Cited by 823Artificial Intelligence in HealthcareMachine Learning in HealthcareArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 109 of the 111 references it cites were found in a registry or an index.

Predictive data mining in clinical medicine: Current issues and guidelines

Open access

BELLAZZI, R, ZUPAN, B·2008·International Journal of Medical Informatics

DOI 10.1016/j.ijmedinf.2006.11.006

Open this paper in CoChat — free account

Abstract

Background The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as 'data mining' offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help practitioners in the appropriate selection of data mining tools, construction and validation of predictive models, along with the dissemination of predictive models within clinical environments.Purpose The goal of this review is to discuss the extent and role of the research area of predictive data mining and to propose a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine.Methods We review the recent relevant work published in the area of predictive data mining in clinical medicine, highlighting critical issues and summarizing the approaches in a set of learned lessons.Results The paper provides a comprehensive review of the state of the art of predictive data mining in clinical medicine and gives guidelines to carry out data mining studies in this field.Conclusions Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Understanding the main issues underlying these methods and the application of agreed and standardized procedures is mandatory for their deployment and the dissemination of results. Thanks to the integration of molecular and clinical data taking place within genomic medicine, the area has recently not only gained a fresh impulse but also a new set of complex problems it needs to address.

Integrity signals

Registry

Crossref · 10.1016/j.ijmedinf.2006.11.006

Cites

109 of 109 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Applied logistic regression
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Support-Vector Networks
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Statistical Learning Theory
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Untitled work
(1992) · Cited by 23,431
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855

