ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the performance of clinical prediction models

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Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis

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A deep learning hybrid ensemble fusion for chest radiograph classification

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Spatial mapping Zataria multiflora using different machine-learning algorithms

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Predicting retail customers' distress in the finance industry: An early warning system approach

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Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?

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Deep Sparse Topology Network for Robust Bearing Fault Diagnosis by Maximizing Prior Knowledge Functions

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Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L.

Hojati, M., Naderi, R., Edalat, M., & Pourghasemi, H. R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322442 · Cited by 7 · see citing works

Identification of Rainfall Thresholds Likely to Trigger Flood Damages across a Mediterranean Region, Based on Insurance Data and Rainfall Observations

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Platelets lipidomics study of blood stasis rat model by using liquid chromatography‐tandem mass spectrometry

Sun, M., Li, Y., Hu, G., Yang, H., Zhang, G., Miao, L., Zhang, Y., Li, L., & Liu, J. (2022) · Journal of Separation Science · doi:10.1002/jssc.202200611 · Cited by 7 · see citing works

Prediction begins with diagnosis: Estimating seizure recurrence risk in the First Seizure Clinic

Ooi, S., Tailby, C., Nagino, N., Carney, P. W., Jackson, G. D., & Vaughan, D. N. (2024) · Seizure · doi:10.1016/j.seizure.2024.09.013 · Cited by 6 · see citing works

ROC curves for clinical prediction models part 3. The ROC plot: a picture that needs a 1000 words

CALSTER, B. V., Wynants, L., Collins, P. G. S., Verbakel, J. Y. J., & Steyerberg, E. W. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.05.037 · Cited by 6 · see citing works

Metabolic score for visceral fat as a superior predictor of frailty in overweight and obese adults: a cross-sectional NHANES analysis (2007–2020)

Zhan, Y., Ripley-Gonzalez, J. W., Liu, Y., Gao, Y., Tang, J., & Gao, S. (2025) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-025-02016-9 · Cited by 5 · see citing works

Guidelines for setting cut-off scores in AUC (AUC-GUIDE): balancing sensitivity, specificity, and purpose

Bujang, M. A. (2026) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2026.1808675 · Cited by 7 · see citing works

Establishment and validation of a nomogram model for riskprediction of hepatic encephalopathy: a retrospective analysis

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Aberrant functional connectivity patterns in the pregenual anterior cingulate cortex and anterior midcingulate cortex of patients with irritable bowel syndrome accompanied by depressive symptoms

Tang, R., Jin, Y., Xu, K., Wu, L., Chen, X., Guo, Y., Li, G., & Li, J. (2025) · Brain Imaging and Behavior · doi:10.1007/s11682-024-00964-w · Cited by 5 · see citing works

Pilot Study by Liquid Biopsy in Gastrointestinal Stromal Tumors: Analysis of PDGFRA D842V Mutation and Hypermethylation of SEPT9 Presence by Digital Droplet PCR

Olivera-Salazar, R., Cabañas, G. S., Vega-Clemente, L., Alonso-Martín, D., Megías, V. M. C., Volward, P., García‐Olmo, D., & García‐Arranz, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126783 · Cited by 5 · see citing works

Differentiating COVID-19 and dengue from other febrile illnesses in co-epidemics: Development and internal validation of COVIDENGUE scores

Gérardin, P., Maillard, O., Bruneau, L., Accot, F., Legrand, F., Poubeau, P., MANAQUIN, R., Andry, F., Bertolotti, A., & Levin, C. (2021) · Travel Medicine and Infectious Disease · doi:10.1016/j.tmaid.2021.102232 · Cited by 5 · see citing works

Discovery of octahydropyrrolo [3,2‐b] pyridin derivative as a highly selective Type I inhibitor of FGFR3 over VEGFR2 by high‐throughput virtual screening

Wang, X., Ye, C., Li, E., Xu, L., Lin, W., & Chen, G. (2022) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30357 · Cited by 5 · see citing works

In silico Exploration of Inhibition Mechanism of Lianhua Qingwen Formula (LQF) Interaction on SARS-CoV-2 Mpro

