Deciphering Clinical Narratives - Augmented Intelligence for Decision Making in Healthcare Sector

• Autorzy: Dey Lipika , Jana Sudeshna , Dasgupta Tirthankar , Gupta Tanay

Identyfikatory

DOI

10.15439/2023F3385

• Języki publikacji: EN

Abstrakty

EN

Clinical notes that describe details about diseases, symptoms, treatments and observed reactions of patients to them, are valuable resources to generate insights about the effectiveness of treatments. Their role in designing better clinical decision making systems is being increasingly acknowledged. However, availability of clinical notes is still an issue due to privacy violation concerns. Hence most of the work done are on small datasets and neither the power of machine learning is fully utilized, nor is it possible to vaidate the models properly. With the availability of Medical Information Mart for Intensive Care (MIMIC-III v1.4) dataset for researchers though, the problem has been somewhat eased. In this paper we have presented an overview of our earlier work on designing deep neural models for prediction of outcomes and hospital stay for patients using MIMIC data. We have also presented new work on patient stratification and explanation generation for patient cohorts. This is early work targeted towards studying trajectories for treatment for different cohorts of patients, which can ultimately lead to discovery of low-risk models for individual patients to ensure better outcomes.

Słowa kluczowe

EN

clinical notes; BioNER; clustering; anomaly detection; autoencoder; Shapley value

PL

notatki kliniczne; BioNER; grupowanie; wykrywanie anomalii; autoenkoder; wartość Shapleya

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 11--24
• Opis fizyczny: Bibliogr. 51 poz., tab., wykr., il.