Show all 111 references

Sorted by citations

Items 501–600 of 823

Healthcare Optimization : Current Status and Vitalization Suggestions
Kang, S.-H., Kim, B., Jun, C., Choi, B. K., & Lee, S. (2013) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2013.39.4.313 · Cited by 4
Bayesian nonparametric collaborative topic Poisson factorization for electronic health records-based phenotyping
Lee, W., Lee, Y., Kim, H., & Moon, I. (2016) · International Joint Conference on Artificial Intelligence · Cited by 4
A Case Study on Web-based Analytic Workflow in Big Data Platform
Nguyen, M. C., & Won, H. S. (2016) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2016.0087 · Cited by 4
ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine
Stevens, L. M., Kao, D. P., Hall, J. L., Görg, C., Abdo, K., & Linstead, E. J. (2020) · Applied Sciences · doi:10.3390/app10093309 · Cited by 4
The Personalized Disease Predication Care from Harm using Big Data Analytics in Healthcare
Kumar, J. S. (2016) · Indian Journal of Science and Technology · doi:10.17485/ijst/2016/v9i8/87846 · Cited by 4
Exploring the antecedents of the quality of life of patients with sickle cell disease: using a knowledge discovery and data mining process model-based framework
Mansingh, G., Osei-Bryson, K.-M., & Asnani, M. (2015) · Health Systems · doi:10.1057/hs.2015.3 · Cited by 4
Novel nomogram based on risk factors of chronic obstructive pulmonary disease (COPD) using a naïve Bayesian classifier model
Seo, J.-H., & Lee, J.-Y. (2018) · Journal of the Korean Statistical Society · doi:10.1016/j.jkss.2018.11.006 · Cited by 4
Machine Learning-Based Cognitive Support System for Healthcare
Ramalatha, M., Shivappriya, S. N., & Malarvizhi, K. (2018) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-00865-9_5 · Cited by 5
Partus referral classification using backpropagation neural network
Pertiwi, A. G., Wibawa, A. P., & Pujianto, U. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1193/1/012010 · Cited by 4
Online Variational Learning for Medical Image Data Clustering
Kalra, M., Osadebey, M. E., Bouguila, N., Pedersen, M., & Fan, W. (2019) · Unsupervised and semi-supervised learning · doi:10.1007/978-3-030-23876-6_11 · Cited by 4
Smart Architecture for Diabetic Patients Using Machine Learning
Anishfathima, B., Gautham, P. K., Mahalakshmi, B. K. V. P. S., & Jamadar, S. J. (2021) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs51430.2021.9441985 · Cited by 4
Federated Learning for Data Mining in Healthcare
Sharma, S., Kesarwani, A., Maheshwari, S., & Rai, B. K. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-85559-8_16 · Cited by 4
Using Intelligence Techniques to Predict Postoperative Morbidity of Endovascular Aneurysm Repair
Hsieh, N., Chen, J.-F., Lee, K.-C., & Tsai, H.-C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20039-7_20 · Cited by 4
A distributed architecture for efficient parallelization and computation of knowledge-based temporal abstractions
Shabtai, A., D, Y. S. M., & Elovici, Y. (2011) · Journal of Intelligent Information Systems · doi:10.1007/s10844-011-0190-3 · Cited by 4
Support Vector Machine ensembles using features distribution among subsets for enhancing microarray data classification
Ahmed, E., Gayar, N. E., & El-Azab, I. A. (2010) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2010.5687078 · Cited by 4
Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach
Barbarini, N., Tiengo, A., & Bellazzi, R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023616 · Cited by 4
The Application of Healthcare Information System for Comprehensive Geriatric Assessment
Kuo, N., & Chung, Y.-Y. (2012) · doi:10.6131/misr.201203_17(2).0004 · Cited by 4
Clustering social cues to determine social signals: developing learning algorithms using the "n-most likely states" approach
Best, A., Kapalo, K. A., Warta, S. F., & Fiore, S. M. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2223900 · Cited by 4
Microarray technology reveals potentially novel genes and pathways involved in non-functioning pituitary adenomas
Qiao, X., Wang, H., Wang, X., Zhao, B., & Liu, J. (2016) · Balkan Journal of Medical Genetics · doi:10.1515/bjmg-2016-0030 · Cited by 4
Unified Structured Framework for mHealth Analytics: Building an Open and Collaborative Community
Nguyen, H. D., & Poo, D. C. C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58562-8_34 · Cited by 4
RDE: A novel approach to improve the classification performance and expressivity of KDB
Lou, H., Wang, L., Duan, D., Yang, C., & Mammadov, M. A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199822 · Cited by 4
A Review on Health Data Using Data Mining Techniques
Periasamy, A. R. P., & Mohan, S. (2017) · International Journal of Advanced Research in Computer Science and Software Engineering · doi:10.23956/ijarcsse/v7i3/0136 · Cited by 4
Cerebral and cardiac signal monitoring in fetal sheep with hypoxic-ischemic encephalopathy
Zwanenburg, A. (2015) · doi:10.26481/dis.20151119az · Cited by 4
ANALYSING INTERACTIONS OF RISK FACTORS ACCORDING TO RISK LEVELS FOR HEMODIALYSIS PATIENTS IN TURKEY: A DATA MINING APPLICATION
Altintas, Y. Y., Gökċen, H., Ülgü, M. M., & Demirel, N. (2011) · DergiPark (Istanbul University) · Cited by 3
On the Need of New Approaches for the Novel Problem of Long-Term Prediction over Multi-dimensional Data
Henriques, R., & Antunes, C. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-30454-5_9 · Cited by 3
