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References from Development and validation of an interpretable machine learning model for predicting chemotherapy-induced neutropenia in small cell lung cancer: a web-based clinical decision support tool. Local targets link to admitted publications; unresolved targets remain external evidence.
Small-cell lung cancer
10.1038/s41572-020-00235-0 · 2021 · External reference
Small cell lung cancer, version 2.2022, NCCN clinical practice guidelines in oncology
10.6004/jnccn.2021.0058 · 2021 · External reference
Mortality, morbidity, and cost associated with febrile neutropenia in adult cancer patients
10.1002/cncr.21847 · 2006 · External reference
Myeloid growth factors
10.6004/jnccn.2013.0148 · 2013 · External reference
Trends in G-CSF use in 990 patients after EORTC and ASCO guidelines
10.1016/j.ejca.2010.04.031 · 2010 · External reference
Development and validation of a prediction model for the risk of developing febrile neutropenia in the first cycle of chemotherapy among elderly patients with breast, lung, colorectal, and prostate cancer
10.1007/s00520-010-0821-1 · 2011 · External reference
Development and validation of a risk score for febrile neutropenia after chemotherapy in patients with cancer: The FENCE score
10.1093/jncics/pky053 · 2018 · External reference
Development and validation of a cycle-specific risk score for febrile neutropenia during chemotherapy cycles 2-6 in patients with solid cancers: The (CSR) FENCE score
10.1002/ijc.32249 · 2020 · External reference
Prediction of neutropenic events in chemotherapy patients: A machine learning approach
10.1200/cci.21.00046 · 2021 · External reference
Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data
10.1002/cam4.4465 · 2022 · External reference
Using the Common Terminology Criteria for Adverse Events (CTCAE - Version 5.0) to evaluate the severity of adverse events of anticancer therapies
10.1016/j.ad.2019.05.009 · 2021 · External reference
Multiple imputation with multivariate imputation by chained equation (MICE) package
10.3978/j.issn.2305-5839.2015.12.63 · 2016 · External reference
The lasso method for variable selection in the Cox model
10.1002/(sici)1097-0258(19970228)16:4<385::aid-sim380>3.0.co;2-3 · 1997 · External reference
Feature selection with Boruta package
10.18637/jss.v036.i11 · 2010 · External reference
Comparison of classification success rates of different machine learning algorithms in the diagnosis of breast cancer
10.31557/apjcp.2022.23.10.3287 · 2022 · External reference
A hybrid deep learning and machine learning approach with Mobile-EfficientNet and Grey Wolf Optimizer for lung and colon cancer histopathology classification
10.3390/cancers16223791 · 2024 · External reference
Optuna: A next-generation hyperparameter optimization framework
10.1145/3292500.3330701 · 2019 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · 2020 · External reference
Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: Comparative fNIRS study
10.1007/s13534-023-00291-x · 2023 · External reference
PDPilot: Exploring partial dependence plots through ranking, filtering, and clustering
10.1109/tvcg.2025.3545025 · 2025 · External reference
Chemotherapy-induced neutropenia as a prognostic factor in patients with extensive-stage small cell lung cancer
10.1007/s00228-023-03451-1 · 2023 · External reference
Comparison of irinotecan/platinum versus etoposide/platinum chemotherapy for extensive-stage small cell lung cancer: A meta-analysis
10.1111/ecc.12723 · 2017 · External reference
Risk factors for febrile neutropenia among patients with cancer receiving chemotherapy: A systematic review
10.1016/j.critrevonc.2013.12.006 · 2014 · External reference
2010 update of EORTC guidelines for the use of granulocyte-colony stimulating factor to reduce the incidence of chemotherapy-induced febrile neutropenia in adult patients with lymphoproliferative disorders and solid tumours
10.1016/j.ejca.2010.10.013 · 2011 · External reference
Influence of UGT1A1 polymorphism on etoposide plus platinum-induced neutropenia in Japanese patients with small-cell lung cancer
10.1007/s10147-018-1358-4 · 2019 · External reference
The occurrence and risk factors of chemotherapy-induced neutropenia in patients with breast cancer not receiving primary G-CSF prophylaxis
10.3332/ecancer.2023.1618 · 2023 · External reference
Marrow neutrophil reserve after adjuvant chemotherapy for carcinoma of the breast
10.1002/1097-0142(19820615)49:12<2455::aid-cncr2820491206>3.0.co;2-4 · 1982 · External reference
An interpretable machine learning model based on inflammatory-nutritional biomarkers for predicting metachronous liver metastases after colorectal cancer surgery
10.3390/biomedicines13071706 · 2025 · External reference
CatBoost for big data: An interdisciplinary review
10.1186/s40537-020-00369-8 · 2020 · External reference
Opening the black box of AI-medicine
10.1111/jgh.15384 · 2021 · External reference
Opening the black box: Interpretable machine learning for predictor finding of metabolic syndrome
10.1186/s12902-022-01121-4 · 2022 · External reference
An overview of clinical decision support systems: Benefits, risks, and strategies for success
10.1038/s41746-020-0221-y · 2020 · External reference
