Putri, L.D.; Girsang, E.; Lister, I.N.E.; Kung, H.T.; Kadir, E.A.; Rosa, L., Sri. Public Health Implications for Effective Community Interventions Based on Hospital Patient Data Analysis Using Deep Learning Technology in Indonesia. Information2024, 15, 41.
Putri, L.D.; Girsang, E.; Lister, I.N.E.; Kung, H.T.; Kadir, E.A.; Rosa, L., Sri. Public Health Implications for Effective Community Interventions Based on Hospital Patient Data Analysis Using Deep Learning Technology in Indonesia. Information 2024, 15, 41.
Putri, L.D.; Girsang, E.; Lister, I.N.E.; Kung, H.T.; Kadir, E.A.; Rosa, L., Sri. Public Health Implications for Effective Community Interventions Based on Hospital Patient Data Analysis Using Deep Learning Technology in Indonesia. Information2024, 15, 41.
Putri, L.D.; Girsang, E.; Lister, I.N.E.; Kung, H.T.; Kadir, E.A.; Rosa, L., Sri. Public Health Implications for Effective Community Interventions Based on Hospital Patient Data Analysis Using Deep Learning Technology in Indonesia. Information 2024, 15, 41.
Abstract
Public health is an important aspect of community activities, making research on health necessary because it is a crucial field in maintaining and improving the quality of life in society as a whole. Research in public health allows for a deeper understanding of the health problems faced by a population, including disease prevalence, risk factors and other determinants of health. This work aims to explore the potential of hospital patient data analysis as a valuable tool for understanding community implications and deriving insights for effective community health interventions. The study recognises the significance of harnessing the vast amount of data generated within hospital settings to inform population-level health strategies. The methodology employed in this study involves the collection and analysis of deidentified patient data from a representative sample of a hospital in Indonesia. Various data analysis techniques, such as statistical modelling, data mining and machine learning algorithms, are utilised to identify patterns, trends and associations within the data. A program written in Python is used to analyse patient data in a hospital for five years from 2018 to 2022. These findings are then interpreted within the context of public health implications, considering factors such as disease prevalence, socioeconomic determinants and healthcare utilisation patterns. The results of the data analysis provide valuable insights into the public health implications of hospital patient data. The research also covers prediction for the patient data to the hospital based on disease, age and geographical residence. The research prediction shows that in 2023, the number of patients was not considerably affected, but in in March to April 2024, the number significantly increased to 10,000 patients. These recommendations encompass targeted prevention strategies, improved healthcare delivery models and community engagement initiatives. The research emphasises the importance of collaboration between healthcare providers, policymakers and community stakeholders in implementing and evaluating these interventions.
Keywords
Public health; hospital; patient; community; artificial intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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