An Effective Intelligent Model for Medical Diagnosis
El-Rashidy, M. A., Taha, T. E., Ayad, N., & Sroor, H. (2011) · Cited by 3
Integrating Imaging and Clinical Data for Decision Support
Hsu, W. W. Y., Bui, A. A. T., Taira, R. K., & Kangarloo, H. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-314-2.ch002 · Cited by 3
Ontology Based Integration of Residential Care of the Elderly System in Long-Term Care Institutions
Hsieh, N., Chiang, R., & Hung, W.-P. (2015) · Journal of Advances in Information Technology · doi:10.12720/jait.6.3.96-102 · Cited by 3
Classifying unevenly spaced clinical time series data using forecast error approximation based bottom-up (FeAB) segmented time delay neural network
Jane, Y. N., Nehemiah, H. K., & Kannan, A. (2020) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2020.1817791 · Cited by 3
On the Use of Graphical Models to Study ICU Outcome Prediction in Septic Patients Treated with Statins
Ribas, V. J., Caballero, J., Cuenca, A. S. D. T., Ruiz‐Rodríguez, J. C., Ruiz-Sanmartín, A., Rello, J., & Vellido, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35686-5_9 · Cited by 3
Balance dysfunction the most significant cause of in-hospital falls in patients taking hypnotic drugs: A retrospective study
Hashida, R., Matsuse, H., Yokoyama, S., Kawano, S., Higashi, E., Tajma, H., Bekki, M., Iwanaga, S., Hara, K., Nakamura, Y., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0272832 · Cited by 3
A hierarchy of correlates for objectively measured physical activity, sedentary time, and physical fitness in older adults: A CHAID analysis
Rosa, G. B., Júdice, P. B., Hetherington‐Rauth, M., Magalhães, J. P., Correia, I. R., & Sardinha, L. B. (2022) · European Journal of Sport Science · doi:10.1080/17461391.2022.2127377 · Cited by 3
The Role of Patient-Generated Data in Personalized Oncology Care and Research: Opportunities and Challenges for Real-World Implementation
Fernández-Luque, L., Núñez-Benjumea, F. J., Cervera‐Torres, S., Guerra, J. L. L., Liao, Z. X., Pagán, J. A., & Zambrana, F. R. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-07696-1_9 · Cited by 3
Information Technology
Murphy, S. N., Chueh, H. C., & Herrick, C. D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802101-9.00013-2 · Cited by 3
A Case-Based Reasoning Framework for Prediction of Stroke
Chantamit-o-pas, P., & Goyal, M. L. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-5508-9_21 · Cited by 3
Fuzzy Decision Trees in Medical Decision Making Support Systems
Barach, P. R., Levashenko, V., & Zaitseva, E. (2019) · Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care · doi:10.1177/2327857919081009 · Cited by 3
Using health data repositories for developing clinical system software: a multi‐objective fuzzy genetic approach
Raja, B. S., & Asghar, S. (2020) · IET Software · doi:10.1049/iet-sen.2019.0261 · Cited by 3
Metode-metode Data Mining untuk Penyelesaian Masalah Kehamilan dan Persalinan
Pertiwi, A. G., & Pujianto, U. (2020) · Sains Aplikasi Komputasi dan Teknologi Informasi · doi:10.30872/jsakti.v2i1.3298 · Cited by 3
Identificación de patrones de glucemia en pacientes con diabetes tipo 1 mediante monitorización continua de glucosa y técnicas de clusterización
Contador, S., Botella-Serrano, M., Garnica, Ó., Cabo, J. M. V., Zurimendi, A. A., Martínez, R. R., Maqueda, E., & Pérez, J. I. H. (2020) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2019.12.011 · Cited by 3
Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles
Fakhraei, S., Hamid Soltanian-zadeh, A., Jafari‐Khouzani, K., Elisevich, K. V., & Fotouhi, F. A. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.53 · Cited by 3
Using Data Mining to Help Detect Dysplasia: Extended Abstract
Rosenfeld, A., Sehgal, V., Graham, D. G., Banks, M. R., Haidry, R. J., & Lovat, L. B. (2014) · Software - Science, Technology and Engineering · doi:10.1109/swste.2014.21 · Cited by 3
Rough-Fuzzy Hybrid Approach for Identification of Bio-markers and Classification on Alzheimer's Disease Data
Lee, C., Lam, C., & Mašek, M. (2011) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2011.20 · Cited by 3
Attribute ranking for lateralizing focal epileptogenicity in temporal lobe epilepsy
Fakhraei, S., Hamid Soltanian-zadeh, A., Elisevich, K. V., & Fotouhi, F. A. (2010) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2010.5705029 · Cited by 3
Comparison of conventional risk factors in middle-aged versus elderly diabetic and nondiabetic patients with myocardial infarction: prediction with decision–analytic model
Mahmoodi, M. R., Baneshi, M. R., & Rastegari, A. (2015) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/2042018815600641 · Cited by 3
Healthcare Data Analytics
Hersh, W. (2014) · Cited by 3
Disease Models, Part II: Querying & Applications
Hsu, W. W. Y., & Bui, A. A. T. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0385-3_9 · Cited by 3
Expert knowledge integration in the data mining process with application to cardiovascular risk assessment
Tavares, M., Paredes, S., Rocha, T., Carvalho, P., Ramos, J., Mendes, D., Henriques, J. M. O., & Morais, J. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318909 · Cited by 3
Prior convictions, conduct and disposition: a scientific perspective
Robinson, P. M. (2016) · Griffith Law Review · doi:10.1080/10383441.2016.1168908 · Cited by 2
