Big Data, Decision Models, and Public Health

Chan, Chien-Lung; Chang, Chi‐Chang

doi:10.3390/ijerph17186723

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…The uncertainty of diagnosis usually results from heterogeneity screening and clinical practices; thus, an accurate tool is needed for early prediction to ensure that potential patients receive and comply with preventive health check-ups. Data mining has been successfully used to build predictive models for healthcare prediction tasks [15][16][17][18][19][20][21]. The present study sought to evaluate the novel hypothesis that men and women with CKD possess different risk factors.…”

Section: Introductionmentioning

confidence: 99%

Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease

Kao

Chang

et al. 2022

IJERPH

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Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results (p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.

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Section: Introductionmentioning

confidence: 99%

Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease

Kao

Chang

et al. 2022

IJERPH

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“…To support health professionals in sharing EHRs, integrated communication by means of vocabularies such as SNOMED CT or LOINC is crucial [4]. Therefore, in the future, mappings to other terminologies, especially to SNOMED CT, will enable the international exchange of ICF data in standardized data models [74][75][76] and facilitate big data analysis [77]. To this end, linkage to other established vocabularies is an important goal in addition to establishing a uniform framework and language in dietetics.…”

Section: Integration Of the Icf Cataloguementioning

confidence: 99%

Integrating a New Dietetic Care Process in a Health Information System: A System and Process Analysis and Assessment

Gäbler¹,

Lycett

Gall

2022

IJERPH

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Managing routinely collected data in health care and public health is important for evaluation of interventions and answering research questions using “real life” and ”big data”. In addition to the technical requirements of information systems, both standardized terminology and standardized processes are needed. The aim of this project was to analyse and assess the integration of standardized terminology and document templates for a dietetic care process (DCP) into the health information system (HIS) in a hospital in Austria. Using an action research approach, the DCP was analysed through four expert interviews and the integration into the HIS through two expert interviews with observations. Key strengths and weaknesses for the main criteria (“integration of the ICF catalogue”, “adaption of the document templates”, “adaption of the DCP”, and the “adaption of the user authorizations”) were presented and proposals for improvement given. The system and process integration of the DCP is possible, and the document templates can be adapted with the software currently in use. Although an increase in resources and finances required is to be expected initially, the integration of a standardized dietetic terminology in combination with a standardized process is likely to improve the quality of care and support outcomes management and research.

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“…With the previous Special Issue on ’Big Data, Decision Models, and Public Health’ [ 1 ] being successful, similar to the first edition, this second Special Issue covers five important themes. The first theme looks at preventive medicine and risk assessment.…”

Section: The Organization Of This Special Issuementioning

confidence: 99%

“…As the digital era unfolds, the volume and velocity of environmental, population, and public health data are rapidly increasing. In recent decades, big data analytic techniques such as statistical analysis, data mining, machine learning, and deep learning have made significant progress and have attracted the attention of researchers and scientists in a variety of applications [ 1 ]. Making decisions based on concrete evidence is particularly important and has a substantial impact on public health and program implementation.…”

Section: Introductionmentioning

confidence: 99%