Xue, X., Wang, X., Ye, C., Gao, M., Peng, L., Yu, K., & Chen, G. (2024) · Chemical Research in Chinese Universities · doi:10.1007/s40242-024-4150-1 · Cited by 4 · see citing works

ADNEX-AI: automated extraction of ultrasound predictors for interpretable ovarian cancer risk stratification

Geysels, A., Garofalo, G., Timmerman, S., Ceusters, J., Buonomo, F., Fischerová, D., Testa, A. C., Moro, F., Sladkevicius, P., Jokubkiene, L., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01215-x · Cited by 4 · see citing works

Effects of CAD/CAM restorative materials and thickness on the depth of cure of various light-cured resin composite materials

Fasham, T., Attal, J., Gouze, H., Abdel-Gawad, S., Dursun, E., & François, P. (2024) · Dental Materials Journal · doi:10.4012/dmj.2024-053 · Cited by 4 · see citing works

Hybrid Sensing for Fatigue Detection Using Wearables and RF

Taylor, W., Hill, D., Adam, R., Cooper, J. M., Abbasi, Q. H., & Imran, M. A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3404637 · Cited by 4 · see citing works

Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics

Ahmed, N. N., Bhat, T. K., & Powar, O. S. (2024) · Systems Science & Control Engineering · doi:10.1080/21642583.2024.2427028 · Cited by 4 · see citing works

Detection of sFas, sCD137, and IL-27 Cytokines as Potential Biomarkers for Hepatocellular Carcinoma Diagnosis

Do, X.-H., Le, M. T., Nguyen, T. H., Le, T. T., Nguyen, X., Mai, T. B., Hoang, T. M. N., & Than, U. T. T. (2023) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s409649 · Cited by 4 · see citing works

Predictive nomograms for risk and prognostic factors in metastatic bladder cancer: a population-based study

Shi, S., Peng, G., Luo, L., & Li, D. (2023) · Translational Cancer Research · doi:10.21037/tcr-23-1229 · Cited by 4 · see citing works

Medical Relevance, State-of-the-Art and Perspectives of “Sweet Metacode” in Liquid Biopsy Approaches

Pinkeová, A., Košútová, N., Jáné, E., Lorencová, L., Bertóková, A., Bertók, T., & Tkáč, J. (2024) · Diagnostics · doi:10.3390/diagnostics14070713 · Cited by 4 · see citing works

Preoperatively predicting failure to achieve the minimum clinically important difference and substantial clinical benefit for total knee arthroplasty patients using machine learning

Park, J., Miley, E. N., Zhong, X., & Gray, C. F. (2025) · Knee Surgery and Related Research · doi:10.1186/s43019-025-00289-y · Cited by 3 · see citing works

Development, validation and clinical utility of prognostic models for patients with traumatic brain injury in an Indian setting using machine learning and traditional approaches: a study protocol

Kamal, V. K., Agrawal, D. K., Kumar, A., Radhakrishnan, R., Ponnaiah, M., Ramaiya, D., Thiruvalluvan, A., & Sivaram, A. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-096275 · Cited by 3 · see citing works

Literature‐informed ensemble machine learning for three‐year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model

Tourkmani, A. M., Alharbi, T., Al-Ghamdi, A., Youzghadli, I. M., Alosaimi, F. S., & Azzam, A. Y. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70385 · Cited by 3 · see citing works

Risk factors for preoperative nasal carriage of methicillin-resistant bacteria in total hip and knee arthroplasty patients

Tateiwa, T., Ishida, T., Takahashi, Y., Shinozaki, T., Masaoka, T., Shishido, T., Nishida, J., & Yamamoto, K. (2021) · Journal of Orthopaedic Science · doi:10.1016/j.jos.2021.04.014 · Cited by 3 · see citing works

Reliability, Validity, Modification and Expansion of the Chinese Version of the Disease-Specific Anxiety Questionnaire for Chronic Obstructive Pulmonary Disease