Classification Algorithms in Diagnosis of Alzheimer’s Disease
Kulkarni, N. N., & Bairagi, V. K. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-815392-5.00005-8 · Cited by 3
Using Machine Learning to Refer Patients with Chronic Kidney Disease to Secondary Care
Au-Yeung, L., Xie, X., Chess, J. A., & Scale, T. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413108 · Cited by 3
Designing a Process Model for Health Analytic Projects
Ahangama, S., & Poo, D. C. C. (2015) · Journal of the Association for Information Systems · Cited by 3
Survey on Data Mining and Predictive Analytics Techniques
Sathishkumar, S., Priya, R. D., & Karthika, K. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-0146-3_94 · Cited by 3
Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach
Frías, M., Moyano, J. M., Rivero‐Juárez, A., Luna, J. M., Camacho, Á., Fardoun, H. M., Machuca, I., Al-Twijri, M., Rivero, A., & Ventura, S. (2020) · Journal of Medical Internet Research · doi:10.2196/18766 · Cited by 3
Artificial intelligence in healthcare and IJMI scope
Marin, H. F. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105150 · Cited by 3
aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction in Escherichia coli causing urinary tract infection using next-generation sequencing data
Nayak, D. S. K., Pati, A., Panigrahi, A., Khan, M., Alabdullah, B. I., Sahoo, S. K., Sahu, B., Almjally, A., Mallik, S., & Swarnkar, T. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1651917 · Cited by 2
Identification of Patterns and Factors Affecting the Health of Employees Based on Datamining of Occupational Examinations with the Purpose of Promoting Occupational Health
Soleimanfar, E., Navabakhsh, M., & Sebt, M. V. (2019) · ranian Journal of Health Education and Health Promotion · doi:10.29252/ijhehp.7.3.295 · Cited by 2
Cancer Diagnosis with Improved Particle Swarm Optimization Algorithm (Hilla Hospital – Iraq) Case Study
Nabat, Z. M., Shnain, S. A., Al-Khafaji, B. J., & Salih, M. A. (2020) · Journal of Southwest Jiaotong University · doi:10.35741/issn.0258-2724.55.2.34 · Cited by 2
Efficient Performance of Data Science Application in Medical Field
Konduri, P. S., & Rao, G. S. N. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-86165-0_28 · Cited by 2
Deep Learning Approaches and Biomarkers in Medical Diagnosis
Malik, P., & Singh, S. (2023) · Recent Patents on Engineering · doi:10.2174/1872212117666230130100048 · Cited by 3
MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection
Hailu, T. G., & Abdulkadir, T. (2023) · Intelligent Information Management · doi:10.4236/iim.2023.156019 · Cited by 2
Advancing Through Data: A Critical Review of the Evolution of Medical Information Management Systems
(2023) · International Journal of Bio-Medical Informatics and e-Health · doi:10.30534/ijbmieh/2023/121162023 · Cited by 2
Perbandingan Metode Data Mining dalam Pengklasifikasian Status Desa Kabupaten Purwakarta dan Bandung Barat (Podes 2021)
Almasah, M. Z., & Wijayanto, A. W. (2023) · EIGEN MATHEMATICS JOURNAL · doi:10.29303/emj.v6i1.156 · Cited by 2
MoCab: A framework for the deployment of machine learning models across health information systems
Kuo, Z.-M., Chen, K., & Tseng, Y. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108336 · Cited by 4
Data Mining and Business Intelligence Trends
Mary, S. S. C., & Arun, C. J. (2024) · Springer eBooks · doi:10.1007/978-981-97-2902-9_5 · Cited by 2
Análisis predictivo del riesgo de hipertensión en adultos mexicanos basado en indicadores nutricionales y calóricos
Acuña-Cid, H. A., Mauricio-González, A., Solís-Robles, R., Ruíz, A. A., & Reveles-Gómez, L. C. (2024) · Revista Ingenio · doi:10.22463/2011642x.4684 · Cited by 2
The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework
Walczak, S. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-1754-3.ch008 · Cited by 2
Engineering Principles in Biomedical Informatics
Bellazzi, R., Gabetta, M., & Leonardi, G. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-401678-1.00010-5 · Cited by 2
Data Mining Industrial Applications
Wójcik, W., & Gromaszek, K. (2011) · InTech eBooks · doi:10.5772/13573 · Cited by 2
Agent Technology and Healthcare: Possibilities, Challenges and Examples of Application
Sánchez, D., Isern, D., & Moreno, A. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-14464-6_3 · Cited by 2
Citation Analysis of the Prognosis of Haux et al. for the Year 2013
Stausberg, J. (2014) · Journal of Medical Systems · doi:10.1007/s10916-014-0071-8 · Cited by 2
Querying phenotype-genotype associations across multiple knowledge bases using Semantic Web technologies
Beyan, O., Iqbal, A., Khan, Y., Antoniades, A., Keane, J. A., Hasapis, P., Georgousopoulos, C., Ioannidi, M., Decker, S., & Sahay, R. (2013) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2013.6701573 · Cited by 2
Performance Comparison of Decision Trees of J48 and Reduced-Error Pruning
Jin, H., & Jung, Y. G. (2016) · International journal of advanced smart convergence · doi:10.7236/ijasc.2016.5.1.30 · Cited by 2
Study of Data Mining Algorithms in the Context of Performance Enhancement of Classification
Goel, A., & Kr., S. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016907963 · Cited by 2
Prediction and Prognosis of Health and Disease
Oniśko, A., Tucker, A., & Drużdżel, M. J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-28007-3_11 · Cited by 2
