Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/15658
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMayya V.
dc.contributor.authorSowmya Kamath S.
dc.contributor.authorKrishnan G.S.
dc.contributor.authorGangavarapu T.
dc.date.accessioned2021-05-05T10:27:37Z-
dc.date.available2021-05-05T10:27:37Z-
dc.date.issued2021
dc.identifier.citationFuture Generation Computer Systems Vol. 118 , , p. 374 - 391en_US
dc.identifier.urihttps://doi.org/10.1016/j.future.2021.01.013
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15658-
dc.description.abstractEffective coding of patient records in hospitals is an essential requirement for epidemiology, billing, and managing insurance claims. The prevalent practice of manual coding, carried out by trained medical coders, is error-prone and time-consuming. Mitigating this labor-intensive process by developing diagnostic coding systems built on patients’ Electronic Medical Records (EMRs) is vital. However, developing nations with low digitization rates have limited availability of structured EMRs, thereby necessitating a need for systems that leverage unstructured data sources. Despite the rich clinical information available in such unstructured data, modeling them is complex, owing to the variety and sparseness of diagnostic codes, complex structural and temporal nature of summaries, and prolific use of medical jargon. This work proposes a context-attentive network to facilitate automatic diagnostic code assignment as a multi-label classification problem. The proposed model facilitates information aggregation across a patient's discharge summary via multi-channel, variable-sized convolutional filters to extract multi-granular snippets. The attention mechanism enables selecting vital segments in those snippets that map to the clinical codes. The model's superior performance underscores its effectiveness compared to the state-of-the-art on the MIMIC-III database. Additionally, experimental validation using the CodiEsp dataset exhibited the model's interpretability and explainability. © 2021 Elsevier B.V.en_US
dc.titleMulti-channel, convolutional attention based neural model for automated diagnostic coding of unstructured patient discharge summariesen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.