Miao, X., Han, Y., Wu, Z., Jin, X., Niu, M., Zhao, Q., & Lü, X. (2024) · International Journal of COPD · doi:10.2147/copd.s455171 · Cited by 3 · see citing works

Presenting predictions and performance of probabilistic models for clinical decision support in trauma care

Alptekin, C., Wohlgemut, J., Perkins, Z., Marsh, W., Tai, N., & Yet, B. (2024) · Int. J. Medical Informatics · doi:10.1016/j.ijmedinf.2024.105702 · Cited by 2 · see citing works

Combining artificial neural networks and a marginal structural model to predict the progression from depression to Alzheimer's disease

Orimaye, S. O., & Schmidtke, K. A. (2024) · Frontiers in Dementia · doi:10.3389/frdem.2024.1362230 · Cited by 3 · see citing works

Investigating soil erosion vulnerable zones based on clustered geoinformatics approach: a case study of Tyume River Catchment, Eastern Cape, South Africa

Mbatyoti, S., Sumner, P. D., Kalumba, A. M., Owolabi, S. T., & Belle, J. A. (2024) · Cuaternario y geomorfología: Revista de la Sociedad Española de Geomorfología y Asociación Española para el Estudio del Cuaternario · doi:10.17735/cyg.v38i3-4.101016 · Cited by 3 · see citing works

Risk scores in anaesthesia: the future is hard to predict

Drayton, D. J., Ayres, M., Relton, S. D., Sperrin, M., & Hall, M. (2022) · BJA Open · doi:10.1016/j.bjao.2022.100027 · Cited by 3 · see citing works

Integrated CT-derived fractional flow reserve and perivascular fat attenuation index: a multimodal approach to predict in-stent restenosis

Zhao, W., Huang, L., Zhu, M., Li, X., Liu, X., Zhang, W., Shao, C., & Li, Y. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1703044 · Cited by 2 · see citing works

Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality

Lu, A., Srikanth, V. K., Westworth, S. E., Baey, Y.-G., Moran, C., Beare, R. J., Siostrom, K., Andrew, N. E., & Collyer, T. A. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag048 · Cited by 2 · see citing works

Development and Validation of Risk Prediction Model for New-Onset Diabetes After Percutaneous Coronary Intervention (NODAP): A Study Protocol for a Retrospective, Multicenter Analysis

Li, Y., Cui, J., Liu, Y., Chen, K., Huang, L., & Liu, Y. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.748256 · Cited by 2 · see citing works

Early Risk Pregnancy Prediction Based on Machine Learning Built on Intelligent Application Using Primary Health Care Cohort Data

Miranda, E., Kumbangsila, M., Aryuni, M., Richard, R., Zakiyyah, A. Y., & Sano, A. V. D. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0248-4_11 · Cited by 2 · see citing works

Developing a prediction model to identify people with severe mental illness without regular contact to their GP - a study based on data from the Danish national registers

Naesager, A. H. D., Damgaard, S. N., Rozing, M. P., Siersma, V., Møller, A., & Jensen, K. T. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-05743-x · Cited by 2 · see citing works

Machine Learning-Based Gene Expression Analysis to Identify Prognostic Biomarkers in Upper Tract Urothelial Carcinoma

Padullés, B., López-Aladid, R., Ingelmo‐Torres, M., Roldán, F. L., Martínez, C., Juez, J., Izquierdo, L., Mengual, L., & Alcaraz, A. (2025) · Cancers · doi:10.3390/cancers17162619 · Cited by 2 · see citing works

ROC Analyses Based on Measuring Evidence Using the Relative Belief Ratio

Al‐Labadi, L., Evans, M. J., & Liang, Q. (2022) · Entropy · doi:10.3390/e24121710 · Cited by 2 · see citing works

Predicting Retail Customers' Distress: Early Warning Systems and Machine Learning ApplicationsPreprint

Beltman, J., Osterrieder, J. R., & Machado, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4730470 · Cited by 2 · see citing works

Development and validation of a clinical prediction model for prognostic factors in patients with primary pontine hemorrhage