The Practice of Medicine in the Age of Information Technology
Alscher, M. D., & Schmidt, N. (2018) · InTech eBooks · doi:10.5772/intechopen.75482 · Cited by 2
Omics-Based Nanomedicine
Nayak, C., Chandra, I., Singh, P. C., & Singh, S. K. (2018) · Springer eBooks · doi:10.1007/978-981-10-8693-9_12 · Cited by 2
Precision Health Care for Sustainable Patient Centric Solutions
Odhiambo, J. N., & Ochara, N. M. (2018) · Open Innovations Conference (OI) · doi:10.1109/oi.2018.8535905 · Cited by 2
A two-step approach to predictive modeling of individual-based environmental health risks
Bae, W. D., Horak, M., Alkobaisi, S., Kim, S., Narayanappa, S., Park, C., & Bae, D. J. (2019) · ACM Symposium on Applied Computing · doi:10.1145/3297280.3297350 · Cited by 2
Investigating the Risk Factors for Low Birth Weight Using Data Mining: A Case Study of Imam Ali Hospital, Zahedan, Iran
Jahantigh, F. F., Nazarnejad, R., & Jahantigh, M. F. (2016) · Majallah-i dānishgāh-i ̒ulūm-i pizishkī-i Māzandarān/Journal of Mazandaran University of Medical Sciences · Cited by 2
Integrating deep learning, social networks, and big data for healthcare system
Naoui, M. A., Lejdel, B., Ayad, M., Belkeiri, R., & Khaouazm, A. S. (2020) · Bio-Algorithms and Med-Systems · doi:10.1515/bams-2019-0043 · Cited by 2
Predicting Type 2 Diabetes Complications and Personalising Patient Using Artificial Intelligence Methodology
Yousefi, L., & Tucker, A. (2020) · Type 2 Diabetes [Working Title] · doi:10.5772/intechopen.94228 · Cited by 4
Equine simplified acute physiology score: Personalised medicine for the equine emergency patient
de Barros, A. D. M. C., da Silva, A. F. R., Zibordi, M., Spagnolo, J. D., Corrêa, R. R., Belli, C. B., & Camargo, M. M. D. (2021) · Veterinary Record · doi:10.1002/vetr.136 · Cited by 2
Medicine prediction based on doctor’s degree: a data mining approach
Arman, M. S., Sarker, K., Shakir, A. K., Hossain, S. F., & Hasan, A. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v26.i2.pp1125-1134 · Cited by 2
Ontology Based Optimization with Adaptive Network Based Fuzzy Inference System Based Health Maintenance System
(2017) · International Journal of Current Research and Review · doi:10.7324/ijcrr.2017.9237 · Cited by 2
APPLYING THE PRIORITY DISTRIBUTION METHOD FOR EMPLOYEE MOTIVATION
Žaptorius, J. (2013) · Business Management and Education · doi:10.3846/bme.2013.15 · Cited by 2
An Empirical Analysis of Unsupervised Learning Approach on Medical Databases
Chauhan, R., Kaur, H., & Puri, R. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-1540-3_7 · Cited by 2
A novel method for fuzzy diagnostic system design
Langarizadeh, M., & Orooji, A. (2018) · Medical Journal of the Islamic Republic of Iran · doi:10.14196/mjiri.32.85 · Cited by 2
Why Big Data and What Is It? Basic to Advanced Big Data Journey for the Medical Industry
Sharma, N., Patil, M. M., & Shamkuwar, M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817356-5.00010-3 · Cited by 2
Prediction Models
Tyagi, A. K. (2020) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-7998-2742-9.ch004 · Cited by 2
Clinical decision rules for epilepsy care: The case for thinking big
Pugh, M. J. (2016) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2016.02.034 · Cited by 2
Developing a Predictive Model for Hospital-Acquired Catheter-Associated Urinary Tract Infections Using Electronic Health Records and Nurse Staffing Data
Park, J. I. (2016) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 2
A framework for testing different imputation methods for tabular datasetsPreprint
Kossen, T., Livne, M., Madai, V. I., Galinović, I., Frey, D., & Fiebach, J. B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/773762 · Cited by 2
Artificial Intelligence Assisted Early Warning System for Acute Kidney Injury Driven by Multi-Center ICU DatabasePreprint
Huang, S., Chen, L., Wang, L., Cui, X., Feng, C., Zhang, Z., & Li, T. (2020) · medRxiv · doi:10.1101/2020.01.27.20019091 · Cited by 2
Predictive Modelling of the Comorbidity of Chronic Diseases: A Network and Machine Learning Approach
Hossain, E. (2020) · The Sydney eScholarship Repository (The University of Sydney) · Cited by 2
Analyzing the impact of epidermoid and adeno tissue on cancer incidence whit a data mining approachPreprint
Ghorbian, M. (2023) · Research Square · doi:10.21203/rs.3.rs-2805471/v1 · Cited by 2
Hybrid decision making in the monitoring of hypertensive patients
Chen, L., Kang, G., Zhang, X., Lee, L., & Li, X. (2012) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2012.6380061 · Cited by 1
Ontology-Based CRIS Design Using Three Stage Development Approach
Hsieh, N., Keh, H., Chan, C.-H., & Wang, S. (2012) · Smart innovation, systems and technologies · doi:10.1007/978-3-642-29920-9_46 · Cited by 1
Exploration of Fall-Evaluation Scores Using Clinical Tools with the Short-Form Berg Balance Scale and Timed Up and Go and Motion Detection Sensors
Lee, C.-H., Wu, C.-H., Jiang, B. C., & Sun, T. (2020) · Applied Sciences · doi:10.3390/app10196931 · Cited by 1
Biomedical Advances: Neuroinnovation and Technology
Martinez‐Martin, N. (2023) · Springer eBooks · doi:10.1007/978-3-031-14339-7_5 · Cited by 1
Data Mining Applications for Enhancing Healthcare Services: A Comprehensive Review
Yadav, S., Singh, D. M. K., & Kumar, P. (2023) · International Journal of Engineering Technology and Management Sciences · doi:10.46647/ijetms.2023.v07i05.038 · Cited by 4

