Abstract
Similar patient retrieval has become increasingly important with the explosive growth of electronic health records (EHRs). A similar patient cohort identified from all patients can provide data-driven insights for personalized healthcare. However, the high dimensionality and heterogeneity of EHRs increase the difficulty of measuring patient similarity. How to accurately and efficiently retrieve similar patients from a large number of EHRs remains challenging. In this paper, we propose a novel similar patient retrieval method based on interactive patient labeling and automatic model updating. Combined with the knowledge and experience of physicians, it can be adaptively modified for different patients and diseases. We also develop a visual analytics system to assist patient labeling through pairwise comparisons and support outcome analysis of similar patients. The case studies on two real-world datasets in collaboration with physicians demonstrate the effectiveness and usefulness of our method.
Graphic Abstract
Similar content being viewed by others
References
Abdullah SS, Rostamzadeh N, Sedig K, Garg AX, McArthur E (2020) Visual analytics for dimension reduction and cluster analysis of high dimensional electronic health records. Informatics 7(2):17
Becker J, Friedman E (2013) Renal function status. Am J Roentgenol 200(4):827–829
Bernard J, Hutter M, Zeppelzauer M, Fellner D, Sedlmair M (2017) Comparing visual-interactive labeling with active learning: An experimental study. IEEE Trans Visual Comput Graph 24(1):298–308
Bernard J, Sessler D, May T, Schlomm T, Pehrke D, Kohlhammer J (2015) A visual-interactive system for prostate cancer cohort analysis. IEEE Comput Graph Appl 35(3):44–55
Bernard J, Ritter C, Sessler D, Zeppelzauer M, Kohlhammer J, Fellner D(2017) Visual-interactive similarity search for complex objects by example of soccer player analysis. In 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISAPP 2017, pp. 75–87,
Calisto FM, Santiago C, Nunes N, Nascimento JC (2021) Introduction of human-centric ai assistant to aid radiologists for multimodal breast image classification. International Journal of Human-Computer Studies 150:102607
Calisto FM, Santiago C, Nunes N, Nascimento JC (2022) Breastscreening-ai: Evaluating medical intelligent agents for human-ai interactions. Artificial Intelligence in Medicine 127:102285
Chegini M, Bernard J, Berger P, Sourin A, Andrews K, Schreck T (2019) Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations, clustering, and active learning. Vis Inform 3(1):9–17
Chen TH-H, Chen C-J, Yen M-F, Lu S-N, Sun C-A, Huang G-T, Yang P-M, Lee H-S, Duffy SW (2002) Ultrasound screening and risk factors for death from hepatocellular carcinoma in a high risk group in taiwan. Int J Cancer 98(2):257–261
Choi E, Xiao C, Stewart W F, Sun J (2018) Mime: multilevel medical embedding of electronic health records for predictive healthcare. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 4552–4562
Dai L, Zhu H, Liu D (2020). Patient similarity: methods and applications. arXiv preprint arXiv:2012.01976,
Du F, Plaisant C, Spring N, Shneiderman B. 2017 Finding similar people to guide life choices: Challenge, design, and evaluation. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems PP.5498-5544
Faiola A, . Newlon C (2011). Advancing critical care in the icu: a human-centered biomedical data visualization systems. In International Conference on Ergonomics and Health Aspects of Work with Computers, pp. 119–128. Springer
Fan X, Li C, Yuan X, Dong X, Liang J (2019) An interactive visual analytics approach for network anomaly detection through smart labeling. J Visual 22(5):955–971
Gotz D, Sun J, Cao N, Ebadollahi S (2011). Visual cluster analysis in support of clinical decision intelligence. In AMIA Annual Symposium Proceedings, vol. 2011, pp. 481–490. American Medical Informatics Association
Ha H, Lee J, Han H, Bae S, Son S, Hong C, Shin H, Lee K (2019) Dementia patient segmentation using emr data visualization: A design study. Int J Environ Res Public Health 16(18):3438
Joachims T (2002) Optimizing search engines using clickthrough data. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 133–142
Kwon BC, Choi M-J, Kim JT, Choi E, Kim YB, Kwon S, Sun J, Choo J (2018) Retainvis: Visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Trans Vis Comput Graph 25(1):299–309
Lee J, Maslove DM, Dubin JA (2015) Personalized mortality prediction driven by electronic medical data and a patient similarity metric. PLoS ONE 10(5):e0127428
Liu C, Wenming C, Wu S, Shen W, Jiang D, Yu Z, San Wong H (2020) Supervised graph clustering for cancer subtyping based on survival analysis and integration of multi-omic tumor data. IEEE/ACM Trans Comput Biol Bioinform 19(2):1193–1202
Ma Y, Xie T, Li J, Maciejewski R (2019) Explaining vulnerabilities to adversarial machine learning through visual analytics. IEEE Trans Vis Comput Graph 26(1):1075–1085
Ma F, Gao J, Suo Q, You Q, Zhou J, Zhang A (2018) Risk prediction on electronic health records with prior medical knowledge. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining 2018 pp. 1910-1919
McCullough PA (2008) Contrast-induced acute kidney injury. J Am College Cardiol 51(15):1419–1428
Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19(6):1236–1246
