sagittal plane. These equations differ but all look to accomplish the same goal of providing the surgeon with specific alignment targ...

Early outcomes with virtual surgical planning software and patient-specific instrumentation in adult spinal deformity.

Neurosurgical focus
OBJECTIVE: Software engineering innovations have led to the development of virtual surgical planning software (VSPS) for deformity correction. VSPS uses calibrated radiographs and machine learning predictive models to simulate postoperative spinopelv...

Optimizing predictive model performance in adult spinal deformity surgery: a comparative head-to-head analysis of learning models for perioperative complications.

Neurosurgical focus
OBJECTIVE: The aim of this study was to develop and compare 4 predictive algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and neural network (NN), for perioperative outcomes in adult spinal deformit...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

Critical care explorations
OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventi...

Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline.

Human brain mapping
There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four indiv...

Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.

Anticancer research
BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed to develop a model for predicting the severity of bacteremia in patients with FN and hematologic ma...

Four Different Artificial Intelligence Models Logistic Regression to Enhance the Diagnostic Accuracy of Fecal Immunochemical Test in the Detection of Colorectal Carcinoma in a Screening Setting.

Anticancer research
BACKGROUND/AIM: This study aimed to evaluate the diagnostic accuracy (DA) of four artificial intelligence (AI) models compared to logistic regression (LR) in enhancing the performance of the fecal immunochemical test (FIT) for the detection of colore...

Perspectives of physicians, nurses, and patients on the use of artificial intelligence and robotic nurses in healthcare.

International nursing review
AIM: This study aims to assess the perspectives of physicians, nurses, and patients in Turkey regarding the integration of artificial intelligence (AI) and robotic nurses in healthcare settings while exploring their attitudes toward the use of robots...

Artificial intelligence assisted nutritional risk evaluation model for critically ill patients: Integration of explainable machine learning in intensive care nutrition.

Asia Pacific journal of clinical nutrition
BACKGROUND AND OBJECTIVES: Critically ill patients require individualized nutrition support, with assessment tools like Nutrition Risk Screening 2002 and Nutrition Risk in the Critically Ill scores. Challenges in continu-ous nutrition care prompt the...