A predictive analytics framework for identifying patients at risk of developing multiple medical complications caused by chronic diseases
References
[1]
R. Amarasingham, R.E. Patzer, M. Huesch, N.Q. Nguyen, B. Xie, Implementing electronic health care predictive analytics: considerations and challenges, Health Aff. (Millwood) 33 (2014) 1148–1154.
[2]
I. Bardhan, J. Oh, Z. Zheng, K. Kirksey, Predictive analytics for readmission of patients with congestive heart failure, Inf. Syst. Res. 26 (2014) 19–39.
[3]
D.W. Bates, S. Saria, L. Ohno-Machado, A. Shah, G. Escobar, Big data in health care: using analytics to identify and manage high-risk and high-cost patients, Health Aff. (Millwood) 33 (2014) 1123–1131.
[4]
L. Chen, X. Li, Y. Yang, H. Kurniawati, Q.Z. Sheng, H.-Y. Hu, et al., Personal health indexing based on medical examinations: a data mining approach, Decis Support Syst 81 (2016) 54–65,.
[5]
A. Dag, A. Oztekin, A. Yucel, S. Bulur, F.M. Megahed, Predicting heart transplantation outcomes through data analytics, Decis Support Syst 94 (2017) 42–52.
[6]
D. Delen, A. Oztekin, L. Tomak, An analytic approach to better understanding and management of coronary surgeries, Decis Support Syst 52 (2012) 698–705,.
[7]
G. Meyer, G. Adomavicius, P.E. Johnson, M. Elidrisi, W.A. Rush, J.A.M. Sperl-Hillen, P.J. O’Connor, A machine learning approach to improving dynamic decision making, Inf. Syst. Res. 25 (2014) 239–263,.
[8]
S. Piri, D. Delen, T. Liu, H.M. Zolbanin, A data analytics approach to building a clinical decision support system for diabetic retinopathy: developing and deploying a model ensemble, Decis Support Syst 101 (2017) 12–27.
[9]
C.-J. Tseng, C.-J. Lu, C.-C. Chang, G.-D. Chen, C. Cheewakriangkrai, Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence, Artif Intell Med 78 (2017) 47–54,.
[10]
A. Wulff, B. Haarbrandt, E. Tute, M. Marschollek, P. Beerbaum, T. Jack, An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR, Artif Intell Med (2018),.
[11]
C.L. Brown, B.G. Hammill, L.G. Qualls, L.H. Curtis, A.J. Muir, Significant morbidity and mortality among hospitalized end-stage liver disease patients in Medicare, J Pain Symptom Manage 52 (2016) 412–419. e1.
[12]
M.D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J.C. Folk, et al., Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning, Invest Ophthalmol Vis Sci 57 (2016) 5200–5206.
[13]
E. Choi, A. Schuetz, W.F. Stewart, J. Sun, Using recurrent neural network models for early detection of heart failure onset, J Am Med Inform Assoc 112 (2016),.
[14]
A. Dagliati, S. Marini, L. Sacchi, G. Cogni, M. Teliti, V. Tibollo, et al., Machine learning methods to predict diabetes complications, J Diabetes Sci Technol (2017).
[15]
P.B. Jensen, L.J. Jensen, S. Brunak, Mining electronic health records: towards better research applications and clinical care, Nat Rev Genet 13 (2012) 395.
[16]
R. Kohli, S.S.-L. Tan, Electronic Health Records: How Can IS Researchers Contribute to Transforming Healthcare?, MIS Q 40 (2016) 553–573.
[17]
B. López, C. Martin, P.H. Viñas, Special section on artificial intelligence for diabetes, Artif Intell Med 85 (2018) 26–27,.
[18]
K. Park, A. Ali, D. Kim, Y. An, M. Kim, H. Shin, Robust predictive model for evaluating breast cancer survivability, Eng Appl Artif Intell 26 (2013) 2194–2205.
[19]
R. Stoean, C. Stoean, M. Lupsor, H. Stefanescu, R. Badea, Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C, Artif Intell Med 51 (2011) 53–65,.
[20]
Y.P. Tabak, X. Sun, C.M. Nunez, R.S. Johannes, Using electronic health record data to develop inpatient mortality predictive model: acute Laboratory Risk of Mortality Score (ALaRMS), J Am Med Inform Assoc 21 (2013) 455–463.
[21]
J.-Y. Yeh, T.-H. Wu, C.-W. Tsao, Using data mining techniques to predict hospitalization of hemodialysis patients, Decis Support Syst 50 (2011) 439–448,.
[22]
M. Sangi, K.T. Win, F. Shirvani, M.-R. Namazi-Rad, N. Shukla, Applying a novel combination of techniques to develop a predictive model for diabetes complications, PLoS One 10 (2015),.