Authors: BELLAZZI, R., & ZUPAN, B.
Affiliations: University of Pavia (IT)
University of Ljubljana (SI)
Corresponding author: BELLAZZI, R
ORCID: BELLAZZI, R
ZUPAN, B
Venue: International Journal of Medical Informatics
Publisher: Elsevier BV
Published: 2008-02
Volume: 77
Issue: 2
Pages: 81-97
ISSN: 1386-5056 (ISSN-L)
DOI: 10.1016/j.ijmedinf.2006.11.006
OpenAlex: W2132996843
Cited by: 823
References: 111
Citations by year: 2026: 19 · 2025: 20 · 2024: 43 · 2023: 37 · 2022: 41 · 2021: 44 · 2020: 62 · 2019: 56 · 2018: 64 · 2017: 76 · 2016: 85 · 2015: 59 · 2014: 46 · 2013: 53 · 2012: 35
FWCI: 37.51
Type: journal article
Language: en
Open access: green
repozitorij.uni-lj.si
Licence: other-oa
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: International Journal of Medical Informatics (publishedVersion)
PubMed (publishedVersion)
Repository of the University of Ljubljana (University of Ljubljana) (publishedVersion, other-oa)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: predictive data mining, clinical prediction model, model validation, model dissemination, clinical decision support, genomic medicine, methodological recommendations, medical informatics
MeSH terms: Statistics as Topic, United States, Models, Statistical, Clinical Medicine, Guidelines as Topic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Predictive data mining in clinical medicine: Current issues and guidelines

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks