ArticleInternational journal of medical informatics2022
Development of a knowledge mining approach to uncover heterogeneous risk predictors of acute kidney injury across age groups.
Article in International journal of medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 5 citations in OpenAlex.
- An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.BMC medical informatics and decision making · 2025Article
- Development and validation of a Multi-Causal investigation and discovery framework for knowledge harmonization (MINDMerge): A case study with acute kidney injury risk factor discovery using electronic medical records.International journal of medical informatics · 2024Article
- Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
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Abstract
objectivesAcute kidney injury (AKI) risk increases with age and the underlying clinical predictors may be heterogeneous across age strata. This study aims to uncover the AKI risk factor heterogeneity among general inpatients across age groups using electronic medical records (EMR).
methodsPatient data (n = 179,370 encounters) were collected from an academic hospital between 2007 and 2016, and were stratified into four age groups: 18-35, 36-55, 56-65, and > 65. Potential risk factors extracted for the cohort included demographics, vital signs, laboratory values, past medical diagnoses, medications and admission diagnoses. We developed a data driven knowledge mining approach consisting of a machine learning algorithm to identify AKI predictors across age strata and a statistical method to quantify the impact of those factors on AKI risk. Identified predictors were evaluated for their predictability of AKI in terms of area-under-the-receiver-operating-characteristic-curve (AUC) and validated against expert knowledge.
resultsAmong the final analysis cohort of 76,957 hospital admissions, AKI prediction across age groups 18-35 (16.73%), 36-55 (32.74%), 56-65 (23.52%), and > 65 years (27.01%) achieved AUC of 0.85 (95% CI, 0.80-0.88), 0.86 (95% CI, 0.83-0.89), 0.87 (95% CI, 0.86-0.90), and 0.87 (95% CI, 0.86-0.90), respectively. Compared to expert knowledge, absolute consistency rates of the top-150 identified risk factors for each group were 78.4%, 77.2%, 81.3%, and 79.5%, respectively. Impact of many predictors on AKI varied across age groups; for example, high body mass index (BMI) was found to be associated with higher AKI risk in elderly patients, but low BMI was found to be associated with higher AKI risk in younger patients.
conclusionsWe verified the effectiveness of the knowledge mining method from the perspectives of accuracy, stability and credibility, and used this approach to clarify the heterogeneity of AKI risk factors between age groups. Future decision support systems need to consider such heterogeneity to enhance personalized patient care.
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