Unsupervised extraction, classification and visualization of clinical note segments using the MIMIC-III dataset

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00133337" target="_blank" >RIV/00216224:14330/23:00133337 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/BIBM58861.2023.10385342" target="_blank" >http://dx.doi.org/10.1109/BIBM58861.2023.10385342</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM58861.2023.10385342" target="_blank" >10.1109/BIBM58861.2023.10385342</a>

Result language

angličtina

Original language name

Unsupervised extraction, classification and visualization of clinical note segments using the MIMIC-III dataset

Original language description

This paper presents a text-mining approach to extracting and organizing segments from unstructured clinical notes in an unsupervised way. Our work is motivated by the real challenge of poor semantic integration between clinical notes produced by different doctors, departments, or hospitals. This can lead to clinicians overlooking important information, especially for patients with long and varied medical histories. This work extends a previous approach developed for Czech breast cancer patients and validates it on the publicly accessible MIMIC-III English dataset, demonstrating its universal and language-independent applicability. Our work is a stepping stone to a broad array of downstream tasks, such as summarizing or integrating patient records, extracting structured information, or computing patient embeddings. Additionally, the paper presents a clustering analysis of the latent space of note segment types, using hierarchical clustering and an interactive treemap visualization. The presented results demonstrate that this approach generalizes well for MIMIC and English.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

ISBN

9798350337488

ISSN

2156-1133

e-ISSN

—

Number of pages

7

Pages from-to

4172-4178

Publisher name

IEEE

Place of publication

Istanbul

Event location

Istanbul

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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