AIMC Topic: Severity of Illness Index

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Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis.

Scientific reports
Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging, clinical laboratory tests or clinical history, resulting in subtle changes that may occur between visits being missed. Mobile t...

Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich ataxia.

Scientific reports
Friedreich ataxia (FRDA) is a rare, inherited progressive movement disorder for which there is currently no cure. The field urgently requires more sensitive, objective, and clinically relevant biomarkers to enhance the evaluation of treatment efficac...

MixOmics Integration of Biological Datasets Identifies Highly Correlated Variables of COVID-19 Severity.

International journal of molecular sciences
Despite several years passing since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity of COVID-19 disease and complications of SARS-CoV-2 infection. While many large-scale multi-omic data...

Machine Learning Models Predicting Hospital Admissions During Chemotherapy Utilising Longitudinal Symptom Severity Reports and Patient-Reported Outcome Measures.

Studies in health technology and informatics
Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients' well-being. Machine learning (ML) models identifying patients at risk of emergency admissions are often developed on data lacking pa...

Artificial intelligence for severity triage based on conversations in an emergency department in Korea.

Scientific reports
In the fast-paced emergency departments, where crises unfold unpredictably, the systematic prioritization of critical patients based on a severity classification is vital for swift and effective treatment. This study aimed to enhance the quality of e...

Classification of lung cancer severity using gene expression data based on deep learning.

BMC medical informatics and decision making
Lung cancer is one of the most prevalent diseases affecting people and is a main factor in the rising death rate. Recently, Machine Learning (ML) and Deep Learning (DL) techniques have been utilized to detect and classify various types of cancer, inc...

Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study.

Acta dermato-venereologica
Atopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Danish national register data, adults with atopic dermatitis can only be identified if they have a hospital-diagnosed atopic dermatitis. The purpose of ...

Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning.

Respiratory research
BACKGROUND: Pneumonia is a major threat to the health of children, especially those under the age of five. Mycoplasma  pneumoniae infection is a core cause of pediatric pneumonia, and the incidence of severe mycoplasma pneumoniae pneumonia (SMPP) has...

Prediction models based on machine learning algorithms for COVID-19 severity risk.

BMC public health
BACKGROUND: The World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X; therefore, understanding the epidemiological experiences of COVID-19 is c...

Constructing a prediction model for acute pancreatitis severity based on liquid neural network.

Scientific reports
Acute pancreatitis (AP) is a common disease, and severe acute pancreatitis (SAP) has a high morbidity and mortality rate. Early recognition of SAP is crucial for prognosis. This study aimed to develop a novel liquid neural network (LNN) model for pre...