Diagnostics Based Patient Classification for Clinical Decision Support Systems

[1] C.-J. Hsiao, E. Hing, “Use and characteristics of electronic health record systems among office based physician practices,” NCHS Data Brief, vol. 111, 2012, pp. 1–8. Search in Google Scholar

[2] G.S. Alotaibi, C. Wu, A. Senthilselvan, M.S. McMurtry, “The validity of ICD codes coupled with imaging procedure codes for identifying acute venous thromboembolism using administrative data,” Vasc. Med., vol. 20, no. 4, 2015, pp. 364–368. doi: 10.1177/1358863X15573839. Search in Google Scholar

[3] W.Q. Wei, P.L. Teixeira, H. Mo, R.M. Cronin, J.L. Warner, J.C. Denny, “Combining billing codes, clinica notes, and medications from electronic health records provides superior phenotyping performance,” J. Am. Med. Inform. Assoc., vol. 23, no. e1, 2016, pp. 20–27. doi: 10.1093/jamia/ocv130. Search in Google Scholar

[4] World Health Organization, “International Classification of Diseases (icd),” 2012. Search in Google Scholar

[5] H. Lamberts, I. Okkes, et al, “Icpc-2,” International Classification of Primary Care, 1998. Search in Google Scholar

[6] S. Pakhomov, J.D. Buntrock, C.G. Chute, “Automating the assignment of diagnosis codes to patient encounters using example based and machine learning techniques,” J. Am. Med. Inform. Assoc., vol. 13, no. 5, 2006, pp. 516–525, doi: 10.1197/jamia.M2077. Search in Google Scholar

[7] M.H. Stanfill, M. Williams, S.H. Fenton, R.A. Jenders, W.R. Hersh, “A systematic literature review of automated clinical coding and classification systems,” J. Am. Med. Inform. Assoc., vol. 17, no. 6, 2010, pp. 646–651, doi: 10.1136/jamia.2009.001024. Search in Google Scholar

[8] A.E.W. Johnson, T.J. Pollard, L. Shen, L.-W.H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L.A. Celi, R.G. Mark, MIMIC-III, “A freely accessible critical care database,” Sci. Data, vol. 3, 2016, p. 160035, doi: 10.1038/sdata.2016.35. Search in Google Scholar

[9] A. Perotte, R. Pivovarov, K. Natarajan, N. Weiskopf, F. Wood, N. Elhadad, “Diagnosis code assignment: models and evaluation metrics,” J. Am. Med. Inform. Assoc., vol. 21, no. 2, 2014, pp. 231–237, doi: 10.1136/amiajnl-2013-002159. Search in Google Scholar

[10] M. Subotin, A.R. Davis, “A System for Predicting ICD-10-PCS Codes from Electronic Health Records,” Workshop on BioNLP (BioNLP), 2014, pp. 59–67. Search in Google Scholar

[11] S. Abhyankar, D. Demner-Fushman, F. Callaghan, “Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis,” J. Am. Med. Inform. Assoc., vol. 21, no. 5, 2014, pp. 801–807. Search in Google Scholar

[12] J. Pathak, K.R. Bailey, C.E. Beebe, S. Bethard, D.S. Carrell, P.J. Chen, et al, “Normalization and standardization of electronic health records for high-throughput phenotyping: The SHARPn consortium,” J. Am. Med. Inform. Assoc., vol. 20, no. e2, 2013, pp. e341–e348, doi: 10.1136/amiajnl-2013-001939. Search in Google Scholar

[13] O. Bodenreider, “The Unified Medical Language System (UMLS): Integrating biomedical terminology,” Nucl. Acids Res., vol. 32, suppl. 1, 2004, pp. D267–D270, doi: 10.1093/nar/gkh061. Search in Google Scholar

[14] RIZIV, Rijksinstituut voor ziekte- en invalidite itsuitkeringen nomenclature, http://www.riziv.fgov.be/NL/nomenclatuur/Paginas/default.aspx>. Search in Google Scholar

[15] E. Scheurwegs, K. Luyckx, L. Luyten, W. Daelemans, T. Van den Bulcke, “Data integration of structured and unstructured sources for assigning clinical codes to patient stays,” J. Am. Med. Inform. Assoc., vol. 23, no. e1, 2016, pp. 11–19, doi: 10.1093/jamia/ocv115. Search in Google Scholar