[23]
B.J. Maron, Hypertrophic cardiomyopathy: a systematic review, JAMA 287 (2002) 1308–1320,.
[24]
S.B. Kotsiantis, I.D. Zaharakis, P.E. Pintelas, Machine learning: a review of classification and combining techniques, Artif Intell Rev 26 (2006) 159–190.
[25]
D. Bardou, K. Zhang, S.M. Ahmad, Lung sounds classification using convolutional neural networks, Artif Intell Med 88 (2018) 58–69,.
[26]
S. Kang, Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks, Artif Intell Med 85 (2018) 1–6,.
[27]
Z. Liang, G. Zhang, J.X. Huang, Q.V. Hu, Deep Learning for Healthcare Decision Making With EMRs, in: Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference On, 2014, pp. 556–559.
[28]
R. Miotto, F. Wang, S. Wang, X. Jiang, J.T. Dudley, Deep learning for healthcare: review, opportunities and challenges, Brief. Bioinform (2017).
[29]
D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, et al., Deep learning for health informatics, IEEE J Biomed Health Inform 21 (2017) 4–21.
[30]
V. Schetinin, L. Jakaite, W. Krzanowski, Bayesian averaging over Decision Tree models for trauma severity scoring, Artif Intell Med 84 (2018) 139–145,.
[31]
B. Shickel, P.J. Tighe, A. Bihorac, P. Rashidi, Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis, IEEE J Biomed Health Inform (2017).
[32]
A. Suner, C.C. Çelikoğlu, O. Dicle, S. Sökmen, Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer, Artif Intell Med 56 (2012) 59–68,.
[33]
P. Nguyen, T. Tran, N. Wickramasinghe, S. Venkatesh, Deepr: a convolutional net for medical records, IEEE J Biomed Health Inform 21 (2017) 22–30.
[34]
T. Zheng, W. Xie, L. Xu, X. He, Y. Zhang, M. You, et al., A machine learning-based framework to identify type 2 diabetes through electronic health records, Int J Media Inf Lit 97 (2017) 120–127,.
[35]
S. Walczak, V. Velanovich, An evaluation of artificial neural networks in predicting pancreatic Cancer survival, J Gastrointest Surg 21 (2017) 1606–1612.
[36]
H.M. Zolbanin, D. Delen, A. Hassan Zadeh, Predicting overall survivability in comorbidity of cancers: a data mining approach, Decis Support Syst 74 (2015) 150–161,.
[37]
D.S.W. Ting, C.Y.-L. Cheung, G. Lim, G.S.W. Tan, N.D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I.Y. San Yeo, S.Y. Lee, Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes, Jama 318 (2017) 2211–2223.
[38]
V. Kothari, R.J. Stevens, A.I. Adler, I.M. Stratton, S.E. Manley, H.A. Neil, R.R. Holman, UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine, Stroke 33 (2002) 1776–1781.
[39]
E. Pahl, L.A. Sleeper, C.E. Canter, D.T. Hsu, M. Lu, S.A. Webber, et al., Incidence of and risk factors for sudden cardiac death in children with dilated cardiomyopathy: a report from the pediatric cardiomyopathy registry, J Am Coll Cardiol 59 (2012) 607–615,.
[40]
American Heart Association, Hypertrophic cardiomyopathy [WWW document], URL http://www.heart.org/HEARTORG/Conditions/More/Cardiomyopathy/Hypertrophic-Cardiomyopathy_UCM_444317_Article.jsp#.WoczfKjwbD4 (accessed 2.16.18) 2016.
[41]
R. Caruana, Multitask learning, in: learning to learn, Springer, Boston, MA, 1998, pp. 95–133,.
[42]
M. Tan, Prediction of anti-cancer drug response by kernelized multi-task learning, Artif Intell Med 73 (2016) 70–77,.
[43]
D. Zhou, L. Miao, Y. He, Position-aware deep multi-task learning for drug–drug interaction extraction, Artif Intell Med 87 (2018) 1–8,.
[44]
N. Tangri, L.A. Stevens, J. Griffith, H. Tighiouart, O. Djurdjev, D. Naimark, A. Levin, A.S. Levey, A predictive model for progression of chronic kidney disease to kidney failure, Jama 305 (2011) 1553–1559.
[45]
D. Zhang, D. Shen, Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in alzheimer’s disease, NeuroImage 59 (2012) 895–907,.
[46]
N. Razavian, J. Marcus, D. Sontag, Multi-task prediction of disease onsets from longitudinal laboratory tests, in: machine learning for healthcare Conference, Presented at the Machine Learning for Healthcare Conference (2016) 73–100.
