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Mining Electronic Health Records (EHRs)

Pranjul Yadav, Michael Steinbach, Vipin Kumar, Gyorgy Simon
2018 ACM Computing Surveys  
In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data.  ...  With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.  ...  Textbook data mining techniques have a limited ability to handle the temporal aspect of EHR data.  ... 
doi:10.1145/3127881 fatcat:xil7qev3xbf3pmfv5vtak4f2jq

Big Data Technologies

Riccardo Bellazzi, Arianna Dagliati, Lucia Sacchi, Daniele Segagni
2015 Journal of Diabetes Science and Technology  
Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support.  ...  This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care.  ...  -Mosaic, Models and Simulation Techniques for Discovering Diabetes Influence Factors.  ... 
doi:10.1177/1932296815583505 pmid:25910540 pmcid:PMC4667334 fatcat:ttnwjfxxe5dphfczqyl2o2hzwa

Predicting Type 2 Diabetes Complications and Personalising Patient Using Artificial Intelligence Methodology [chapter]

Leila Yousefi, Allan Tucker
2020 Type 2 Diabetes [Working Title]  
The prediction of the onset of different complications of disease, in general, is challenging due to the existence of unmeasured risk factors, imbalanced data, time-varying data due to dynamics, and various  ...  latent variable discovery by using diabetes as a case study.  ...  Acknowledgements I thank the following individuals for their expertise and assistance throughout all aspects of this study and for their insightful suggestions and careful reading of the manuscript.  ... 
doi:10.5772/intechopen.94228 fatcat:dkhosuu5xbhuxp4sszjpcvpmga

Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

Isa Kristina Kirk, Christian Simon, Karina Banasik, Peter Christoffer Holm, Amalie Dahl Haue, Peter Bjødstrup Jensen, Lars Juhl Jensen, Cristina Leal Rodríguez, Mette Krogh Pedersen, Robert Eriksson, Henrik Ullits Andersen, Thomas Almdal (+9 others)
2019 eLife  
We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort.  ...  Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum.  ...  Stratification of type 1 diabetes risk on the basis of islet autoantibody characteristics. Diabetes 53:384–392.  ... 
doi:10.7554/elife.44941 pmid:31818369 fatcat:wtsnydn46bfn5ojgxvlv2axnpi

Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling

Leila Yousefi, Stephen Swift, Mahir Arzoky, Lucia Saachi, Luca Chiovato, Allan Tucker
2018 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
The prediction process is complex due to the existence of unmeasured risk factors, the unexpected development of complications and varying responses of patients to disease over time.  ...  Exploiting these unmeasured risk factors (hidden variables) can improve the modeling of disease progression and thus enables clinicians to focus on early diagnosis and treatment of unexpected conditions  ...  Data mining and analysis were performed using MATLAB and Bayes Net toolbox [19] and for visualization we used Graphviz. II.  ... 
doi:10.1109/bibm.2018.8621484 dblp:conf/bibm/YousefiSASCT18 fatcat:q3k6f4pw6jfk5gumjjkas77iha

A dashboard-based system for supporting diabetes care

Arianna Dagliati, Lucia Sacchi, Valentina Tibollo, Giulia Cogni, Marsida Teliti, Antonio Martinez-Millana, Vicente Traver, Daniele Segagni, Jorge Posada, Manuel Ottaviano, Giuseppe Fico, Maria Teresa Arredondo (+3 others)
2018 JAMIA Journal of the American Medical Informatics Association  
Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction  ...  models for type 2 diabetes complications.  ...  ' temporal data and assess the risk of developing complications or disease progression.  ... 
doi:10.1093/jamia/ocx159 pmid:29409033 fatcat:bssbn6x2wbdc7f77j5mnmexfeq

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Ivan Contreras, Josep Vehi
2018 Journal of Medical Internet Research  
Conclusions: We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes  ...  Consequently, these methods provide powerful tools for improving patients' quality of life.  ...  Risk assessment and patient stratification methods are important to improving the management of diabetes, and therefore the overall health outcomes of diabetic patients, and consequently have attracted  ... 
doi:10.2196/10775 pmid:29848472 pmcid:PMC6000484 fatcat:2ncnce6nxzfxtdxu3g5dbt3riq

Machine Learning and Data Mining Methods in Diabetes Research

Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda
2017 Computational and Structural Biotechnology Journal  
The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction  ...  Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data.  ...  Acknowledgements This work has been partially supported by Horizon 2020 Framework Programme of the European Union under grant agreement 644906, the AEGLE project.  ... 
doi:10.1016/j.csbj.2016.12.005 pmid:28138367 pmcid:PMC5257026 fatcat:gq3lcg5i7jal7ps45vrbje6ufu

