Work

Data format optimization for subsequent analyses of hospitalization data

Year: 2025

Type: conference-paper

Abstract: Contemporary healthcare systems generate large amounts of data, related to patient health, diagnostics, medical procedures, and more. Processing and analyzing the available data make it possible to identify trends, correlations, and other interconnections that can serve as a basis for subsequent management decisions. Based on data and its correct interpretation the overall quality of public healthcare can be improved. The main objective of the present research is to examine medical data from hospitals in Bulgarian healthcare with a focus on subsequent data analysis. We use descriptive statistics to discover patient admission tendencies and use predictive analysis to forecast future admission trends. Our study presents hospitalization trend analyses, using descriptive statistics and predictive analysis. The obtained insights can be used to improve patient care by predicting hospitalization rates and optimizing patient planning procedures and resource allocation.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2025.07.031

Authors: Kalinka Kaloyanova, Elitsa Kaloyanova, Irina Atanasova

Institutions: Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Sirma Group (Bulgaria)

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: European Commission