[16] R. Miotto, L. Li, B.A. Kidd, J.T. Dudley, “Deep patient: An unsupervised representation to predict the future of patients from the electronic health records,” Sci. Rep., vol. 6, no. April, 2016, p. 26094, doi: 10.1038/srep26094. Search in Google Scholar

[17] R. Cohen, M. Elhadad, N. Elhadad, “Redundancy in electronic health record corpora: Analysis, impact on text mining performance and mitigation strategies,” BMC Bioinform., vol. 14, no. 1, 2013, p. 10, doi: 10.1186/1471-2105-14-10. Search in Google Scholar

[18] S.M. Vieira, L.F. Mendonça, G.J. Farinha, J.M.C. Sousa, “Modified binary {PSO} for feature selection using {SVM} applied to mortality prediction of septic patients,” Appl. Soft Comput., vol. 13, no. 8, 2013, pp. 3494–3504. Search in Google Scholar

[19] T. Botsis, M.D. Nguyen, E.J. Woo, M. Markatou, R. Ball, “Text mining for the Vaccine Adverse Event Reporting System: Medical text classification using informative feature selection,” J. Am. Med. Inform. Assoc., vol. 18, no. 5, 2011, pp. 631–638. Search in Google Scholar

[20] I. Guyon, A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res., vol. 3, 2003, pp. 1157–1182, doi: 10.1016/j.aca.2011.07.027.arXiv:1111.6189v1. Search in Google Scholar

[21] R. Kohavi, G.H. John, “Wrappers for feature subset selection,” Artif. Intell., vol. 97, no. 1–2, 1997, pp. 273–324, doi: 10.1016/S0004-3702(97)00043-X. Search in Google Scholar

[22] H.C. Peng, F. Long, C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min redundancy,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, 2005, pp. 1226–1238. Search in Google Scholar

[23] S. Fu, M.C. Desmarais, “Markov blanket based feature selection: a review of past decade,” Proceedings of the World Congress on Engineering 2010, vol. I, 2010, pp. 321–328. Search in Google Scholar

[24] I. Tsamardinos, L.E. Brown, C.F. Aliferis, “The max-min hill-climbing Bayesian network structure learning algorithm,” Mach. Learn., vol. 65, no. 1, 2006, pp. 31–78, doi: 10.1007/s10994-006-6889-7. Search in Google Scholar

[25] A.E.W. Johnson, T.J. Pollard, L. Shen, L. Lehman, M. Feng, M. Ghassemi, et al, “MIMIC-III, A freely accessible critical care database,” Scientific Data, 2016, doi: 10.1038/sdata.2016.35. Available from: http://www.nature.com/articles/sdata201635. Search in Google Scholar

[26] B. Dolhansky, Artificial Neural Networks: Linear Multiclass Classification (Part 3) September 27, 2013 in ml primers, neural networks, http://www.briandolhansky.com/blog/2013/9/23/artificial-neural-nets-linear-multiclass-part-3. Search in Google Scholar

[27] E. Scheurwegs, B. Cule, K. Luyckx, L. Luyten, W. Daelemans, “Selecting relevant features from the electronic health record for clinical code prediction,” Journal of Biomedical Informatics, vol. 74, 2017, pp. 92–103. Search in Google Scholar

[28] S.M. Zhou, F. Fernandez-Gutierrez, J. Kennedy, R. Cooksey, M. Atkinson, S. Denaxas, C. Sudlow, “Defining disease phenotypes in primary care electronic health records by a machine learning approach: a case study in identifying rheumatoid arthritis,” PloS one, vol. 11, no. 5, 2016, p. e0154515. Search in Google Scholar

[29] T. Lingren, P. Chen, J. Bochenek, F. Doshi-Velez, P. Manning-Courtney, J. Bickel, et al, “Electronic health record based algorithm to identify patients with autism spectrum disorder,” PloS one, vol. 11, no. 7, 2016, p. e0159621. Search in Google Scholar

