Artificial intelligence in rheumatology research: what is it good for? by Diego Benavent, José Miguel Sequí-Sabater

“…Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. …”
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Evaluating Machine Learning Models for Prostate Cancer Classification Using Gene Expression Profiles from DNA Microarrays by Haddou Bouazza Sara, Haddou Bouazza Jihad

“…Our results demonstrated that the best combination was the Signal to Noise Ratio with Linear Discriminant Analysis, achieving a classification accuracy of 95% using only six genes. …”
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Correlation of Grade of Urothelial Cell Carcinomas and DNA Histogram Features Assessed by Flow Cytometry and Automated Image Cytometry by Marco G. W. Bol, Jan P. A. Baak, Bianca v. Diermen, E. A. M. Janssen, Susanne B. K. Buhr-Wildhagen, Kjell-Henning Kjellevold

“…The DNA ICM results have stronger discriminative power than DNA FCM for grade in TaT1 UCs. …”
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Risk prediction model of cognitive performance in older people with cardiovascular diseases: a study of the National Health and Nutrition Examination Survey database by Hui Wang, Sensen Wu, Dikang Pan, Yachan Ning, Cong Wang, Jianming Guo, Yongquan Gu

“…The effectiveness of the resultant nomogram was evaluated by assessing its discriminative capability, calibration, and conducting decision curve analysis (DCA). …”
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Adaptation and Validation of Standardized Aphasia Tests in Different Languages: Lessons from the Boston Diagnostic Aphasia Examination–Short Form in Greek by Kyrana Tsapkini, Christina Helen Vlahou, Costantin Potagas

“…The aim of the current study was to adapt the Boston Diagnostic Aphasia Examination – Short Form (BDAE-SF) [1] to the Greek language and culture, determine the influence of demographic variables on performance and in particular the effects of age and education, develop normative data, and examine the discriminative validity of the test for acute stroke patients. …”
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Determination of Geographical Origin of Wuchang Rice with the Geographical Indicator by Multielement Analysis by Lili Qian, Feng Zuo, Hongyan Liu, Caidong Zhang, Xiaoxing Chi, Dongjie Zhang

“…The analysis of variance and linear discriminant analysis were performed to analyze the variation among regions and rice genotypes and classify the geographical origins of rice. …”
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Classification of Different Blueberry Cultivars by Analysis of Physical Factors, Chemical and Nutritional Ingredients, and Antioxidant Capacities by Juan Song, Qiong Shi, Si-Min Yan, Hai-Yan Fu, Si-Zhan Wu, Lu Xu

“…A supervised classification method, partial least squares discriminant analysis (PLSDA), was combined with the global particle swarm optimization algorithm (PSO) and two multiclass strategies, one-versus-rest (OVR) and one-versus-one (OVO), to select discriminative quality factors and develop classification models of the 12 cultivars. …”
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High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds by S. Bovo, M. Bolner, G. Schiavo, G. Galimberti, F. Bertolini, S. Dall’Olio, A. Ribani, P. Zambonelli, M. Gallo, L. Fontanesi

“…After thoroughly evaluating the impact of random components on missing value imputation, 100 discriminant metabolites were selected by Boruta and 17 discriminant metabolites (all included within the previous list) were identified with sPLS-DA. …”
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QuickStroop for screening for minimal hepatic encephalopathy in patients with cirrhosis by Christian Labenz, Simon J. Gairing, Leonard Kaps, Alena F. Ehrenbauer, Eva M. Schleicher, Sophie Mengel, Julius F.M. Egge, Maria M. Gabriel, Peter R. Galle, Heiner Wedemeyer, Alexander Zipprich, Cristina Ripoll, Robin Greinert, Benjamin Maasoumy

“…QuickStroop had a better discriminative ability in patients below than above the age of 60 years. …”
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Quality identification of Amomi fructus using E-nose, HS-GC-IMS, and intelligent data fusion methods by Pan-Pan Zhang, Xin-Jing Gui, Xin-Jing Gui, Xin-Jing Gui, Xue-Hua Fan, Han-Li, Hai-Yang Li, Xiao-Peng Li, Feng-Yu Dong, Yan-Li Wang, Jing-Yao, Jing-Yao, Jun-Han Shi, Jun-Han Shi, Rui-Xin Liu, Rui-Xin Liu, Rui-Xin Liu, Rui-Xin Liu, Rui-Xin Liu

