Hosting
Wednesday, February 5, 2025
Google search engine
HomeArtificial IntelligenceAn interpretable and transparent machine learning framework for appendicitis detection in pediatric...

An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients


  • He, K. & Rangel, S.J. Advances in the diagnosis and treatment of appendicitis in children. Given Surgeon. 559–33 (2021).

    Article PubMed Google Scholar

  • Malia, L. et al. Predictors for acute appendicitis in children. Pediatrics Emerging. Concern. 37 (12), e962-e968. https://doi.org/10.1097/PEC.0000000000001840 (2021).

    Article PubMed Google Scholar

  • Fujii, T., Tanaka, A., Katami, H. & Shimono, R. Usefulness of the pediatric appendicitis score for assessing the severity of acute appendicitis in children. Pediatrics Int. 62 (1), 70–73. https://doi.org/10.1111/ped.14032 (2020).

    Article PubMed Google Scholar

  • Fujishiro, J. et al. Laparoscopic versus open appendectomy for acute appendicitis in children: a nationwide retrospective study of postoperative outcomes. J. Gastrointest. Surgeon. 25 (4), 1036–1044. https://doi.org/10.1007/s11605-020-04544-3 (2021).

    Article PubMed Google Scholar

  • Feng, W., Zhao, XF, Li, MM & Cui, HL A clinical prediction model for complicated appendicitis in children under five years of age. BMC Pediatrician. 201–9. https://doi.org/10.1186/s12887-020-02286-4 (2020).

    Article Google Scholar

  • Fasihfar, Z., Rokhsati, H., Sadeghsalehi, H., Ghaderzadeh, M. & Gheisari, M. AI-driven malaria diagnosis: development of a robust model for accurate detection and classification of malaria parasites. Iran. J. Blood cancer. 15 (3), 112–124. https://doi.org/10.61186/ijbc.15.3.112 (2023).

    Article Google Scholar

  • Ghaderzadeh, M., Asadi, F., Ramezan Ghorbani, N., Almasi, S. & Taami, T. Towards applications of artificial intelligence (AI) in determining the severity of COVID-19 infection: Considering AI as a disease control strategy in future pandemics. Iran. J. Blood cancer. 15 (3), 93–111. https://doi.org/10.61186/ijbc.15.3.93 (2023).

    Article Google Scholar

  • Chadaga, K. et al. SADXAI: Predicting Social Anxiety Disorder Using Multiple Interpretable Artificial Intelligence Techniques. SLAS technology. 29 (2), 100129. https://doi.org/10.1016/j.slast.2024.100129 (2024).

    Article PubMed Google Scholar

  • Chadaga, K. et al. Explainable Artificial Intelligence Approaches for Predicting Prognosis of COVID-19 Using Clinical Markers. Science Rep. 14 (1), 1783. https://doi.org/10.1038/s41598-024-52428-2 (2024).

    Article PubMed PubMed Central Google Scholar

  • Nie, D. et al. Artificial intelligence distinguishes abdominal Henoch-Schönlein purpura from acute appendicitis in children. Int. J. Rheum. Dis. 26 (12), 2534–2542. https://doi.org/10.1111/1756-185X.14956 (2023).

    Article PubMed Google Scholar

  • Mijwil, MM & Aggarwal, K. A diagnostic test for people with appendicitis using machine learning techniques. Multimedia tools Appl. 81 (5), 7011–7023. https://doi.org/10.1007/s11042-022-11939-8 (2022).

    Article Google Scholar

  • Marcinkevics, R., Reis Wolfertstetter, P., Wellmann, S., Knorr, C. & Vogt, J.E. Using machine learning to predict the diagnosis, management and severity of appendicitis in children. Front. Pead. 9662183. https://doi.org/10.3389/fped.2021.662183 (2021).

    Article Google Scholar

  • Aydin, E. et al. A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children. Pediatrics Surgeon. Int. 36735–742. https://doi.org/10.1007/s00383-020-04655-7 (2020).

    Article PubMed Google Scholar

  • Akbulut, S. et al. Prediction of perforated and non-perforated acute appendicitis using machine learning-based explainable artificial intelligence. Diagnostics. 13 (6), 1173. https://doi.org/10.3390/diagnostics13061173 (2023).

    Article PubMed PubMed Central Google Scholar

  • Marcinkevičs, R. et al. Pediatric Appendicitis Dataset Regensburg. Zenodo; (2023).

  • Meyer, KE, van Witteloostuijn, A. & Beugelsdijk, S. What’s in ap? Reassessing best practices for conducting and reporting hypothesis-testing research. In: (eds Eden, L., Nielsen, BB & Verbeke, A.) Research methods in international business. JIBS Special Collections. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-22113-3_4 (2020).

    Chapter Google Scholar

  • Bolt, MA et al. Inference after multiple imputation for generalized additive models: a study of the median p-value rule with applications to the Pulmonary Hypertension Association Registry and Colorado COVID-19 hospitalization data. BMC Med. Res. Method. 22 (1), 148. https://doi.org/10.1186/s12874-022-01613-w (2022).

