AIMC Topic: Adult

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Long non-coding RNAs in biomarking COVID-19: a machine learning-based approach.

Virology journal
BACKGROUND: The coronavirus pandemic that started in 2019 has caused the highest mortality and morbidity rates worldwide. Data on the role of long non-coding RNAs (lncRNAs) in coronavirus disease 2019 (COVID-19) is scarce. We aimed to elucidate the r...

Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study.

Scientific reports
Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis ...

A Multimodal Assistive-Robotic-Arm Control System to Increase Independence After Tetraplegia.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Following tetraplegia, independence for completing essential daily tasks, such as opening doors and eating, significantly declines. Assistive robotic manipulators (ARMs) could restore independence, but typically input devices for these manipulators r...

A Dynamic Window Method Based on Reinforcement Learning for SSVEP Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher info...

Multi-machine Learning Model Based on Habitat Subregions for Outcome Prediction in Adenomyosis Treated by Uterine Artery Embolization.

Academic radiology
RATIONALE AND OBJECTIVES: To establish and validate a predictive multi-machine learning model for the long-term efficacy of uterine artery embolization (UAE) in the treatment of adenomyosis based on habitat subregions.

Predicting Lymphovascular Invasion in Non-small Cell Lung Cancer Using Deep Convolutional Neural Networks on Preoperative Chest CT.

Academic radiology
RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images w...

Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.

Journal of imaging informatics in medicine
This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 t...

Predicting Musculoskeletal Loading at Common Running Injury Locations Using Machine Learning and Instrumented Insoles.

Medicine and science in sports and exercise
INTRODUCTION: Wearables have the potential to provide accurate estimates of tissue loads at common running injury locations. Here we investigate the accuracy by which commercially available instrumented insoles (ARION; ATO-GEAR, Eindhoven, The Nether...

Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.

British journal of clinical pharmacology
AIMS: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-bas...