AIMC Topic: Middle Aged

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AKIML: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in c...

Developing a Machine-Learning Predictive Model for Retention of Posterior Cruciate Ligament in Patients Undergoing Total Knee Arthroplasty.

Orthopaedic surgery
OBJECTIVE: Predicting whether the posterior cruciate ligament (PCL) should be preserved during total knee arthroplasty (TKA) procedures is a complex task in the preoperative phase. The choice to either retain or excise the PCL has a substantial effec...

Combining serum microRNAs and machine learning algorithms for diagnosing infectious fever after HSCT.

Annals of hematology
Infection post-hematopoietic stem cell transplantation (HSCT) is one of the main causes of patient mortality. Fever is the most crucial clinical symptom indicating infection. However, current microbial detection methods are limited. Therefore, timely...

Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.

Cell reports. Medicine
Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonan...

Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning.

Computers, informatics, nursing : CIN
As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arr...

Emergency department risk model: timely identification of patients for outpatient care coordination.

The American journal of managed care
OBJECTIVE: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patie...

Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study.

International journal of surgery (London, England)
PURPOSE: The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism.

QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms.

JACC. Clinical electrophysiology
BACKGROUND: Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS.

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and im...

Impact of F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
We aimed to investigate the effects of F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. We extracted 1,037 F-FDG PET radiomic features q...