AIMC Topic: Bilirubin
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Machine learning models including preoperative and postoperative albumin-bilirubin score: short-term outcomes among patients with hepatocellular carcinoma.
HPB : the official journal of the International Hepato Pancreato Biliary Association
Jul 25, 2024
BACKGROUND: We sought to assess the impact of various perioperative factors on the risk of severe complications and post-surgical mortality using a novel maching learning technique.
Middle Aged
Risk Assessment
Postoperative Complications
Time Factors
Female
Risk Factors
Aged
Male
Predictive Value of Tests
Bilirubin
Hepatectomy
Serum Albumin, Human
Serum Albumin
Hospital Mortality
Liver Neoplasms
Carcinoma, Hepatocellular
Treatment Outcome
Humans
Machine Learning
Databases, Factual
Retrospective Studies
Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.
Frontiers in immunology
May 28, 2024
INTRODUCTION: This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scor...
Machine Learning to Improve Accuracy of Transcutaneous Bilirubinometry.
Neonatology
Apr 29, 2024
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
Feb 20, 2024
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...
Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function.
Cancer medicine
Aug 23, 2023
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...
Diagnosis of liver diseases based on artificial intelligence.
Biotechnology & genetic engineering reviews
Mar 26, 2023
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 ...
Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder.
Journal of affective disorders
Apr 16, 2022
BACKGROUND: Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).
Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI with deep learning.
Pediatric research
Jun 5, 2021
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...
The Challenges of Implementing Artificial Intelligence into Surgical Practice.
World journal of surgery
Oct 13, 2020
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...
Humans
Cholangiopancreatography, Endoscopic Retrograde
Bilirubin
Female
Liver Function Tests
Aged
Choledocholithiasis
Artificial Intelligence
Middle Aged
Aged, 80 and over
Machine Learning
Predictive Value of Tests
Adult
Computer Simulation
Male
Acute Disease
Retrospective Studies
Biliary Tract Diseases
Identification of exacerbation risk in patients with liver dysfunction using machine learning algorithms.
PloS one
Oct 9, 2020
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...
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