Predicting complications of major head and neck oncological surgery: an evaluation of the ACS NSQIP surgical risk calculator by Peter S. Vosler, Mario Orsini, Danny J. Enepekides, Kevin M. Higgins

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Machine learning-based prognostic model for patients with anaplastic thyroid carcinoma by Yihan Sun, Da Lin, Xiangyang Deng, Yinlong Zhang

“…Results An RSF model and a traditional Cox model were successfully developed in training set. The Brier score for the RSF model was 0.055, which is lower than the Cox model’s score of 0.063, indicating better performance since a lower Brier score signifies superior model accuracy. …”
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Extensive benchmarking of a method that estimates external model performance from limited statistical characteristics by Tal El-Hay, Jenna M. Reps, Chen Yanover

“…Results showed accurate estimations for all metrics: 95th error percentiles for the area under the receiver operating characteristics (discrimination), calibration-in-the-large (calibration), Brier and scaled Brier scores (overall accuracy) of 0.03, 0.08, 0.0002, and 0.07, respectively. …”
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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…Results: Random forest (AUC = 0.98, accuracy = 0.95, Brier score = 0.06, and F1 = 0.94), extreme gradient boost (XGB) (AUC = 0.96, accuracy = 0.91, Brier score = 0.08, F1 = 0.90) and support vector machine (SVM) (AUC = 0.93, accuracy = 0.93, Brier score = 0.07, F1 = 0.92) were the best performing models (base learners). …”
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On the estimation of inverse-probability-of-censoring weights for the evaluation of survival prediction error. by Thomas Prince, Andrea Bommert, Jörg Rahnenführer, Matthias Schmid

“…A key requirement of the IPCW Brier score is the estimation of the censoring distribution, which is needed to compute the weights. …”
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How do palliative care doctors recognise imminently dying patients? A judgement analysis by Nicola White, Priscilla Harries, Adam JL Harris, Victoria Vickerstaff, Philip Lodge, Catherine McGowan, Ollie Minton, Christopher Tomlinson, Adrian Tookman, Fiona Reid, Patrick Stone

“…The mean Brier score of the ‘experts’ was 0.184 (95% CI 0.176 to 0.192). …”
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Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning by Min Liang, Zhiwen Zhang, Langming Wu, Mafeng Chen, Shifan Tan, Jian Huang

“…In comparison to other models, the XGBoost model excelled in both training and validation phases, as demonstrated by substantial differences in AUC, DCA, calibration, and Brier score. This model has been made accessible via a web-based platform. …”
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Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation by Ling Hao, Yang Chen, Xuejiao Su, Buyun Ma

“…The model’s accuracy, Brier score, and log loss were 0.796, 0.197, and 0.599, respectively. …”
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Development and Validation of the RCOS Prognostic Index: A Bedside Multivariable Logistic Regression Model to Predict Hypoxaemia or Death in Patients with SARS-CoV-2 Infection by Gerardo Alvarez-Uria, Sumanth Gandra, Venkata R. Gurram, Raghu P. Reddy, Manoranjan Midde, Praveen Kumar, Ketty E Arce

“…In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892–0.922), and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where the admission of patients was decided according to the prognostic index. …”
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External validation of the prediction model of intradialytic hypotension: a multicenter prospective cohort study by Yuhe Xiang, Guoting Ma, Qin Yang, Min Cao, Wenbin Xu, Lin Li, Qian Yang

“…Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). …”
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Clinical Response Predictive Model for Omalizumab in Moderate-to-Severe Asthma Patients by Ruiqi Qian, Lingyi Yang, Xurui Shen, Cheng Chen, Jianan Huang, Xiuqin Zhang, Cuiping Fu

“…The corrected AUC and Brier scores were 0.872 and 0.111, which showed good discrimination. …”
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Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study by Xiaojuan Xiong, Hong Fu, Bo Xu, Wang Wei, Mi Zhou, Peng Hu, Yunqin Ren, Qingxiang Mao

“…Model performance was assessed with 5-fold cross-validation, bootstrapping, Brier score, and Shapley additive explanation values. …”
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The clinical prediction model to distinguish between colonization and infection by Klebsiella pneumoniae by Xiaoyu Zhang, Xifan Zhang, Deng Zhang, Jing Xu, Jingping Zhang, Xin Zhang