Existential Methods on Diabetes Detection using Machine Learning

2020 International journal of recent technology and engineering  
Various ML algorithms are used using PIDD (Pima Indian diabetes dataset), and improved k- means using logistic regression among all algorithms achieved the highest accuracy.  ...  Nowadays, a lot of research is going on in healthcare. One of the significant diseases increased all over the world is Diabetes Mellitus (DM).  ...  They have also discussed on macrovascular and microvascular diabetic complications, for these researchers used temporal data mining and machine learning algorithms for risk stratification.  ... 
doi:10.35940/ijrte.f7157.038620 fatcat:eceud5ajobbgla4pfhyrdwkzt4

Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

Leila Yousefi, Stephen Swift, Mahir Arzoky, Lucia Saachi, Luca Chiovato, Allan Tucker
2020 Computational intelligence  
We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction.  ...  It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated.  ...  In particular, we used dynamic Bayesian networks to model clinical data and predict the onset of type 2 diabetes mellitus (T2DM) complications. 1 We developed methods to infer the location of hidden  ... 
doi:10.1111/coin.12313 fatcat:t42aoje46nfqvfwtqo4rdcwmwu

Investigating risk factors and predicting complications in deep brain stimulation surgery with machine learning algorithms

Farrokh Farrokhi, Quinlan D. Buchlak, Matt Sikora, Nazanin Esmaili, Maria Marsans, Pamela McLeod, Jamie Mark, Emily Cox, Christine Bennett, Jonathan Carlson
2019 World Neurosurgery  
Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data.  ...  DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes.  ...  Models of this nature 296 should form part of a broader comprehensive approach to clinical risk stratification and patient safety 297 improvement.  ... 
doi:10.1016/j.wneu.2019.10.063 pmid:31634625 fatcat:cqwpw6khbfhy3krxmxqculmaiu

Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

Arianna Dagliati, Valentina Tibollo, Lucia Sacchi, Alberto Malovini, Ivan Limongelli, Matteo Gabetta, Carlo Napolitano, Andrea Mazzanti, Pasquale De Cata, Luca Chiovato, Silvia Priori, Riccardo Bellazzi
2018 Frontiers in Digital Humanities  
Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration  ...  Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrhythmogenic Diseases.  ...  The data is collected into an i2b2 data warehouse, and exploited via advanced temporal analytics tools focused on Diabetes complications.  ... 
doi:10.3389/fdigh.2018.00008 fatcat:pgumv6gsjrgcpecr65zrljsrja

AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes [article]

Marianna Maranghi, Aris Anagnostopoulos, Irene Cannistraci, Ioannis Chatzigiannakis, Federico Croce, Giulia Di Teodoro, Michele Gentile, Giorgio Grani, Maurizio Lenzerini, Stefano Leonardi, Andrea Mastropietro, Laura Palagi (+4 others)
2022 arXiv   pre-print
The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database.  ...  This paper presents the initial results of an ongoing project whose focus is the application of Artificial Intelligence and Machine Learning techniques for conceptualizing, cleaning, and analyzing such  ...  This work would have not been possible without the precious guidance and expertise of Dr.  ... 
arXiv:2206.06182v2 fatcat:7p55g7ac5bhgraqeflmdgzccai

Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study

Wenjuan Wang, Anthony G. Rudd, Yanzhong Wang, Vasa Curcin, Charles D. Wolfe, Niels Peek, Benjamin Bray
2022 BMC Neurology  
Backgrounds We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care  ...  Methods Data from the UK Sentinel Stroke National Audit Program between 2013 to 2019 were used.  ...  The findings of the research are intended to inform the design of predictive analytics used for mortality risk stratification and to support quality improvement in stroke care and benchmark stroke care  ... 
doi:10.1186/s12883-022-02722-1 pmid:35624434 pmcid:PMC9137068 fatcat:lmivi2sq6naxvibcgzq6xmp6qi

A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes

Simone Marini, Emanuele Trifoglio, Nicola Barbarini, Francesco Sambo, Barbara Di Camillo, Alberto Malovini, Marco Manfrini, Claudio Cobelli, Riccardo Bellazzi
2015 Journal of Biomedical Informatics  
The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related  ...  In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal  ...  learning a DBN with a rich data set coming from the Diabetes Control and Complications Trial (DCCT) [21] and Epidemiology of Diabetes Interventions and Complications (EDIC) [22] studies, comprising  ... 
doi:10.1016/j.jbi.2015.08.021 pmid:26325295 fatcat:qnbmypt6m5eedevfwpipidd5bm
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