ArticleJournal of clinical medicine2020
Increasing the Density of Laboratory Measures for Machine Learning Applications.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Machine Learning-Based Prediction of Stroke in Emergency Departments.Therapeutic advances in neurological disorders · 2024Article
- Emerging role for R-loop formation in hepatocellular carcinoma.Genes & genomics · 2023Review
- Defining the Age of Young Ischemic Stroke Using Data-Driven Approaches.Journal of clinical medicine · 2023Article
- Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine.Journal of clinical medicine · 2021Review
- Imputation of missing values for electronic health record laboratory data.NPJ digital medicine · 2021Article
- Predicting short and long-term mortality after acute ischemic stroke using EHR.Journal of the neurological sciences · 2021Article
- Prediction of Long-Term Stroke Recurrence Using Machine Learning Models.Journal of clinical medicine · 2021Article
- Early Detection of Septic Shock Onset Using Interpretable Machine Learners.Journal of clinical medicine · 2021Article
- Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
backgroundThe imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications.
methodWe analyzed the laboratory measures derived from Geisinger's EHR on patients in three distinct cohorts-patients tested for
resultsWe analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for
conclusionsAn adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis.
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