[30] J. Zhao, P. Papapetrou, L. Asker, H. Boström, “Learning from heterogeneous temporal data in electronic health records,” Journal of Biomedical Informatics, vol. 65, pp. 105–119. Search in Google Scholar

[31] S. Pai, S. Hui, R. Isserlin, M.A. Shah, H. Kaka, G.D. Bader, “netDx: interpretable patient classification using integrated patient similarity networks,” Molecular Systems Biology, vol. 15, no. 3, 2016, p. e8497. Search in Google Scholar

[32] P. Courtiol, C. Maussion, M. Moarii, E. Pronier, S. Pilcer, M. Sefta, T. Clozel, “Deep learning-based classification of mesothelioma improves prediction of patient outcome,” Nature Medicine, vol. 25, no. 10, 2019, pp. 1519–1525. Search in Google Scholar

[33] B. Dolhansky, 2013, Artificial neural networks: Mathematics of backpropagation (part 4), https://www.briandolhansky.com/blog/2013/9/27/artificial-neural-networks-backpropagationpart-4. Search in Google Scholar

[34] B. Dolhansky, J.A. Bilmes, “Deep submodular functions: Definitions and learning. Advances in Neural Information Processing Systems,” vol. 29, 2016, pp. 3404–3412. Search in Google Scholar

[35] G. Paliwal, A. Bunglowala, P. Kanthed, “An architectural design study of electronic healthcare record systems with associated context parameters on MIMIC III,” Health and Technology, 2022, pp. 1–15. Search in Google Scholar

[36] H. Sharma, C. Mao, Y. Zhang, H. Vatani, L. Yao, Y. Zhong, Y. Luo, “Developing a portable natural language processing based phenotyping system,” BMC Medical Informatics and Decision Making, vol. 19, no. 3, pp. 79–87. Search in Google Scholar

[37] A.P. Reimer, A. Milinovich, “Using UMLS for electronic health data standardization and database design,” Journal of the American Medical Informatics Association, vol. 27, no. 10, 2020, pp. 1520–1528. Search in Google Scholar

[38] H. Zhang, T. Lyu, P. Yin, S. Bost, X. He, Y. Guo, J. Bian, “A scoping review of semantic integration of health data and information,” International Journal of Medical Informatics, 2022, p. 104834. Search in Google Scholar

[39] D. Yuvaraj, A.M.U. Ahamed, M. Sivaram, “A study on the role of natural language processing in the healthcare sector,” Materials Today: Proceedings, 2021. Search in Google Scholar

[40] H. Sharma, C. Mao, Y. Zhang, H. Vatani, L. Yao, Y. Zhong, et al, “Developing a portable natural language processing based phenotyping system,” BMC Medical Informatics and Decision Making, vol. 19, no. 3, 2019, pp. 79–87. Search in Google Scholar

[41] S. Moosavinasab, E. Sezgin, H. Sun, J. Hoffman, Y. Huang, S. Lin, “DeepSuggest: Using neural networks to suggest related keywords for a comprehensive search of clinical notes,” ACI open, vol. 5, no. 01, 2021, pp. e1–e12. Search in Google Scholar

[42] B. Wang, Y. Sun, Y. Chu, D. Zhao, Z. Yang, J. Wang, “Refining electronic medical records representation in manifold subspace,” BMC bioinformatics, vol. 23, no. 1, pp. 1–17. Search in Google Scholar

[43] E.O. Omuya, G.O. Okeyo, M.W. Kimwele, “Feature selection for classification using principal component analysis and information gain,” Expert Systems with Applications, vol. 174, 2021, p. 114765. Search in Google Scholar

[44] U.A. Bhatti, L. Yuan, Z. Yu, S.A. Nawaz, A. Mehmood, M.A. Bhatti, et al, “Predictive Data Modeling Using sp-kNN for Risk Factor Evaluation in Urban Demographical Healthcare Data,” Journal of Medical Imaging and Health Informatics, vol. 11, no. 1, 2021, pp. 7–14. Search in Google Scholar

[45] N. Wang, Y. Huang, H. Liu, Z. Zhang, L. Wei, X. Fei, H. Chen, “Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records,” BMC medical informatics and decision making, vol. 21, no. 2, 2021, pp. 1–13. Search in Google Scholar