[47]
H.H.-C. Chuang, Mathematical modeling and Bayesian estimation for error-prone retail shelf audits, Decis Support Syst 80 (2015) 72–82,.
[48]
B. Heinrich, M. Klier, A. Schiller, G. Wagner, Assessing data quality – a probability-based metric for semantic consistency, Decis Support Syst 110 (2018) 95–106,.
[49]
C. Liu, A. Talaei-Khoei, D. Zowghi, J. Daniel, Data completeness in healthcare: a literature survey, Pac. Asia J. Assoc. Inf. Syst. 9 (2017).
[50]
R.K. Ando, T. Zhang, A framework for learning predictive structures from multiple tasks and unlabeled data, J Mach Learn Res 6 (2005) 1817–1853.
[51]
B. Bakker, T. Heskes, Task clustering and gating for bayesian multitask learning, J Mach Learn Res 4 (2003) 83–99.
[52]
J. Baxter, A model of inductive bias learning, J Artif Intell Res 12 (2000) 149–198.
[53]
A.I. Namburete, W. Xie, M. Yaqub, A. Zisserman, J.A. Noble, Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning, Med Image Anal (2018).
[54]
R. Ranjan, V.M. Patel, R. Chellappa, Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition, IEEE Trans Pattern Anal Mach Intell (2017).
[55]
J. Yu, B. Zhang, Z. Kuang, D. Lin, J. Fan, iPrivacy: image privacy protection by identifying sensitive objects via deep multi-task learning, IEEE Trans. Inf. Forensics Secur. 12 (2017) 1005–1016.
[56]
J. Baxter, A model of inductive bias learning, J Artif Intell ResJAIR 12 (2000) 3.
[57]
Y. Liu, C. Jiang, H. Zhao, Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums, Decis Support Syst 105 (2018) 1–12,.
[58]
A. Gelman, A. Jakulin, M.G. Pittau, Y.-S. Su, A weakly informative default prior distribution for logistic and other regression models, Ann Appl Stat 2 (2008) 1360–1383.
[59]
S.R. Jammalamadaka, J. Qiu, N. Ning, Multivariate bayesian structural time series model, ArXiv Prepr (2018) ArXiv180103222.
[60]
L. Melie-Garcia, B. Draganski, J. Ashburner, F. Kherif, Multiple linear regression: bayesian inference for distributed and big data in the medical informatics platform of the human brain project, bioRxiv (2018).
[61]
S. Gribling, D. de Laat, M. Laurent, Matrices with high completely positive semidefinite rank, Linear Algebra Its Appl. 513 (2017) 122–148.
[62]
L. Follett, C. Yu, Achieving parsimony in bayesian VARs with the horseshoe prior, ArXiv Prepr (2017) ArXiv170907524.
[63]
D. Lewandowski, D. Kurowicka, H. Joe, Generating random correlation matrices based on vines and extended onion method, J Multivar Anal 100 (2009) 1989–2001.
[64]
E.W. Steyerberg, G.J. Borsboom, H.C. van Houwelingen, M.J. Eijkemans, J.D.F. Habbema, Validation and updating of predictive logistic regression models: a study on sample size and shrinkage, Stat Med 23 (2004) 2567–2586.
[65]
C. De Mol, D. Giannone, L. Reichlin, Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?, J Econom 146 (2008) 318–328.
[66]
S.K. Lee, On generalized multivariate decision tree by using GEE, Comput Stat Data Anal 49 (2005) 1105–1119.
[67]
P. Shih, C. Liu, Face detection using discriminating feature analysis and support vector machine, Pattern Recognit 39 (2006) 260–276.
[68]
F. Ahmadzadeh, Change point detection with multivariate control charts by artificial neural network, Int. J. Adv. Manuf. Technol. (2009) 1–12.
[69]
T. Liu, D. Tao, M. Song, S.J. Maybank, Algorithm-dependent generalization bounds for multi-task learning, IEEE Trans Pattern Anal Mach Intell 39 (2017) 227–241.
[70]
P.J. García-Laencina, P.H. Abreu, M.H. Abreu, N. Afonoso, Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values, Comput Biol Med 59 (2015) 125–133,.
[71]
N. Shukla, M. Hagenbuchner, K.T. Win, J. Yang, Breast cancer data analysis for survivability studies and prediction, Comput Methods Programs Biomed 155 (2018) 199–208,.
[72]
P. Cunningham, S.J. Delany, k-Nearest neighbour classifiers, Mult. Classif. Syst. 34 (2007) 1–17.
[73]
A. Kusiak, B. Dixon, S. Shah, Predicting survival time for kidney dialysis patients: a data mining approach, Comput Biol Med 35 (2005) 311–327.
