AIMC Topic: Severity of Illness Index

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Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

PloS one
Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise relationship with mitochondrial pathways remains elusive, recent advances in machine learning present...

Improving fMRI-Based Autism Severity Identification via Brain Network Distance and Adaptive Label Distribution Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Machine learning methodologies have been profoundly researched in the realm of autism spectrum disorder (ASD) diagnosis. Nonetheless, owing to the ambiguity of ASD severity labels and individual differences in ASD severity, current fMRI-based methods...

Outcome measures in chronic urticaria: A comprehensive review.

Indian journal of dermatology, venereology and leprology
Chronic urticaria, characterised by pruritic wheals, angioedema or both significantly impacts individuals' quality of life. This review article examines the patient-reported outcome measures (PROMs) in chronic urticaria assessment, aiming to enhance ...

Construction of a Multi-View Deep Learning Model for the Severity Classification of Acute Pancreatitis.

Discovery medicine
BACKGROUND: Acute pancreatitis (AP) is a prevalent pathological condition of abdomen characterized by sudden onset, high incidence and complex progression. Timely assessment of AP severity is crucial for informing intervention decisions so as to dela...

A Multi-Label Deep Learning Model for Detailed Classification of Alzheimer's Disease.

Actas espanolas de psiquiatria
BACKGROUND: Accurate diagnosis and classification of Alzheimer's disease (AD) are crucial for effective treatment and management. Traditional diagnostic models, largely based on binary classification systems, fail to adequately capture the complexiti...

ML-Based Framework to Predict the Severity of the Symptomatology in Patients with Post-Acute COVID-19 Syndrome.

Studies in health technology and informatics
The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of symptoms following the acute COVID-19 infection. The patient's baseline clinical features were used ...

A tailored machine learning approach for mortality prediction in severe COVID-19 treated with glucocorticoids.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
BACKGROUNDThe impact of severe COVID-19 pneumonia on healthcare systems highlighted the need for accurate predictions to improve patient outcomes. Despite the established efficacy of glucocorticoids (GCs), variable patient respons...

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

European heart journal. Cardiovascular Imaging
AIMS: Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence (AI) analysis may assist in precise quantification and characterization of coronary a...

Explainable Machine Learning Based Prediction of Severity of Heart Failure Using Primary Electronic Health Records.

Studies in health technology and informatics
Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from pri...

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likeliho...