Identifying Hidden Patterns from Health Administrative Claims by Means of “HAC2Vec” Embedding

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F24%3A00377149" target="_blank" >RIV/68407700:21460/24:00377149 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-031-62520-6_6" target="_blank" >http://dx.doi.org/10.1007/978-3-031-62520-6_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-62520-6_6" target="_blank" >10.1007/978-3-031-62520-6_6</a>

Result language

angličtina

Original language name

Identifying Hidden Patterns from Health Administrative Claims by Means of “HAC2Vec” Embedding

Original language description

The field of artificial intelligence (AI) has recently seen a significant role for Generative AI, particularly large language models (LLMs), and Natural Language Processing (NLP) techniques in healthcare applications. This paper explores the utility of language technologies, in deepening the understanding of Health Administrative Claims (HAC) data, a critical healthcare data source containing codes related to healthcare services. HAC data often lack essential clinical details, making it challenging to analyze disease phases, forms and subtypes. However, distinctive patterns of codes within HAC data can potentially signify specific disease phenotypes, making language technologies valuable tools for analysis. To address this, we introduce the “HAC2vec-mean” method, which utilizes skip-gram neural networks to convert HAC sequences into numerical vectors. We employ random forest models for binary and multiclass classification tasks, achieving an Area under the Receiver Operating Characteristic Curve of 0.86 for International Classification of Diseases v10. The paper presents data visualizations indicating the effectiveness of the approach in reducing data dimensionality and identifying patterns in patient profiles. Furthermore, it highlights the potential of this approach for cohort selection and index date specification. In conclusion, our study demonstrates the potential of NLP embeddings in enhancing the analysis of HAC data. This flexible framework offers improved insights into patient journeys and healthcare conditions, mitigating the limitations associated with traditional methods. Future work includes exploring the clinical relevance of identified patterns and enhancing explainability. Overall, this research opens doors to uncovering hidden structures with prognostic and therapeutic potential within HAC data.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

30304 - Public and environmental health

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Article name in the collection

Advances in Digital Health and Medical Bioengineering, Proceedings of the 11th International Conference on E-Health and Bioengineering, EHB-2023, November 9–10, 2023, Bucharest, Romania – Volume 2: Health Technology Assessment, Biomedical Signal Processing, Medicine and Informatics

ISBN

978-3-031-62519-0

ISSN

1680-0737

e-ISSN

1433-9277

Number of pages

8

Pages from-to

45-52

Publisher name

Springer Nature Switzerland AG

Place of publication

Basel

Event location

Bucuresti

Event date

Nov 9, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001326809000006