Twórcy

autor

Dey Lipika

• TCS Research, India

autor

Jana Sudeshna

• TCS Research, India

autor

Dasgupta Tirthankar

• TCS Research, India

autor

Gupta Tanay

• TCS Research, India

Bibliografia

• [1] H. Dalianis, Clinical text mining: Secondary use of electronic patient records. Springer Nature, 2018.
• [2] A. E. Johnson, T. J. Pollard, L. Shen, H. L. Li-Wei, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. A. Celi, and R. G. Mark, “Mimic-iii, a freely accessible critical care database,” Scientific data, vol. 3, no. 1, pp. 1–9, 2016.
• [3] B. Percha, “Modern clinical text mining: a guide and review,” Annual review of biomedical data science, vol. 4, pp. 165–187, 2021.
• [4] T. L. Rodziewicz and J. E. Hipskind, “Medical error prevention,” StatPearls. Treasure Island (FL): StatPearls Publishing, 2020.
• [5] K. Stone, R. Zwiggelaar, P. Jones, and N. Mac Parthal´ain, “A systematic review of the prediction of hospital length of stay: Towards a unified framework,” PLOS Digital Health, vol. 1, no. 4, p. e0000017, 2022.
• [6] K.-C. Chang, M.-C. Tseng, H.-H. Weng, Y.-H. Lin, C.-W. Liou, and T.-Y. Tan, “Prediction of length of stay of first-ever ischemic stroke,” Stroke, vol. 33, no. 11, pp. 2670–2674, 2002.
• [7] A. Lim, “Statistic methods for modeling incidence of infectious diseases mortality and length of stay in hospital fot patients dying in southern thailand,” Ph.D. dissertation, Prince of Songkla University, Pattani Campus, 2009.
• [8] D. A. Huntley, D. W. Cho, J. Christman, and J. G. Csernansky, “Predicting length of stay in an acute psychiatric hospital,” Psychiatric services, vol. 49, no. 8, pp. 1049–1053, 1998.
• [9] T. Gentimis, A. Ala’J, A. Durante, K. Cook, and R. Steele, “Predicting hospital length of stay using neural networks on mimic iii data,” in 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, 2017, pp. 1194–1201.
• [10] K. Alghatani, N. Ammar, A. Rezgui, A. Shaban-Nejad et al., “Predicting intensive care unit length of stay and mortality using patient vital signs: Machine learning model development and validation,” JMIR Medical Informatics, vol. 9, no. 5, p. e21347, 2021.
• [11] L. Su, Z. Xu, F. Chang, Y. Ma, S. Liu, H. Jiang, H. Wang, D. Li, H. Chen, X. Zhou et al., “Early prediction of mortality, severity, and length of stay in the intensive care unit of sepsis patients based on sepsis 3.0 by machine learning models,” Frontiers in Medicine, vol. 8, p. 883, 2021.
• [12] E. Rocheteau, P. Li`o, and S. Hyland, “Predicting length of stay in theintensive care unit with temporal pointwise convolutional networks,” arXiv preprint arXiv:2006.16109, 2020.
• [13] H. Harutyunyan, H. Khachatrian, D. C. Kale, G. Ver Steeg, and A. Galstyan, “Multitask learning and benchmarking with clinical time series data,” Scientific data, vol. 6, no. 1, pp. 1–18, 2019.
• [14] S. Khadanga, K. Aggarwal, S. Joty, and J. Srivastava, “Using clinical notes with time series data for icu management,” arXiv preprint arXiv:1909.09702, 2019.
• [15] B. van Aken, J.-M. Papaioannou, M. Mayrdorfer, K. Budde, F. A.Gers, and A. L¨oser, “Clinical outcome prediction from admission notes using self-supervised knowledge integration,” arXiv preprint arXiv:2102.04110, 2021.
• [16] S. Jana, T. Dasgupta, and L. Dey, “Using nursing notes to predict length of stay in icu for critically ill patients,” in Multimodal AI in healthcare: A paradigm shift in health intelligence. Springer, 2022, pp. 387–398.
• [17] S. Jana, T. Dasgupta, and L. Dey, “Predicting medical events and icu requirements using a multi-modal multiobjective transformer network,” Experimental Biology and Medicine, vol. 247, no. 22, pp. 1988–2002, 2022.
• [18] T. J. Pollard, A. E. Johnson, J. D. Raffa, L. A. Celi, R. G. Mark, and O. Badawi, “The eicu collaborative research database, a freely available multi-center database for critical care research,” Scientific data, vol. 5, no. 1, pp. 1–13, 2018.
• [19] Y. Peng, S. Yan, and Z. Lu, “Transfer learning in biomedical natural language processing: an evaluation of bert and elmo on ten benchmarking datasets,” arXiv preprint arXiv:1906.05474, 2019.
• [20] E. Alsentzer, J. R. Murphy, W. Boag, W.-H. Weng, D. Jin, T. Naumann, and M. McDermott, “Publicly available clinical bert embeddings,” arXiv preprint arXiv:1904.03323, 2019.
• [21] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700–4708.
• [22] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
• [23] X. Li and K.-C. Wong, “Evolutionary multiobjective clustering and its applications to patient stratification,” IEEE transactions on cybernetics, vol. 49, no. 5, pp. 1680–1693, 2018.
• [24] R. W. Grant, J. McCloskey, M. Hatfield, C. Uratsu, J. D. Ralston, E. Bayliss, and C. J. Kennedy, “Use of latent class analysis and k-means clustering to identify complex patient profiles,” JAMA network open, vol. 3, no. 12, pp. e2 029 068–e2 029 068, 2020.
• [25] N. Alexander, D. C. Alexander, F. Barkhof, and S. Denaxas, “Identifying and evaluating clinical subtypes of alzheimer’s disease in care electronic health records using unsupervised machine learning,” BMC Medical Informatics and Decision Making, vol. 21, no. 1, pp. 1–13, 2021.
