Abstract
Accurate risk management and disease prediction are vital in intensive care units to channel prompt care to patients in critical conditions and aid medical personnel in effective decision making. Clinical nursing notes document subjective assessments and crucial information of a patient’s state, which is mostly lost when transcribed into Electronic Medical Records (EMRs). The Clinical Decision Support Systems (CDSSs) in the existing body of literature are heavily dependent on the structured nature of EMRs. Moreover, works which aim at benchmarking deep learning models are limited. In this paper, we aim at leveraging the underutilized treasure-trove of patient-specific information present in the unstructured clinical nursing notes towards the development of CDSSs. We present a fuzzy token-based similarity approach to aggregate voluminous clinical documentations of a patient. To structure the free-text in the unstructured notes, vector space and coherence-based topic modeling approaches that capture the syntactic and latent semantic information are presented. Furthermore, we utilize the predictive capabilities of deep neural architectures for disease prediction as ICD-9 code group. Experimental validation revealed that the proposed Term weighting of nursing notes AGgregated using Similarity (TAGS) model outperformed the state-of-the-art model by 5% in AUPRC and 1.55% in AUROC.
Keywords
This work is funded by the Government of India’s DST-SERB Early Career Research Grant (ECR/2017/001056) to Sowmya Kamath S.
T. Gangavarapu and A. Jayasimha—contributed equally to this work.
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Notes
- 1.
International Statistical Classification of Diseases and Related Health Problems.
- 2.
The code ranges used for mapping can be found at http://tdrdata.com/ipd/ipd_SearchForICD9CodesAndDescriptions.aspx.
References
Baumel, T., Nassour-Kassis, J., Cohen, R., Elhadad, M., Elhadad, N.: Multi-label classification of patient notes a case study on ICD code assignment. arXiv preprint arXiv:1709.09587 (2017)
Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. Proc. GSCL, 31–40 (2009)
Collins, S.A., Cato, K., Albers, D., Scott, K., et al.: Relationship between nursing documentation and patients’ mortality. Am. J. Crit. Care 22(4), 306–313 (2013)
Dubois, S., Romano, N., Kale, D.C., Shah, N., Jung, K.: Learning effective representations from clinical notes. arXiv preprint arXiv:1705.07025 (2017)
Harutyunyan, H., Khachatrian, H., Kale, D.C., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. arXiv preprint arXiv:1703.07771 (2017)
Henry, J., Pylypchuk, Y., Searcy, T., Patel, V.: Adoption of electronic health record systems among us non-federal acute care hospitals: 2008-2015. ONC Data Brief 35, 1–9 (2016)
Jo, Y., Lee, L., Palaskar, S.: Combining LSTM and latent topic modeling for mortality prediction. arXiv preprint arXiv:1709.02842 (2017)
Johnson, A.E., Pollard, T.J., Mark, R.G.: Reproducibility in critical care: a mortality prediction case study. In: Machine Learning for Healthcare Conference, pp. 361–376 (2017)
Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)
Krishnan, G.S., Sowmya Kamath, S.: A supervised learning approach for ICU mortality prediction based on unstructured electrocardiogram text reports. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds.) NLDB 2018. LNCS, vol. 10859, pp. 126–134. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91947-8_13
Larkey, L.S., Croft, W.B.: Automatic assignment of ICD9 codes to discharge summaries. Technical report, University of Massachusetts at Amherst, Amherst, MA (1995)
Pirracchio, R.: Mortality prediction in the ICU based on MIMIC-II results from the super ICU learner algorithm (SICULA) project. Secondary Analysis of Electronic Health Records, pp. 295–313. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43742-2_20
Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. J. Biomed. Inform. 83, 112–134 (2018)
Wang, Y., et al.: MedSTS: a resource for clinical semantic textual similarity. Lang. Resour. Eval., 1–16 (2018)
Waudby-Smith, I.E., Tran, N., Dubin, J.A., Lee, J.: Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients. PLoS ONE 13(6), e0198687 (2018)
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Gangavarapu, T., Jayasimha, A., Krishnan, G.S., S., S.K. (2019). TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes. In: MĂ©tais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_16
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