[74]
SCAO, National Clinical Terminology Service (NCTS) website [WWW document], URL https://www.healthterminologies.gov.au/ (accessed 12.29.17) 2016.
[75]
M. Sariyar, A. Borg, K. Pommerening, Missing values in deduplication of electronic patient data, J Am Med Inform Assoc 19 (2011) e76–e82.
[76]
C. Ferri, J. Hernández-Orallo, R. Modroiu, An experimental comparison of performance measures for classification, Pattern Recognit Lett 30 (2009) 27–38,.
[77]
M.H. Zweig, G. Campbell, Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine, Clin Chem 39 (1993) 561–577.
[78]
A. Dag, K. Topuz, A. Oztekin, S. Bulur, F.M. Megahed, A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival, Decis Support Syst 86 (2016) 1–12.
[79]
M. Ghil, P. Yiou, S. Hallegatte, B.D. Malamud, P. Naveau, A. Soloviev, et al., Extreme events: dynamics, statistics and prediction, Nonlinear Process Geophys 18 (2011) 295–350.
[80]
E. Demir, A decision support tool for predicting patients at risk of readmission: a comparison of classification trees, logistic regression, generalized additive models, and multivariate adaptive regression splines, Decis. Sci. 45 (2014) 849–880.
[81]
M. Ivanović, Z. Budimac, An overview of ontologies and data resources in medical domains, Expert Syst Appl 41 (2014) 5158–5166,.
[82]
M.F. McGuire, Pancreatic Cancer: insights from counterterrorism theories, Decis Anal 11 (2014) 265–276,.
[83]
F. Zandi, A bi-level interactive decision support framework to identify data mining-oriented electronic health record architectures, Appl Soft Comput 18 (2014) 136–145,.
[84]
S.-T. Liaw, A. Rahimi, P. Ray, J. Taggart, S. Dennis, S. de Lusignan, et al., Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature, Int J Media Inf Lit 82 (2013) 10–24.
[85]
M.-L. Zhang, Z.-H. Zhou, A review on multi-label learning algorithms, IEEE Trans Knowl Data Eng 26 (2014) 1819–1837.
[86]
L. Chen, D.J. Magliano, P.Z. Zimmet, The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives, Nat Rev Endocrinol 8 (2012) 228–236.
[87]
M.G. White, J.A.M. Shaw, R. Taylor, Type 2 diabetes: the pathologic basis of reversible β-Cell dysfunction, Diabetes Care 39 (2016) 2080–2088,.
[88]
Y.-K. Lin, H. Chen, R.A. Brown, S.-H. Li, H.-J. Yang, Healthcare predictive analytics for risk profiling in chronic care: a Bayesian multitask learning approach, MIS Q (2017) 41.
[89]
B. Liu, Y. Li, S. Ghosh, Z. Sun, K. Ng, J. Hu, Complication risk profiling in diabetes care: a bayesian multi-task and feature relationship learning approach, IEEE Trans Knowl Data Eng (2019).
[90]
A. Argyriou, T. Evgeniou, M. Pontil, Convex multi-task feature learning, Mach Learn 73 (2008) 243–272.
[91]
Y. Zhang, D.-Y. Yeung, A convex formulation for learning task relationships in multi-task learning, ArXiv Prepr (2012).
Index Terms
- A predictive analytics framework for identifying patients at risk of developing multiple medical complications caused by chronic diseases
Index terms have been assigned to the content through auto-classification.
Recommendations
Understanding the Comorbidity of Multiple Chronic Diseases Using a Network Approach
ACSW '19: Proceedings of the Australasian Computer Science Week MulticonferenceChronic diseases and associated conditions are the leading causes of death in most of the countries worldwide. Due to this, governments all over the world are concerned about the burden of chronic diseases. These diseases often pose severe health risks ...
Predictive Modelling for Chronic Disease: Machine Learning Approach
ICCDA '20: Proceedings of the 2020 4th International Conference on Compute and Data AnalysisChronic diseases are responsible for half of annual mortality (51%) and almost half of the burden of all diseases (41%) in Bangladesh. Developing countries like Bangladesh are in a probable state of approximate loss of $7.3 trillion due to chronic ...
Non-alcoholic Fatty Liver and Liver Fibrosis Predictive Analytics: Risk Prediction and Machine Learning Techniques for Improved Preventive Medicine
AbstractNon-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, with a prevalence of 20%–30% in the general population. NAFLD is associated with increased risk of cardiovascular disease and may progress to cirrhosis with ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Elsevier B.V.
Publisher
Elsevier Science Publishers Ltd.
United Kingdom
Publication History
Published: 01 November 2019
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 21 Jan 2025