• [26] F. Angelini, P. Widera, A. Mobasheri, J. Blair, A. Struglics, M. Uebelhoer, Y. Henrotin, A. C. Marijnissen, M. Kloppenburg, F. J. Blanco et al., “Osteoarthritis endotype discovery via clustering of biochemical marker data,” Annals of the Rheumatic Diseases, vol. 81, no. 5, pp. 666–675, 2022.
• [27] S. V. Bhavani, L. Xiong, A. Pius, M. Semler, E. T. Qian, P. A. Verhoef, C. Robichaux, C. M. Coopersmith, and M. M. Churpek, “Comparison of time series clustering methods for identifying novel subphenotypes of patients with infection,” Journal of the American Medical Informatics Association, vol. 30, no. 6, pp. 1158–1166, 2023.
• [28] T. Chen, X. Li, Y. Li, E. Xia, Y. Qin, S. Liang, F. Xu, D. Liang, C. Zeng, and Z. Liu, “Prediction and risk stratification of kidney outcomes in iga nephropathy,” American journal of kidney diseases, vol. 74, no. 3, pp. 300–309, 2019.
• [29] F. Kanwal, J. H. Shubrook, L. A. Adams, K. Pfotenhauer, V. W.-S. Wong, E. Wright, M. F. Abdelmalek, S. A. Harrison, R. Loomba, C. S. Mantzoros et al., “Clinical care pathway for the risk stratification and management of patients with nonalcoholic fatty liver disease,” Gastroenterology, vol. 161, no. 5, pp. 1657–1669, 2021.
• [30] T. M. Seinen, E. A. Fridgeirsson, S. Ioannou, D. Jeannetot, L. H. John, J. A. Kors, A. F. Markus, V. Pera, A. Rekkas, R. D. Williams et al., “Use of unstructured text in prognostic clinical prediction models: a systematic review,” Journal of the American Medical Informatics Association, vol. 29, no. 7, pp. 1292–1302, 2022.
• [31] O. Bodenreider, “The unified medical language system (umls): integrating biomedical terminology,” Nucleic acids research, vol. 32, no. suppl 1, pp. D267–D270, 2004.
• [32] M. Neumann, D. King, I. Beltagy, and W. Ammar, “Scispacy: Fast and robust models for biomedical natural language processing. arxiv 2019,” arXiv preprint arXiv:1902.07669.
• [33] A. R. Aronson, “Metamap: Mapping text to the umls metathesaurus,” Bethesda, MD: NLM, NIH, DHHS, vol. 1, p. 26, 2006.
• [34] W. W. Chapman, W. Bridewell, P. Hanbury, G. F. Cooper, and B. G. Buchanan, “A simple algorithm for identifying negated findings and diseases in discharge summaries,” Journal of biomedical informatics, vol. 34, no. 5, pp. 301–310, 2001.
• [35] B. McInnes, Y. Liu, T. Pedersen, G. Melton, and S. Pakhomov, “Umls:: similarity: Measuring the relatedness and similarity of biomedical concepts.” Association for Computational Linguistics, 2013.
• [36] P. A. Hall and G. R. Dowling, “Approximate string matching,” ACM computing surveys (CSUR), vol. 12, no. 4, pp. 381–402, 1980.
• [37] R. Haldar and D. Mukhopadhyay, “Levenshtein distance technique in dictionary lookup methods: An improved approach,” arXiv preprint arXiv:1101.1232, 2011.
• [38] Y. Wang, H. Yao, and S. Zhao, “Auto-encoder based dimensionality reduction,” Neurocomputing, vol. 184, pp. 232–242, 2016.
• [39] W. Wang, Y. Huang, Y. Wang, and L. Wang, “Generalized autoencoder: A neural network framework for dimensionality reduction,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2014, pp. 490–497.
• [40] D. Bank, N. Koenigstein, and R. Giryes, “Autoencoders,” arXiv preprint arXiv:2003.05991, 2020.
• [41] J. A. Hartigan and M. A. Wong, “Algorithm as 136: A k-means clustering algorithm,” Journal of the royal statistical society. series c (applied statistics), vol. 28, no. 1, pp. 100–108, 1979.
• [42] T. M. Kodinariya, P. R. Makwana et al., “Review on determining number of cluster in k-means clustering,” International Journal, vol. 1, no. 6, pp. 90–95, 2013.
• [43] L. Merrick and A. Taly, “The explanation game: Explaining machine learning models using shapley values,” in Machine Learning and Knowledge Extraction: 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25–28, 2020, Proceedings 4. Springer, 2020, pp. 17–38.
• [44] A. Cutler, D. R. Cutler, and J. R. Stevens, “Random forests,” Ensemble machine learning: Methods and applications, pp. 157–175, 2012.
• [45] I. Ekanayake, D. Meddage, and U. Rathnayake, “A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using shapley additive explanations (shap),” Case Studies in Construction Materials, vol. 16, p. e01059, 2022.
• [46] S. Cohen, E. Ruppin, and G. Dror, “Feature selection based on the shapley value,” other words, vol. 1, p. 98Eqr, 2005.
• [47] A. Sharma and E. Kiciman, “Dowhy: An end-to-end library for causal inference,” arXiv preprint arXiv:2011.04216, 2020.
• [48] P. C. Austin, “An introduction to propensity score methods for reducing the effects of confounding in observational studies,” Multivariate behavioral research, vol. 46, no. 3, pp. 399–424, 2011.
• [49] A. Sharma, V. Syrgkanis, C. Zhang, and E. Kıcıman, “Dowhy: Addressing challenges in expressing and validating causal assumptions,” arXiv preprint arXiv:2108.13518, 2021.
• [50] L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research, vol. 9, no. 11, 2008.
• [51] J. Ye, X. Chen, N. Xu, C. Zu, Z. Shao, S. Liu, Y. Cui, Z. Zhou, C. Gong, Y. Shen et al., “A comprehensive capability analysis of gpt-3 and gpt-3.5 series models,” arXiv preprint arXiv:2303.10420, 2023.

Uwagi

1. Main Track Invited Contributions

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).