Murray L, Gopinath D, Agrawal M, Horng S, Sontag D, Karger D R (2021). Medknowts: Unified documentation and information retrieval for electronic health records. In The 34th Annual ACM Symposium on User Interface Software and Technology, pp. 1169–1183
Ng K, Sun J, Hu J, Wang F (2015) Personalized predictive modeling and risk factor identification using patient similarity. AMIA Summits Trans Sci Proceed 132–136:2015
Ozkok S, Ozkok A (2017) Contrast-induced acute kidney injury: A review of practical points. World J Nephrol 6(3):86–99
Panahiazar M, Taslimitehrani V, Pereira NL, Pathak J (2015) Using ehrs for heart failure therapy recommendation using multidimensional patient similarity analytics. Stud Health Technol Inform 210:369–373
Parimbelli E, Marini S, Sacchi L, Bellazzi R (2018) Patient similarity for precision medicine: A systematic review. J Biomed Inform 83:87–96
Perrone RD, Madias NE, Levey AS (1992) Serum creatinine as an index of renal function: new insights into old concepts. Clin Chem 38(10):1933–1953
Pikoula M, Quint JK, Nissen F, Hemingway H, Smeeth L, Denaxas S (2019) Identifying clinically important copd sub-types using data-driven approaches in primary care population based electronic health records. BMC Med Inform Decis Mak 19(1):1–14
Plaisant C, Mushlin R, Snyder A, Li J, Heller D, Shneiderman B(1998). Lifelines: using visualization to enhance navigation and analysis of patient records. In Proceedings of the AMIA Symposium, pp. 76–80. American Medical Informatics Association,
Regimbeau JM, Abdalla EK, Vauthey JN, Lauwers GY, Durand F, Nagorney DM, Ikai I, Yamaoka Y, Belghiti J (2004) Risk factors for early death due to recurrence after liver resection for hepatocellular carcinoma: results of a multicenter study. J Surg Oncol 85(1):36–41
Sarwar T, Seifollahi S, Chan J, Zhang X, Aksakalli V, Hudson I, Verspoor K, Cavedon L (2022) The secondary use of electronic health records for data mining: Data characteristics and challenges. ACM Comput Surv (CSUR) 55(2):1–40
Shahar Y, Goren-Bar D, Boaz D, Tahan G (2006) Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions. Artif Intell Med 38(2):115–135
Shen L, Zeng Q, Guo P, Huang J, Li C, Pan T, Chang B, Wu N, Yang L, Chen Q et al (2018) Dynamically prognosticating patients with hepatocellular carcinoma through survival paths mapping based on time-series data. Nat Commun 9(1):1–10
Sun J, Wang F, Hu J, Edabollahi S (2012) Supervised patient similarity measure of heterogeneous patient records. ACM SIGKDD Explor Newsl 14(1):16–24
Suo Q, Ma F, Yuan Y, Huai M, Zhong W, Gao J, Zhang A (2018) Deep patient similarity learning for personalized healthcare. IEEE Trans Nanobiosci 17(3):219–227
Suo Q, Ma F, Yuan Y, Huai M, Zhong W, Zhang A, Gao J (2017). Personalized disease prediction using a cnn-based similarity learning method. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 811–816. IEEE
Udler MS, Kim J, von Grotthuss M, Bonàs-Guarch S, Cole J B, Chiou J, C D. A. on behalf of METASTROKE, the ISGC, M. Boehnke, M. Laakso, G. Atzmon, et al (2018) Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS medicine, 15(9):e1002654,
Wang TD, Plaisant C, Quinn AJ, Stanchak R, Murphy S, Shneiderman B (2008) Aligning temporal data by sentinel events: discovering patterns in electronic health records. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp 457–466
Tashkandi A, Wiese I, Wiese L (2018) Efficient in-database patient similarity analysis for personalized medical decision support systems. Big Data Res 13:52–64
Wall E, Das S, Chawla R, Kalidindi B, Brown ET, Endert A (2017) Podium: Ranking data using mixed-initiative visual analytics. IEEE Trans Visual Comput Graph 24(1):288–297
Wang N, Huang Y, Liu H, Fei X, Wei L, Zhao X, Chen H (2019) Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records. Biomed Eng Online 18(1):1–15
Wang Q, Laramee RS (2022) Ehr star: The state-of-the-art in interactive ehr visualization. Comput Graph Forum 41(1):69–105
Wang Y, Tian Y, Tian L-L, Qian Y-M, Li J-S (2015) An electronic medical record system with treatment recommendations based on patient similarity. J Med Syst 39(5):1–9
Wang F, Hu J, Sun J (2012). Medical prognosis based on patient similarity and expert feedback. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 1799–1802. IEEE,
Xiao C, Choi E, Sun J (2018) Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assoc 25(10):1419–1428
Zhang P, Wang F, Hu J, Sorrentino R (2014) Towards personalized medicine: leveraging patient similarity and drug similarity analytics. AMIA Summits Trans Sci Proceed 132–136:2014
Zhu Z, Yin C, Qian B, Cheng Y, Wei J, Wang F (2016). Measuring patient similarities via a deep architecture with medical concept embedding. In 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 749–758. IEEE
Acknowledgements
We would like to thank Zeyu Jiang and Bin Yin of Philips Research China for their data and insightful suggestions. This research was partially supported by Philips, ZJU & TU / BrainBridge Program under Project No.BB3-2016-06, the National Natural Science Foundation of China under Grant 61972343, the Key Research and Development of Zhejiang Province under Grant 2021C03032, and the Natural Science Foundation of Zhejiang Province under Grant LQ22F020017.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, H., Dai, H., Chen, J. et al. Interactive similar patient retrieval for visual summary of patient outcomes. J Vis 26, 577–592 (2023). https://doi.org/10.1007/s12650-022-00898-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12650-022-00898-9