AIMC Topic: Adult

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Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a predictive model for osteoporosis and osteopenia prediction by fusing deep transfer learning (DTL) features and classical radiomics features based on single-source dual-energy computed tomography (C...

The relationship between satisfaction with life, dietary attitude, and comfort level in hemodialysis patients: Analysis with machine learning approach.

Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy
INTRODUCTION: Hemodialysis (HD) patients have become a serious public health problem, the number of which is increasing every year worldwide. This study aimed to determine the relationship between satisfaction with life, dietary attitude, and comfort...

Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network.

Computers in biology and medicine
As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown path...

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...

Visual feedbacks influence short-term learning of torque versus motion profile with robotic guidance among young adults.

Human movement science
Robotic assistance can improve the learning of complex motor skills. However, the assistance designed and used up to now mainly guides motor commands for trajectory learning, not dynamics learning. The present study explored how a complex motor skill...

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...

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.