“…Based on the E-nose sensor response value and the peak volumes of the 111 VOCs, the unguided Principal Component Analysis (PCA), guided Principal Component Analysis-Discriminant Analysis (PCA-DA), and Partial Least Squares-Discriminant Analysis (PLS-DA) models were established to differentiate AF by authenticity, origin, and provenance. …”
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Model for predicting metabolic activity in athletes based on biochemical blood test analysis by Victoria A. Zaborova, Evgenii I. Balakin, Ksenia A. Yurku, Olga E. Aprishko, Vasiliy I. Pustovoyt

“…To determine the predominance of anabolic or catabolic processes, equations were derived from a linear discriminant function. The discriminant function of predicting metabolic processes in athletes has a high information capacity (92.1%), as confirmed by the biochemical results of neuroendocrine system activity, which characterized the body's stage of adaptive regulatory mechanisms in response to stress factors. …”
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The Perioperative NonaGEnaRIan And cenTenarian suRgICal (GERIATRIC) Risk Stratification Tool by Laurence Weinberg, PhD, Dong Kyu Lee, PhD, Luke Fletcher, MD, Bobby Ou Yang, MD, Jadon Karp, MD, Anoop N Koshy, PhD, Ranjan Guha, FANZCA, Hugh Slifirski, MD, Michael R D’Silva, MD, Rinaldo Bellomo, PhD, Leonid Churilov, PhD

“…The GERIATRIC risk tool had good discriminative accuracy for any postoperative complication [area under the receiver operating characteristic curves (AUROC), 0.857; 95% CI = 0.824–0.890] and any severe postoperative complication (AUROC, 0.833; 95% CI = 0.793–0.874), and fair discriminative accuracy for in-hospital mortality (AUROC, 0.780; 95% CI = 0.668–0.893). …”
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A Combination of Cell Findings Improves the Accuracy of Voided Urine Cytology by Sachiko Iwai, Mitsuru Kinjo

“…Using the cell findings extracted by the exact tests, discriminant analysis was performed to determine the valid cell findings for HGUC diagnosis. …”
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Adversarial example generation method for SAR images based on mask extraction by ZHANG Jianwu, NAI Hao, LI Jie, QIAN Jianhua, FANG Yinfeng

“…Secondly, the improved relativistic average GAN (RaGAN) loss was introduced into AdvGAN, and the relative mean discriminator was used to make the discriminator rely on both real data and generated data during training, which improved the stability of training and the attack effect. …”
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Graph Convolutional Network for Word Sense Disambiguation by Chun-Xiang Zhang, Rui Liu, Xue-Yao Gao, Bo Yu

“…Word, part of speech, and semantic category are extracted from contexts of the ambiguous word as discriminative features. Discriminative features and sentence containing the ambiguous word are used as nodes to construct the WSD graph. …”
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Latitude-Time Total Electron Content Anomalies as Precursors to Japan's Large Earthquakes Associated with Principal Component Analysis by Jyh-Woei Lin

“…According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 18 earthquakes occurred. …”
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Multifault Diagnosis of Rolling Element Bearings Using a Wavelet Kurtogram and Vector Median-Based Feature Analysis by Phuong H. Nguyen, Jong-Myon Kim

“…Then, an enhanced feature analysis approach based on the linear discriminant analysis (LDA) technique is used to select the most discriminant bearing fault features from the original feature set. …”
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A Novel Approach to Word Sense Disambiguation Based on Topical and Semantic Association by Xin Wang, Wanli Zuo, Ying Wang

“…We firstly extract topic discriminative terms from document and construct topical graph based on topic span intervals to implement topic identification. …”
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Consensus for the Development of a New Early Warning Score for Predicting Patients’ Clinical Deterioration in Angola: A Delphi Study by Esmael Tomás, Ana Escoval, Maria Lina Antunes

“…Further study must be conducted to test and validate the impact of the newly suggested vital parameters on the discriminant and predictive capability of a new aggregated model specifically adjusted to the Angolan healthcare setting.…”
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Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning by Jiayu Liu, Xingju Wang, Yanting Li, Xuejian Kang, Lu Gao

“…The analysis results show that the model proposed in this paper has a good performance on the evaluation and prediction of the traffic state of the highway network, which is consistent with the discriminant result using the real data.…”
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