Baseline Complete Blood Count and Chemistry Panel Profile from the Japan Environment and Children’s Study (JECS)

Taniguchi, Yu; Yamazaki, Shin; Nakayama, Shoji F.; Sekiyama, Makiko; Michikawa, Takehiro; Isobe, Takeshi; Iwai-Shimada, Miyuki; Kobayashi, Yayoi; Takagi, M.; Kamijima, Michihiro

doi:10.3390/ijerph19063277

Cited by 7 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work guides the potential of machine learning in enhancing healthcare diagnosis and treatment. Information technology has been applied in processing hospital patient data for public health [29][30][31]. This article highlights the application of information technology in the processing of hospital patient data for public health purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Public Health Implications for Effective Community Interventions Based on Hospital Patient Data Analysis Using Deep Learning Technology in Indonesia

Putri,

Girsang,

Lister

et al. 2024

Information

View full text Add to dashboard Cite

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 on 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 predictions for the patient data to the hospital based on disease, age, and geographical residence. The research prediction shows that, in the year 2023, the number of patients will not be considerably affected by the infection, but in March to April 2024 the number will increase significantly up to 10,000 patients due to the trend in the previous year at the end of 2022. 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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%