“…The Random Forest model performed best among all indicators (accuracy 0.93, precision 0.98, Brier Score 0.06, recall 0.72, F1 Score 0.83, AUC 0.99). …”
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Charlson comorbidity index has no incremental value for mortality risk prediction in nursing home residents with COVID-19 disease by Anum Zahra, Maarten van Smeden, Petra J. M. Elders, Jan Festen, Jacobijn Gussekloo, Karlijn J. Joling, Anouk van Loon, Kim Luijken, René J. F. Melis, Simon P. Mooijaart, Karel G. M. Moons, Geeske Peeters, Harmke A. Polinder-Bos, Fenne Wouters, Anne de Hond

“…The base model, with age and sex as predictors, had an AUROC of 0.61 (CI: 0.60 to 0.63), a scaled brier score of 0.03 (CI: 0.02 to 0.04), and a calibration slope of 0.97 (CI: 0.83 to 1.13). …”
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Development and validation of a hyperlipidemia risk prediction model for middle-aged and older adult Chinese using 2015 CHARLS data by Li-xiang Zhang, Shan-Bing Hou, Fang-fang Zhao, Ting-ting Wang, Ying Jiang, Xiao-juan Zhou, Jiao-yu Cao

“…The Spiegelhalter’s z-statistic test confirmed that the predicted probabilities from the nomogram model are in good agreement with the observed frequencies of hyperlipidemia (p = 0.560). The Brier score for the nomogram model was 17.1%, which is below the threshold of 25%, indicating good calibration. …”
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Derivation and validation of a clinical predictive model for longer duration diarrhea among pediatric patients in Kenya using machine learning algorithms by Billy Ogwel, Vincent H. Mzazi, Alex O. Awuor, Caleb Okonji, Raphael O. Anyango, Caren Oreso, John B. Ochieng, Stephen Munga, Dilruba Nasrin, Kirkby D. Tickell, Patricia B. Pavlinac, Karen L. Kotloff, Richard Omore

“…Model calibrations were assessed using Brier, Spiegelhalter’s z-test and its accompanying p-value. …”
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Development of a Bayesian Network-based Safety Performance Quantification Model on building construction projects in Korea by Taegeun Song, Kiseok Lee, Yoonseok Shin, Wi Sung Yoo

“…The SPQM also leverages safety inspection documents produced by site supervisors to train Bayesian networks with Python 3.8 and verify network performance through the Brier Score (BS). The BS of the trained model is below 0.25, and the prediction rate is approximately 80%. …”
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Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions by Seyed Mohammad Sina Mirabdolbaghi, Babak Amiri

“…It outperforms other ensemble and ML algorithms like AdaBoost, SVM, and decision tree on over seven evaluation metrics: accuracy, area under the curve (AUC), Kappa, Mathews correlation coefficient (MCC), Brier score, F1 score, and EMPC. In light of the evaluation metrics, our model shows a significant improvement in handling imbalanced datasets in churn prediction. …”
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Use of Nomogram to Predict the Risk of Lymph Node Metastasis among Patients with Cervical Adenocarcinoma by Yongju Tian, Yiping Hao, Qingqing Liu, Ruowen Li, Zhonghao Mao, Nan Jiang, Bingyu Wang, Wenjing Zhang, Xiaofang Zhang, Baoxia Cui

“…The nomogram developed by incorporation of these four predictors performed well in terms of discrimination and calibration capabilities (C−index=0.794; 95% confidence interval (CI), 0.727–0.862; Brier score=0.127). Decision curve analysis demonstrated that the nomogram was clinically effective in the prediction of LNM. …”
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Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies by Alireza Maleki, Mohammad Mahdi Mirza Ali Mohammadi, Shahab Gholizadeh, Behnaz Dalvandi, Mohammad Rahimi, Aidin Tarokhian

“…Model performance was evaluated using sensitivity, specificity, likelihood ratios, predictive values, AUC-ROC, Brier score, and overall accuracy. Feature importance was assessed using SHapley Additive exPlanations (SHAP) values. …”
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