    Article PubMed PubMed Central Google Scholar

  • Ahsan, MM, Mahmud, MP, Saha, PK, Gupta, KD & Siddique, Z. Effect of data scaling methods on machine learning algorithms and model performance. Technologies. 9 (3), 52. https://doi.org/10.3390/technologies9030052 (2021).

    Article Google Scholar

  • Hancock, JT & Khoshgoftaar, TM Survey on categorical data for neural networks. J. big dates. 7 (1), 28. https://doi.org/10.1186/s40537-020-00305-w (2020).

    Article Google Scholar

  • Thabtah, F., Hammoud, S., Kamalov, F. & Gonsalves, A. Data imbalance in classification: experimental evaluation. Inf. Science 513https://doi.org/10.1016/j.ins.2019.11.004 (2020). :429 – 41.

  • Chen, Y., Chang, R. & Guo, J. Effects of data augmentation method borderline-SMOTE on emotion recognition from EEG signals based on convolutional neural network. IEEE Access. 947491–47502. https://doi.org/10.1109/ACCESS.2021.3068316 (2021).

    Article Google Scholar

  • Koopialipoor, M. et al. Introducing stacking machine learning approaches for predicting rock deformation. Transp. Geotechnics. 34100756. https://doi.org/10.1016/j.trgeo.2022.100756 (2022).

    Article Google Scholar

  • Feng, D.C., Wang, W.J., Mangalathu, S. & Taciroglu, E. Interpretable XGBoost-SHAP machine learning model for predicting shear strength of stubby RC walls. J. Structure. Scary. 147 (11), 04021173. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003115 (2021).

    Article Google Scholar

  • Visani, G. et al. Statistical stability indices for LIME: obtaining reliable explanations for machine learning models. Journal of the Operational Research Society. ;73(1):91–101., Statistical stability indices for LIME: obtaining reliable explanations for machine learning models. Journal of the Operational Research Society. 2022;73(1):91–101. (2022).

  • Khanna, V.V., Chadaga, K., Sampathila, N., Prabhu, S. & Chadaga, R. A machine learning and explainable artificial intelligence triage prediction system for COVID-19. Decision Analytics Journal. Be able to 6:100246. (2023). https://doi.org/10.1016/j.dajour.2023.100246

  • Sun, D., Ding, Y., Wen, H. & Zhang, F. A novel QLattice-based whitening machine learning model for landslide susceptibility mapping. Earth. Surfing. Proc. Country. 49 (1), 304–317. https://doi.org/10.1002/esp.5675 (2024).

    Article Google Scholar

  • Fernández, RR, de Diego, IM, Moguerza, JM & Herrera, F. Explanation sets: a general framework for machine learning explainability. Inf. Science 617464–481. https://doi.org/10.1016/j.ins.2022.10.084 (2022).

    Article Google Scholar

  • Stuke, A., Rinke, P. & Todorović, M. Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization. Mach. Learning: Science. Technology 2 (3), 035022. (2021).

    Google Scholar

  • Eskandari, S. & Javidi, M. M. A new hybrid bat algorithm with fast clustering-based hybridization. Evolve Intel. 13 (3), 427–442. https://doi.org/10.1007/s12065-019-00307-5 (2020).

    Article Google Scholar

  • Bi, J., Yuan, H., Zhai, J., Zhou, M. & Poor, H. V. Self-adaptive bat algorithm with genetic operations. IEEE/CAA J. Automatica Sinica. 9 (7), 1284–1294 (2022).

    Article Google Scholar

  • Kumar, V. & Kumar, D. A systematic review of the Firefly algorithm: past, present and future. Bow. Computer. Methods Eng.283269–3291. https://doi.org/10.1007/s11831-020-09498-y (2021).

    Article MathSciNet Google Scholar

  • Belete, DM & Huchaiah, MD Grid research in hyperparameter optimization of machine learning models for predicting HIV/AIDS test results. Int. J. Calculate. Appl. 44 (9), 875-886. https://doi.org/10.1080/1206212X.2021.1974663 (2022).

    Article Google Scholar

  • Ren, P. et al. A comprehensive review of neural architecture search: challenges and solutions. ACM computer. To survive. (CSUR). 54 (4), 1–34. https://doi.org/10.1145/3447582 (2021).

    Article Google Scholar

  • De Jonge, J. et al. Normal inflammatory markers and acute appendicitis: a national multicenter prospective cohort analysis. Int. J. Colorectal Dis. 36 (7), 1507–1513. https://doi.org/10.1007/s00384-021-03933-7 (2021).

    Article PubMed PubMed Central Google Scholar

  • Kim, JJ et al. Can normal inflammatory markers rule out acute appendicitis? The reliability of biochemical research in diagnosis. ANZ J. Surg. 90 (10), 1970–1974. https://doi.org/10.1111/ans.15559 (2020).

    Article PubMed Google Scholar

  • Dooki, ME et al. Diagnostic accuracy of laboratory markers for the diagnosis of acute appendicitis in children. Vienna. Med. Wochenschr. 172 (13), 303–307. https://doi.org/10.1007/s10354-021-00898-8 (2022).

    Article PubMed Google Scholar



  • Source link

    RELATED ARTICLES
    - Advertisment -
    Google search engine

    Most Popular