Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F22%3A00077664" target="_blank" >RIV/00159816:_____/22:00077664 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14330/22:00125875

Result on the web

<a href="https://www.mdpi.com/2075-4426/12/6/869" target="_blank" >https://www.mdpi.com/2075-4426/12/6/869</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/jpm12060869" target="_blank" >10.3390/jpm12060869</a>

Result language

angličtina

Original language name

Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases

Original language description

Electronic health records naturally contain most of the medical information in the form of doctor&apos;s notes as unstructured or semi-structured texts. Current deep learning text analysis approaches allow researchers to reveal the inner semantics of text information and even identify hidden consequences that can offer extra decision support to doctors. In the presented article, we offer a new automated analysis of Polish summary texts of patient hospitalizations. The presented models were found to be able to predict the final diagnosis with almost 70% accuracy based just on the patient&apos;s medical history (only 132 words on average), with possible accuracy increases when adding further sentences from hospitalization results; even one sentence was found to improve the results by 4%, and the best accuracy of 78% was achieved with five extra sentences. In addition to detailed descriptions of the data and methodology, we present an evaluation of the analysis using more than 50,000 Polish cardiology patient texts and dive into a detailed error analysis of the approach. The results indicate that the deep analysis of just the medical history summary can suggest the direction of diagnosis with a high probability that can be further increased just by supplementing the records with further examination results.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30300 - Health sciences

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

JOURNAL OF PERSONALIZED MEDICINE

ISSN

2075-4426

e-ISSN

2075-4426

Volume of the periodical

12

Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

nestrankovano

UT code for WoS article

000818311800001

EID of the result in the Scopus database

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