ArticleArchives of toxicology2024
Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.
Article in Archives of toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
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- Exploratory Machine Learning-Based Classification of Type 2 Diabetes Using Routine Clinical Parameters: A Single-Center Comparative Study.Healthcare (Basel, Switzerland) · 2026Article
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Subject-Independent Depression Recognition from EEG Using an Improved Bidirectional LSTM with Dynamic Vector Routing.Bioengineering (Basel, Switzerland) · 2026Article
- Transformer-enhanced deep ensemble for multi-class liver disease classification using computed tomography images.Scientific reports · 2026Article
- Current status and future challenges of transition-metal oxides as enzyme-mimetics for detection of clinically relevant biomarkers: a comprehensive review.Mikrochimica acta · 2026Review
- Automated machine learning model to predict anti-tuberculosis drug-induced liver injury in patients with tuberculous meningitis.Frontiers in pharmacology · 2026Article
- Association of prediabetes phenotypes with metabolic dysfunction-associated fatty liver disease and liver fibrosis: a population-based study.Therapeutic advances in endocrinology and metabolism · 2026Article
- A novel clustered-based binary grey wolf optimizer to solve the feature selection problem for uncovering the genetic links between non-Hodgkin lymphomas and rheumatologic diseases.Health information science and systems · 2025Article
- REST/NRSF Regulation of Epilepsy and Cognitive Impairment: Mechanisms and EEG Correlations.Molecular neurobiology · 2025Article
- Machine learning-based prediction of speed of sound in fatty acid ethyl esters.Scientific reports · 2025Article
- Exploring digestive enzymes' differential affectivity of synthesized 2-thienyl-based chalcones.Future medicinal chemistry · 2025Article
- Biotransformation of Phenolic Acids in Foods: Pathways, Key Enzymes, and Technological Applications.Foods (Basel, Switzerland) · 2025Review
- Efficacy of 6-nitrobenzo[d]thiazol-2 Amine Derivative (N3) in Mitigating PTZ-Induced Epileptic Conditions Via Modulation of Inflammatory and Neuroprotective Pathways in-vivo Zebrafish.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2025Article
- FCN-PD: An Advanced Deep Learning Framework for Parkinson's Disease Diagnosis Using MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Predictive models of digestible and metabolizable energy of wheat in growing pigs.Frontiers in nutrition · 2025Article
- Advancing epileptic seizure recognition through bidirectional LSTM networks.Frontiers in computational neuroscience · 2025Article
- Developing and Validating a Robust RP-HPLC Method for Metoclopramide and Camylofin Simultaneous Analysis Using Response Surface Methodology.International journal of analytical chemistry · 2025Article
- Mobile apps, AI, and teletherapy: a comprehensive review of digital mental health tools for nurses.Frontiers in public health · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises concerns among individuals. This study aims to establish an automated machine learning (autoML) model for forecasting the risk of abnormal increase of transaminase levels while undergoing VPA therapy for 1995 epilepsy patients. The study employed the two-tailed T test, Chi-square test, and binary logistic regression analysis, selecting six clinical parameters, including age, stature, leukocyte count, Total Bilirubin, oral dosage of VPA, and VPA concentration. These variables were used to build a risk prediction model using "H2O" autoML platform, achieving the best performance (AUC training = 0.855, AUC test = 0.789) in the training and testing data set. The model also exhibited robust accuracy (AUC valid = 0.742) in an external validation set, underscoring its credibility in anticipating VPA-induced transaminase abnormalities. The significance of the six variables was elucidated through importance ranking, partial dependence, and the TreeSHAP algorithm. This novel model offers enhanced versatility and explicability, rendering it suitable for clinicians seeking to refine parameter adjustments and address imbalanced data sets, thereby bolstering classification precision. To summarize, the personalized prediction model for VPA-treated epilepsy, established with an autoML model, displayed commendable predictive capability, furnishing clinicians with valuable insights for fostering pharmacovigilance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.