AIMC Topic: Bilirubin

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Association of albumin-bilirubin grade with prognosis in ICU patients with pulmonary edema: a retrospective cohort study and a predictive model based on machine learning.

BMC pulmonary medicine
BACKGROUND: The Albumin-Bilirubin (ALBI) grade was initially used to assess liver reserve function in patients with cirrhosis and has since been applied in the prognostic evaluation of various diseases. This study explored the relationship between th...

Integrated machine learning identifies biomarkers for bilirubin-induced Alzheimer's disease-like lesions in neonates and adults.

Scientific reports
Neurological impairments resulting from bilirubin encephalopathy represent a hallmark of bilirubin's neurotoxic effects. Earlier research suggests that bilirubin may contribute to Alzheimer's disease (AD) pathology by inducing neuronal necrosis and a...

Bilirubin biosensors for liver disease management.

Clinica chimica acta; international journal of clinical chemistry
Bilirubin is a vital biomarker that plays a significant role in assessing liver function and diagnosing hemolytic disorders. While current detection methods are indeed sufficient for clinical practice, the accurate and timely detection of bilirubin l...

Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms.

BMC anesthesiology
BACKGROUND: With the aging demographic on the rise, we're seeing a spike in the occurrence of postoperative delirium (POD). Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of cr...

MAMSI: Integration of Multiassay Liquid Chromatography-Mass Spectrometry Metabolomics Data Using Multiview Machine Learning.

Analytical chemistry
Liquid chromatography-mass spectrometry (LC-MS) is a commonly used analytical technique in untargeted metabolomics. However, the diverse chemical and physical properties of metabolites often require the use of several different analytical assays for ...

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Scientific reports
Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely detection and intervention are crucial in preventing severe health outcomes. In this study, we implemen...

Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network.

Scientific reports
Neonatal jaundice, characterized by elevated bilirubin levels causing yellow discoloration of the skin and eyes in newborns, is a critical condition requiring accurate and timely diagnosis. This study proposes a novel approach using 1D Convolutional ...

Machine Learning Models predicting Decompensation in Cirrhosis.

Journal of gastrointestinal and liver diseases : JGLD
BACKGROUND AND AIMS: Decompensation of cirrhosis significantly decreases survival, thus, prevention of complications is paramount. We used machine learning techniques to identify parameters predicting decompensation.

Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice.

Scientific reports
Untreated neonatal jaundice can have severe consequences. Effective screening for neonatal jaundice can prevent long-term complications in infants. Non-invasive approaches may be beneficial in settings with limited resources. This feasibility study e...

Artificial Intelligence non-invasive methods for neonatal jaundice detection: A review.

Artificial intelligence in medicine
Neonatal jaundice is a common and potentially fatal health condition in neonates, especially in low and middle income countries, where it contributes considerably to neonatal morbidity and death. Traditional diagnostic approaches, such as Total Serum...