Patient Case Similarity


Authors : varsha V.; Nishanth P H.; Chethan Jadav M.; Rohith M.; Vineetha B.

Volume/Issue : Volume 10 - 2025, Issue 1 - January


Google Scholar : https://tinyurl.com/3mwjhccm

Scribd : https://tinyurl.com/ycyahup5

DOI : https://doi.org/10.5281/zenodo.14769409


Abstract : Patient case similarity project investigates patterns and similarities across cases requiring patience, with a focus on identifying underlying factors that influence individual or collective responses. By examining various scenarios where patience is a critical attribute—ranging from interpersonal conflicts and healthcare management to organizational problem- solving—the study employs qualitative and quantitative analyses to uncover recurring themes and behaviors. A combination of data collection methods, including surveys, interviews, and case studies, was utilized to gather insights. The findings highlight the role of emotional intelligence, situational context, and external stressors in shaping patience levels, offering practical strategies for fostering resilience and adaptive coping mechanisms. This report aims to contribute to a deeper understanding of patience as a multidimensional construct, with implications for fields such as psychology, education, and organizational leadership. This project explores the concept of patience by analyzing similarities across various cases where patience plays a central role. Patience is a critical virtue in diverse contexts such as healthcare, interpersonal relationships, education, and organizational management, yet its underlying mechanisms and influencing factors remain understudied. The study leverages a mixed-methods approach, incorporating qualitative case studies, quantitative surveys, and behavioral analysis to identify patterns and shared characteristics among cases requiring patience. Key areas of focus include the psychological and emotional factors that contribute to patience, the role of situational and environmental variables, and the impact of stressors on individuals' ability to remain patient. Findings reveal that patience is influenced by a combination of intrinsic traits, such as emotional intelligence and resilience, and extrinsic factors, such as cultural norms, time pressures, and social support. Notable similarities across cases suggest that fostering patience can be systematically approached through targeted interventions, including mindfulness practices, stress management techniques, and improved communication strategies.

References :

