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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

9798350337488

ISSN

2156-1133

e-ISSN

—

Počet stran výsledku

7

Strana od-do

4172-4178

Název nakladatele

IEEE

Místo vydání

Istanbul

Místo konání akce

Istanbul

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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