Hu, A., Qin, H., Wu, S., Zhao, X., Li, Y. Q., Chen, F., & Liu, T. (2024) · Brazilian Journal of Medical and Biological Research · doi:10.1590/1414-431x2024e13359 · Cited by 1 · see citing works

Prediction of Range of Motion in Patients After Total Knee Arthroplasty by Shear Wave Elastography

Kim, M.-W., & Lee, D.-H. (2025) · Bioengineering · doi:10.3390/bioengineering12101009 · Cited by 1 · see citing works

Modeling habitat suitability for saffron (Crocus sativus L.) using AI-based approaches to support sustainable industrial cultivation

Sarvestani, G. S., Edalat, M., Dastres, E., Naderi, R., & Pourghasemi, H. R. (2025) · Industrial Crops and Products · doi:10.1016/j.indcrop.2025.121974 · Cited by 1 · see citing works

Secondary Prevention of AFAIS: Deploying Traditional Regression, Machine Learning, and Deep Learning Models to Validate and Update CHA2DS2-VASc for 90-Day Recurrence

Simon, J. E., Kraiński, Ł., Karlinski, M. A., & Niewada, M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14207327 · Cited by 1 · see citing works

Data enhancement to improve CNN accuracy for early detection of colorectal cancer

Athiyah, U., Rustad, S., Soeleman, M. A., Muljono, M., Akrom, M., & Konoralma, R. A. (2025) · Array · doi:10.1016/j.array.2025.100581 · Cited by 1 · see citing works

Enhancing happiness and life satisfaction in university students: analysis with a machine learning approach

Long, Q., Wang, Y., Axelin, A., Zheng, F., Cao, Z., Li, X., & Guo, J. (2025) · BMC Psychology · doi:10.1186/s40359-025-03809-3 · Cited by 1 · see citing works

Clinical Factors Associated With Current Relevance in Allergic Contact Dermatitis: Development of Predictive Models Based on Data From the Spanish Contact Dermatitis Register ( REIDAC )

Pesqué, D., de Frutos, F. J. O., Navarro‐Triviño, F. J., Sanz‐Sánchez, T., Zaragoza‐Ninet, V., Guijarro, S. C., Miquel‐Miquel, J., Salvador, J. F. S., González‐Pérez, R., González, I. R., et al. (2026) · Contact Dermatitis · doi:10.1111/cod.70089 · Cited by 1 · see citing works

Depth-stratified oxygen stress index for quantifying vertical hypoxia and enabling precise management in stratified reservoirs

Arzhangi, A., Partani, S., & Mehrjerdi, L. F. (2026) · Environmental Advances · doi:10.1016/j.envadv.2026.100694 · Cited by 1 · see citing works

Loss function influence on hyperparameter optimization for observational healthcare prediction models

Vereijken, F., Reps, J., Rijnbeek, P. R., & Williams, R. D. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag075 · Cited by 1 · see citing works

Differentiated pathways of heatwave adaptation: Urban–Rural contrasts in the role of regional income level and energy consumption on adaptive behavior

Zhang, H., Shi, Q., Xiao, C., Wang, J., & (21935), N. W. (2026) · Habitat International · doi:10.1016/j.habitatint.2026.103865 · Cited by 1 · see citing works

Exploring the Impact of Hyperparameter Tuning on the Accuracy of Machine Learning-based Flood Susceptibility Mapping

Razavi-Termeh, S. V., Sadeghi-Niaraki, A., Pourzangbar, A., Kişi, Ö., Hussain, J., & Choi, S.-M. (2026) · Water Resources Management · doi:10.1007/s11269-026-04827-5 · Cited by 1 · see citing works

DATA MINING APPLICATION FOR FINANCIAL DECISION OPTIMIZATION AT RISK

KOÇOĞLU, E., & Ersöz, F. (2021) · International Journal of 3D Printing Technologies and Digital Industry · doi:10.46519/ij3dptdi.950062 · Cited by 1 · see citing works

The correlation between the inner canthal distance and maxillary mesiodens in children

