Passos para implantação de sistemas de previsão de demanda: técnicas e estudo de caso by Fernando R. Pellegrini, Flávio S. Fogliatto

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Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The AUC metric was employed to identify the optimal predictive model, whereas SHAP was utilized to achieve interpretability of the model’s predictive decisions. …”
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Deep learning approach with ConvNeXt-SE-attn model for in vitro oral squamous cell carcinoma and chemotherapy analysis by Abhay Nath, Om Roy, Priyanka Silveri, Sanskruti Patel

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Initial Establishment of Warning Model for Epidemic Intensity of Norovirus GII Associated with Acute Gastroenteritis in Beijing Based on Synthetic Index Method by Taoli Han, Yan Gao, Shiyao Zhang, Yang Jiao, Jianhong Zhao, Jiaxin Zhao, Yujie Liu, Kuankuan Liu, Pan Lu, Ru Fan, Yuqi Zhang, Xingmei Ren, Mengnan Wang, Zhiyong Gao, Wenjing Li, Beibei Li, Tongyue Su, Lingli Sun

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Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric Malperfusion Based on Initial Laboratory Results by Zhechuan Jin, Jiale Dong, Jian Yang, Chengxiang Li, Zequan Li, Zhaofei Ye, Yuyu Li, Ping Li, Yulin Li, Zhili Ji

“…This model can serve as a basis for making decisions in the treatment and diagnosis of MMP.…”
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Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort s... by Jia-Wei Feng, Lu Zhang, Yu-Xin Yang, Rong-Jie Qin, Shui-Qing Liu, An-Cheng Qin, Yong Jiang

“…Our methodology employed gradient boosting machine for feature selection and random forest for classification, with model interpretability provided through SHapley Additive exPlanations (SHAP) analysis. …”
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Development and validation of a prediction model for the prolonged length of stay in Chinese patients with lower extremity atherosclerotic disease: a retrospective study by Jian Zhang, Yu Yang, Xue Wang, Shuang Zang

“…We selected nine variables and created the prediction model using the least absolute shrinkage and selection operator (LASSO) regression model after dividing the dataset into training and test sets in a 7:3 ratio. …”
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Evaluation of prognostic models to improve prediction of metastasis in patients following potentially curative treatment for primary colorectal cancer: the PROSPECT trial by Vicky Goh, Susan Mallett, Manuel Rodriguez-Justo, Victor Boulter, Rob Glynne-Jones, Saif Khan, Sarah Lessels, Dominic Patel, Davide Prezzi, Stuart Taylor, Steve Halligan

“…We estimated a sample size of 320 patients with 80 events (i.e. metastasis) would have 80% power to detect a 15% difference in correct risk classification by the model, allowing for loss to follow-up. …”
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An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques by William Hoyos, Kenia Hoyos, Rander Ruiz, Jose Aguilar

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DLProv: a suite of provenance services for deep learning workflow analyses by Débora Pina, Liliane Kunstmann, Adriane Chapman, Daniel de Oliveira, Marta Mattoso

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Evaluating Ecological Vulnerability and Its Driving Mechanisms in the Dongting Lake Region from a Multi-Method Integrated Perspective: Based on Geodetector and Explainable Machine... by Fuchao Li, Tian Nan, Huang Zhang, Kun Luo, Kui Xiang, Yi Peng

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A Novel Machine Learning-based Diagnostic Algorithm for Detection of Onychomycosis through Nail Appearance by Serkan Düzayak, Muhammed Kürşad Uçar

“…The best features were selected through feature selection algorithms in the next step to increase the performance and reduce the number of features, and models were created by algorithm classification. The average performance values of all proposed models, accuracy, sensitivity, and specificity, are 89.65, 0.9, and 0.89, respectively. …”
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Comparison of a machine learning model with a conventional rule-based selective dry cow therapy algorithm for detection of intramammary infections by S.M. Rowe, E. Zhang, S.M. Godden, A.K. Vasquez, D.V. Nydam

“…Area under the curve (AUC) and Youden's index were used to compare models, in addition to binary classification metrics, including sensitivity, specificity, and predictive values. …”
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Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions. by Yiye Zhang, Yufang Huang, Anthony Rosen, Lynn G Jiang, Matthew McCarty, Arindam RoyChoudhury, Jin Ho Han, Adam Wright, Jessica S Ancker, Peter Ad Steel

“…To support clinical actionability, clinician investigators conducted manual chart reviews of the cases identified by the model. Chart reviews categorized predicted cases across index ED discharge diagnosis and RVA root cause classifications. …”
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Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals by Prabal Datta Barua, Makiko Kobayashi, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Jose Kunnel Paul, Thomas Iype, U. R. Acharya

“…The proposed model achieved classification accuracies of 99.36% and 98.37% for sleep stages 2 and 3, respectively. …”
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Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design by Fang-Fang Chua, Tek-Yong Lim, Bushra Tajuddin, Amarilis Putri Yanuarifiani

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Breast Cancer Detection Analysis Using Different Machine Learning Techniques: South Iraq Case Study by Salma Abdulbaki Mahmood, Myssar Jabbar Hammood Al-Battbootti, Saad Shaheen Hamadi, Iuliana Marin, Costin-Anton Boiangiu, Nicolae Goga

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Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features by Hongwei Zhang, Kexin Wang, Shurong Liu, Guowei Chen, Yong Jiang, Yingchao Wu, Xiaocong Pang, Xiaoying Wang, Junling Zhang, Xin Wang

“…A combined model was developed by integrating the clinical, CT, and radiomics models, with positivity defined as all three models being positive at a 90% sensitivity threshold. …”
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Predicting aflatoxin contamination in white and yellow maize using Vis/NIR spectroscopy combined with PCA-LDA and PLSR models through aquaphotomics approaches by William Appaw, John-Lewis Zinia Zaukuu, Balkis Aouadi, Eric Tetteh Mensah, Ibok Nsa Oduro, Zoltan Kovacs

“…Researchers are therefore exploring cheaper, faster but reliable alternatives such as near-infrared spectroscopy (NIRS), which does not destroy the integrity of the food but rather, supports possible on-spot data driven decision making. This study aimed to develop models, optimized with pre-processing techniques and wavelength ranges to classify and predict 0, 3, 5, 10, 20, 30 and 50 ng/g aflatoxin in three major datasets (naturally contaminated white, spiked white maize and spiked yellow maize). …”
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Preventing reduction loss following proximal humerus fractures after locking compression plate fixation: a new clinical prediction model by Yiyuan Yang, Zhuo Chen, Yuanyu Hu, Fang Zhou, Zhishan Zhang, Yang Lv, Hongquan Ji, Yan Guo, Guojin Hou, Yun Tian

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