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Bilirubin

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Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder.

Journal of affective disorders
BACKGROUND: Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).

"White bile" as a result of a malignant bile obstruction.

Revista espanola de enfermedades digestivas
An 87-year-old female was admitted for endoscopic retrograde cholangiopancreatography (ERCP) due to obstructive jaundice. On admission, total bilirubin was 15 mg/dl at the expense of direct bilirubin, GOT 356 U/l mg/dl, GPT 174 U/l, FA 2,358 U/l and ...

Identification of exacerbation risk in patients with liver dysfunction using machine learning algorithms.

PloS one
The prediction of the liver failure (LF) and its proper diagnosis would lead to a reduction in the complications of the disease and prevents the progress of the disease. To improve the treatment of LF patients and reduce the cost of treatment, we bui...

The Challenges of Implementing Artificial Intelligence into Surgical Practice.

World journal of surgery
BACKGROUND: Artificial intelligence is touted as the future of medicine. Classical algorithms for the detection of common bile duct stones (CBD) have had poor clinical uptake due to low accuracy. This study explores the challenges of developing and i...

Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI with deep learning.

Pediatric research
BACKGROUND: Differentiating acute bilirubin encephalopathy (ABE) from non-ABE in neonates with hyperbilirubinemia (HB) from routine magnetic resonance imaging (MRI) is extremely challenging since both conditions demonstrate similar T1 hyperintensitie...

Diagnosis of liver diseases based on artificial intelligence.

Biotechnology & genetic engineering reviews
Due to a series of problems in the diagnosis of liver disease, the mortality rate of liver disease patients is very high. Therefore, it is necessary for doctors and researchers to find a more effective non-invasive diagnostic method to meet clinical ...

Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function.

Cancer medicine
BACKGROUND: In those receiving chemotherapy, renal and hepatic dysfunction can increase the risk of toxicity and should therefore be monitored. We aimed to develop a machine learning model to identify those patients that need closer monitoring, enabl...

An APRI+ALBI-Based Multivariable Model as a Preoperative Predictor for Posthepatectomy Liver Failure.

Annals of surgery
OBJECTIVE AND BACKGROUND: Clinically significant posthepatectomy liver failure (PHLF B+C) remains the main cause of mortality after major hepatic resection. This study aimed to establish an aspartate aminotransferase to platelet ratio combined with a...

Machine Learning to Improve Accuracy of Transcutaneous Bilirubinometry.

Neonatology
INTRODUCTION: This study aimed to develop models for predicting total serum bilirubin by correcting errors of transcutaneous bilirubin using machine learning based on neonatal biomarkers that could affect spectrophotometric measurements of tissue bil...

Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.

European journal of clinical investigation
BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer th...