AIMC Topic: Aged

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Early detection of vascular catheter-associated infections employing supervised machine learning - a case study in Lleida region.

BMC medical informatics and decision making
Healthcare-associated infections (HAIs), particularly Vascular Catheter-Associated Infections (VCAIs), are a significant concern, accounting for over 7% of all infections and are often linked to medical devices. Early detection of VCAIs before invasi...

Leveraging BERT for embedding ICD codes from large scale cardiovascular EMR data to understand patient diagnostic patterns.

BMC medical informatics and decision making
The integration of electronic medical records (EMRs) with artificial intelligence (AI) is enhancing medical research, particularly in real-world evidence (RWE) studies. Extracting insights from coded medical data, such as ICD-10 codes, is essential f...

Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.

BMC medical imaging
BACKGROUND: Gliomas exhibit a high recurrence rate, particularly in the peritumoural brain zone after surgery. This study aims to develop and validate a radiomics-based model using preoperative fluid-attenuated inversion recovery (FLAIR) and T1-weigh...

18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.

Medical oncology (Northwood, London, England)
Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer. In this study, we developed deep radiomic models to enhance the prediction of chemotherapy response after...

Multimodal anti fraud education improves cognitive emotional and behavioral engagement in older adults.

Scientific reports
This study examines the differential effectiveness of video-based versus text-based anti-fraud educational interventions in improving cognitive comprehension, emotional engagement, and behavioral intentions among older adults. Using a stratified samp...

AI-driven fusion of multimodal data for Alzheimer's disease biomarker assessment.

Nature communications
Alzheimer's disease (AD) diagnosis hinges on detecting amyloid beta (Aβ) plaques and neurofibrillary tau (τ) tangles, typically assessed using PET imaging. While accurate, these modalities are expensive and not widely accessible, limiting their utili...

Deep learning and radiomics fusion for predicting the invasiveness of lung adenocarcinoma within ground glass nodules.

Scientific reports
Microinvasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) require distinct treatment strategies and are associated with different prognoses, underscoring the importance of accurate differentiation. This study aims to develop a predictive m...

Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images.

PloS one
BACKGROUND: Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potentia...

Prediction of hematoma changes in spontaneous intracerebral hemorrhage using a Transformer-based generative adversarial network to generate follow-up CT images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
PURPOSE: To visualize and assess hematoma growth trends by generating follow-up CT images within 24 h based on baseline CT images of spontaneous intracerebral hemorrhage (sICH) using Transformer-integrated Generative Adversarial Networks (GAN).

Improving early detection of Alzheimer's disease through MRI slice selection and deep learning techniques.

Scientific reports
Alzheimer's disease is a progressive neurodegenerative disorder marked by cognitive decline, memory loss, and behavioral changes. Early diagnosis, particularly identifying Early Mild Cognitive Impairment (EMCI), is vital for managing the disease and ...