Tadano, M., Matsunaga, Y., Saito, K., Suzuki, Y., Nakamura, T., Hoshikawa, S., Chiba, M., Hino, R., Maruya, Y., Fukumoto, E., et al. (2023) · Pediatric Dental Journal · doi:10.1016/j.pdj.2023.04.001 · Cited by 1 · see citing works

Construction and validation of a postoperative hypothermia prediction model in elderly patients undergoing colorectal surgery

Wang, Y., Yang, N. Y., Zhang, J., Han, W.-Y., & Li, X. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32391 · Cited by 1 · see citing works

Human activity recognition based on machine learning and FMCW radar

Faraj, A. A., Al-Nakkash, A. H., & Wadday, A. G. (2024) · AIP conference proceedings · doi:10.1063/5.0236567 · Cited by 1 · see citing works

LCLN-CA: A Survival Regression Analysis-Based Prediction Method for Catechin Content in Yunnan Sun-Dried Tea

Li, H., Wang, Q., Wang, H., Li, L., Wang, X., Wu, T., Wang, C., Qian, Y., Wang, X., Xia, Y., et al. (2024) · Horticulturae · doi:10.3390/horticulturae10121321 · Cited by 1 · see citing works

PIWI-interacting RNA biomarkers in gastrointestinal disease

Huang, X.-Y., Chen, S., Wang, Z., Lu, Y.-S., Liu, C.-T., & Chen, S.-Z. (2025) · Clinica Chimica Acta · doi:10.1016/j.cca.2025.120182 · Cited by 1 · see citing works

Machine Learning Techniques in Bipolar Disorder

Rebouças, D. B., Barreto, P., Noronha, L. T., Roza, T. H., & Passos, I. C. (2025) · Springer eBooks · doi:10.1007/978-3-031-85519-1_40 · Cited by 2 · see citing works

Predicting mortality by comorbidity for patients with hip arthroplasty: Prospective observational register studies of a nationwide Swedish cohort

Bülow, E. (2020) · Gothenburg University Publications Electronic Archive (Gothenburg University) · Cited by 1 · see citing works

Bi-dimensional Health Space Mapping: Machine Learning Analysis of Health Dynamics in Korean and Dutch CohortsPreprint

Kim, Y. J., van den Broek, T., Brouwer-Brolsma, E. M., Feskens, E. J. M., Bouwman, J., & Kwon, O. (2025) · Research Square · doi:10.21203/rs.3.rs-6960301/v1 · Cited by 1 · see citing works

Developing, validating, and evaluating clinical prediction models in breast and prostate cancer

Hueting, T. A. (2022) · doi:10.3990/1.9789036553940

Predicting hospital readmission risk from Electronic Health Records data for patients with Multiple Chronic Conditions: a validation study (Preprint)Preprint

Rafiq, M., Mazzocato, P., Guttmann, C., Spaak, J., & Savage, C. (2022) · doi:10.2196/preprints.39847

ROC curves for clinical prediction models: from waste of ink towards useful insight?

Verbakel, J. Y. J., Steyerberg, E. W., Uno, H., Cock, B. D., Wynants, L., Collins, P. G. S., & CALSTER, B. V. (2020) · Journal of Clinical Epidemiology · Cited by 3 · see citing works

ROC curves for clinical prediction models part 3. The ROC plot: a picture that needs a 1000 words

CALSTER, B. V., Wynants, L., Collins, P. G. S., Verbakel, J. Y. J., & Steyerberg, E. W. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.05.037 · Cited by 6 · see citing works

Reflection on modern methods: Revisiting the area under the ROC Curve

Janssens, A. C. J. W., & Martens, F. K. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz274 · Cited by 328 · see citing works

ROC curves for clinical prediction models part 4. Selection of the risk threshold—once chosen, always the same?

Janssens, A. C. J. W. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.05.038 · Cited by 1 · see citing works

A more intuitive interpretation of the area under the ROC curvePreprint

Janssens, A. C. J. W. (2017) · doi:10.7287/peerj.preprints.3468