  1. Anis Sharafoddini, Joel A Dubin and Joon Lee, ―Patient Similarity in Prediction Models Based on Health Data‖, JMIR Med Inform 2017 Jan-Mar, 5(1): e7.
  2. LWC Chan, T Chan, LF Cheng, WS Mak, ―Machine Learning of Patient Similarity‖, Bioinformatics and Biomedicines Workshops, 2010 IEEE International Conference. Gyusoo Kim and Seulgi Lee, ―2014 Payment Research‖, Bank of Korea, Vol. 2015, No. 1, Jan. 2015.
  3. Sharafoddini, A.; Dubin, J.; Lee, J. Patient Similarity in Prediction Models Based on Health Data: A Scoping Review. JMIR Med. Inform. 2017, 5, e7.
  4. Roque, F.; Jensen, P.; Schmock, H.; Dalgaard, M.; Andreatta, M.; Hansen, T.; Søeby, K.; Bredkjær, S.; Juul, A.; Werge, T.; et al. Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts. PLoS Comput. Biol. 2011, 7, e1002141.
  5. Planet, C.; Gevaert, TO. CoINcIDE: A framework for discovery of patient subtypes across multiple datasets. Genome Med. 2016, 8, 27.
  6. Zhan, M.; Cao, S.; Qian, B.; Chang, S.; Wei, J. Low-Rank Sparse Feature Selection for Patient Similarity Learning. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain, 12–15 December 2016.
  7. Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016;6(1):1–10.
  8. Choi E, Schuetz A, Stewart WF, Sun J. Medical concept representation learning from electronic health records and its application on heart failure prediction. arXiv Prepr arXiv 160203686. 2016;
  9. Savolainen, M. J., Karlsson, A., Ronkainen, SToppila, I., Lassenius, M. I., Falconi, C. V., et al. (2021). The Gaucher Earlier Diagnosis Consensus point-scoring System (GED-C PSS): Evaluation of a Prototype in Finnish Gaucher Disease Patients and Feasibility of Screening Retrospective Electronic Health Record Data for the Recognition of Potential Undiagnosed Patients in Finland.
  10. Mol. Genet. Metab. Rep. 27, 100725. doi:10.1016/j.ymgme.2021.1 00725
  11. Barco, T. L., Kuchenbuch, M., Garcelon, N., Neuraz, A., and Nabbout, R. (2021). Improving Early Diagnosis of Rare Diseases Using Natural Language Processing in Unstructured Medical Records: an Illustration from Dravet Syndrome. Orphanet J. Rare Dis. 16, 309. doi:10.1186/s13023
  12. Weng, C., Wu, X., Luo, Z., & Boland, M. R. (2017). Using electronic health records for clinical research: The case of patient similarity. AMIA Summits on Translational Science Proceedings, 2017, 403–407
  13. Jia, Z., Zhang, H., Guo, H., & Yu, G. (2019). A Patient-similarity-based Model for Diagnostic Prediction. International Journal of Data Mining and Bioinformatics, 21(2), 174–188.
  14. Ng, K., Sun, J., Hu, J., Wang, F., & Shen, Y. (2015). Personalized Predictive Modeling and Risk Factor Identification Using Patient Similarity. AMIA Annual Symposium Proceedings, 2015, 911– 920.
  15. Zhang, R., & Wang, F. (2014). Learning patient similarities from heterogeneous data. Journal of Biomedical Informatics, 50, 71–79.
  16. Liu, Y., Chen, P. H., Krause, J., & Peng, L. (2019). How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature. JAMA, 322(18), 1806–1816.
  17. Wei, X., Zhang, Y., & Wu, J. (2022). SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding. arXiv preprint.
  18. Memarzadeh, H., & Kalankesh, L. R. (2020). Patient Similarity Analysis with Longitudinal Health Data: A Systematic Review. Journal of Medical Systems, 44(6), 1–10.
  19. Lee, J., & Maslove, D. M. (2015). Identification of Patient Risk Groups Using Electronic Medical Record Data: A Machine Learning Approach. BMC Medical Informatics and Decision Making, 15, 23.
  20. Esteban, C., Hyland, S. L., & Rätsch, G. (2017). Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs. arXiv preprint.
  21. Suad A .Alasadi, Wesam S. Bhaya, ―Review of data preprocessing techniques in data mining‖, in Journal of Engineering and Applied Sciences on September 2017.

Patient case similarity project investigates patterns and similarities across cases requiring patience, with a focus on identifying underlying factors that influence individual or collective responses. By examining various scenarios where patience is a critical attribute—ranging from interpersonal conflicts and healthcare management to organizational problem- solving—the study employs qualitative and quantitative analyses to uncover recurring themes and behaviors. A combination of data collection methods, including surveys, interviews, and case studies, was utilized to gather insights. The findings highlight the role of emotional intelligence, situational context, and external stressors in shaping patience levels, offering practical strategies for fostering resilience and adaptive coping mechanisms. This report aims to contribute to a deeper understanding of patience as a multidimensional construct, with implications for fields such as psychology, education, and organizational leadership. This project explores the concept of patience by analyzing similarities across various cases where patience plays a central role. Patience is a critical virtue in diverse contexts such as healthcare, interpersonal relationships, education, and organizational management, yet its underlying mechanisms and influencing factors remain understudied. The study leverages a mixed-methods approach, incorporating qualitative case studies, quantitative surveys, and behavioral analysis to identify patterns and shared characteristics among cases requiring patience. Key areas of focus include the psychological and emotional factors that contribute to patience, the role of situational and environmental variables, and the impact of stressors on individuals' ability to remain patient. Findings reveal that patience is influenced by a combination of intrinsic traits, such as emotional intelligence and resilience, and extrinsic factors, such as cultural norms, time pressures, and social support. Notable similarities across cases suggest that fostering patience can be systematically approached through targeted interventions, including mindfulness practices, stress management techniques, and improved communication strategies.

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