Random Forest Model for the Prediction of Herbal-Induced Liver Injury: Application to Molecules from the West African Pharmacopoeia...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy.
Herbal medicines play a crucial role in primary healthcare across West Africa, yet their potential for liver toxicity remains poorly documented. Predicting herbal-induced liver injury is therefore essential to ensure the safe use of traditional remedies. A Random Forest model was developed to predic...
| Publicado en: | Studies in Health Technology & Informatics Vol. 336; pp. 473 - 478 |
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| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194018838&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194018838 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2026 vid: 336 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194018838 194018838 194018838 10.3233/SHTI260200 194018838 ppf: 473 ppct: 5 formats: tig: atl: Random Forest Model for the Prediction of Herbal-Induced Liver Injury: Application to Molecules from the West African Pharmacopoeia...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy. aug: au: KANTAGBA, Yves M. K. BARRO, Seydou Golo NIKIEMA, Serge L. W. STACCINI, Pascal affil: Université NAZI BONI, Bobo Dioulasso, Burkina-Faso. sug: subj: Random Forest Prediction Models Medicine, Herbal Adverse Effects Liver Diseases Etiology Plants, Medicinal Adverse Effects Medicine, African Traditional Hepatotoxicity Etiology Risk Assessment Congresses and Conferences Italy Italy Human Africa, Western Phytochemicals Descriptive Statistics Fusidic Acid Data Analysis Software ROC Curve Artificial Intelligence Drug Toxicity Pharmacovigilance ab: Herbal medicines play a crucial role in primary healthcare across West Africa, yet their potential for liver toxicity remains poorly documented. Predicting herbal-induced liver injury is therefore essential to ensure the safe use of traditional remedies. A Random Forest model was developed to predict hepatotoxicity using a combined feature set of nine physicochemical descriptors and Morgan fingerprints R2 (1024 bits). The training dataset included reference compounds from the FDA DILIst dataset, while external validation was performed on the Greene dataset. Model performance was assessed using nested cross-validation and evaluated through multiple metrics including AUC-ROC, AUC-PR, F1-macro, and MCC. The combined descriptors-molecular fingerprints model achieved an external AUCROC of 0.83, AUC-PR of 0.85, and MCC of 0.55, demonstrating strong generalization capacity. Application of the model to 191 phytochemicals from the West African pharmacopoeia indicated that 80.1% were potentially hepatotoxic, with fusidic acid and nicamin showing the highest probabilities (>80%). These results confirm the reliability of the RF approach for hepatotoxicity prediction and highlight the need for systematic toxicological evaluation of traditional medicines. Artificial intelligence thus offers an efficient framework for integrating safety assessment into pharmacopoeia modernization. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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