[46] C. Comito, D. Falcone, A. Forestiero, Diagnosis prediction based on similarity of patients physiological parameters. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 487–494. Search in Google Scholar

[47] W.M. Shaban, A. Rabie, A.I. Saleh, M.A. Abo-Elsoud, “Accurate detection of COVID-19 patients based on distance biased Naive Bayes (DBNB) classification strategy,” Pattern Recognition, vol. 119, 2021, p. 108110. Search in Google Scholar

[48] V. Jackins, S. Vimal, M. Kaliappan, M.Y. Lee, “AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes,” The Journal of Supercomputing, 77(5), 2021, pp. 5198–5219. Search in Google Scholar

[49] J.M. Bae, “The clinical decision analysis using decision tree,” Epidemiology and health, vol. 36, 2021. Search in Google Scholar

[50] S.H. Rukmawan, F.R. Aszhari, Z. Rustam, J. Pandelaki, “Cerebral infarction classification using the k-nearest neighbor and naive bayes classifier,” Journal of Physics: Conference Series, Vol. 1752, No. 1, 2021, p. 012045. Search in Google Scholar

[51] L.I. Qi, “Patient classification with ensemble treebased modelling for decision support in acute clinical care settings,” Doctoral dissertation, RMIT University. Search in Google Scholar

[52] A. Singh, A. Dhillon, N. Kumar, M. Hossain, G. Muhammad, M. Kumar, “eDiaPredict: An Ensemble-based framework for diabetes prediction,” ACM Transactions on Multimidia Computing Communications and Applications, vol. 17, no. 2s, 2021, pp. 1–26. Search in Google Scholar

[53] T. Razzaghi, O. Roderick, I. Safro, N. Marko, “Multilevel weighted support vector machine for classification on healthcare data with missing values,”PloS one, vol. 11, no. 5, 2021, p. e0155119. Search in Google Scholar

[54] D.M. Abdullah, A.M. Abdulazeez, “Machine Learning Applications based on SVM Classification A Review,” Qubahan Academic Journal, vol. 1, no. 2, 2021, pp. 81–90. Search in Google Scholar

[55] N. Shahid, T. Rappon, W. Berta, “Applications of artificial neural networks in health care organizational decision-making: A scoping review,” PloS one, vol. 14, no. 2, 2019, p. e0212356. Search in Google Scholar

[56] W. Liu, Z. Wang, N. Zeng, F.E. Alsaadi, X. Liu, “A PSO-based deep learning approach to classifying patients from emergency departments,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 7, 2021, pp. 1939-1948. Search in Google Scholar

[57] B.S. Panchbhai, V.M. Pathak, “A Systematic Review of Natural Language Processing in Healthcare,” Journal of Algebraic Statistics, vol. 13, no. 1, 2022, pp. 682–707. Search in Google Scholar

[58] B. Zhou, G. Yang, Z. Shi, S. Ma, “Natural language processing for smart healthcare,” IEEE Reviews in Biomedical Engineering, 2022. Search in Google Scholar

[59] K.M. Al-Aidaroos, A.A. Bakar, Z. Othman, “Medical data classification with Naive Bayes approach,” Information Technology Journal, vol. 11, no. 9, p. 1166. Search in Google Scholar

[60] I. Korobiichuk, A. Ladanyuk, R. Boiko, S. Hrybkov, “Features of Control Processes in Organizational-Technical (Technological) Systems of Continuous Type,” Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 14, no. 4, pp. 11–17. doi: 10.14313/JAMRIS/4-2020/39. Search in Google Scholar

[61] S. Yousfi, M. Rhanoui, M. Mikram, “Comparative Study of CNN and LSTM for Opinion Mining in Long Text,” Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 14, no. 3, pp. 50–55. doi: 10.14313/JAMRIS/3-2020/34. Search in Google Scholar

[62] A. Ndayikengurukiye, A. Ez-zahout, A. Aboubakr, Y. Charkaoui, O. Fouzia, “Resource Optimisation in Cloud Computing: Comparative Study of Algorithms Applied to Recommendations in a Big Data Analysis Architecture,” Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 15, no. 4, pp. 65–75. doi: 10.14313/JAMRIS/4-2021/28. Search in Google Scholar