AIMC Topic: Severity of Illness Index

Clear Filters Showing 31 to 40 of 896 articles

Construction of a predictive model for the risk of moderate-to-severe cancer-related fatigue in colorectal cancer chemotherapy patients: an interpretable machine learning approach.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: This study aimed to analyze the influencing factors of moderate-to-severe cancer-related fatigue (CRF) in colorectal cancer (CRC) chemotherapy patients and to develop a predictive risk stratification model.

Post-COVID-19 disruption of the respiratory microbiome modulates : a multi-center retrospective investigation study.

Emerging microbes & infections
Since the COVID-19 pandemic, there has been a notable resurgence of pneumonia (MPP) in children, with a concerning rise in the severity of cases. Although changes in post-pandemic respiratory infection patterns have been documented, the reasons behi...

Harnessing the Power of Technology to Transform Delirium Severity Measurement in the Intensive Care Unit: Protocol for a Prospective Cohort Study.

JMIR research protocols
BACKGROUND: Delirium, an acute brain dysfunction, is a complication in up to 50% of patients in the intensive care unit (ICU). Measuring and mitigating delirium severity can reduce associated morbidity and improve long-term health outcomes post disch...

Predicting severe renal dysfunction in alcohol-associated cirrhosis: Comparative performance of liver function scores and machine learning models.

PloS one
BACKGROUND: Renal dysfunction is a frequent and clinically relevant complication of cirrhosis, yet chronic kidney disease (CKD) often remains underrecognized, particularly in non-acute settings. Early identification of at-risk patients is essential t...

Submovements Derived from Wearable Sensors Capture Ataxia Severity and Differ Across Motor Tasks and Directions of Motion.

Cerebellum (London, England)
Digital measures derived from wearable sensors are a promising approach for assessing motor impairment in clinical trials. Submovements, which are velocity curves extracted from time series data, have been successful in characterizing impaired moveme...

Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity.

Sleep & breathing = Schlaf & Atmung
PURPOSE: obstructive sleep apnea is underdiagnosed due to limited access to polysomnography (PSG). We aimed to assess the performances of Apneal, an application recording sound and movements thanks to a smartphone's microphone, accelerometer and gyro...

Pulmonary Embolism Survival Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data.

Journal of thoracic imaging
PURPOSE: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using computed tomography pulmonary angiography (CTPA), clinical data, and PE Severity In...

Common Variable Immunodeficiency Disorder: A Decade of Insights from a Cohort of 150 Patients in India and the Use of Machine Learning Algorithms to Predict Severity.

Journal of clinical immunology
Common Variable Immunodeficiency (CVID) is a heterogeneous disorder characterized by impaired antibody production and recurrent infections. In this study we investigated the clinical and immunological features of CVID in Indian patients and develops ...

Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.

Journal of medical Internet research
BACKGROUND: Depression is highly recurrent and heterogeneous. The unobtrusive, continuous collection of mobile sensing data via smartphones and wearable devices offers a promising approach to monitor and predict individual depression trajectories, di...