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.
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.
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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: Métais, 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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