ArticleHealthcare informatics research2023
Improvement of Dialysis Dosing Using Big Data Analytics.
Article in Healthcare informatics research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.BMC medical informatics and decision making · 2024Trial
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Authors and funding
4 authors.
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Abstract
objectivesLarge amounts of healthcare data are now generated via patient health records, records of diagnosis and treatment, smart devices, and wearables. Extracting insights from such data can transform healthcare from a traditional, symptom-driven practice into precisely personalized medicine. Dialysis treatments generate a vast amount of data, with more than 100 parameters that must be regulated for ideal treatment outcomes. When complications occur, understanding electrolyte parameters and predicting their outcomes to deliver the optimal dialysis dosing for each patient is a challenge. This study focused on refining dialysis dosing by utilizing emerging data from the growing number of dialysis patients to improve patients' quality of life and well-being.
methodsExploratory data analysis and data prediction approaches were performed to gather insights from patients' vital electrolytes on how to improve the patients' dialysis dosing. Four predictive models were constructed to predict electrolyte levels through various dialysis parameters.
resultsThe decision tree model showed excellent performance and more accurate results than the support vector machine, linear regression, and neural network models.
conclusionsThe predictive models identified that pre-dialysis blood urea nitrogen, pre-weight, dry weight, anticoagulation, and sex had the most significant effects on electrolyte concentrations. Such models could fine-tune dialysis dosing levels for the growing number of dialysis patients to improve each patient's quality of life, life expectancy, and well-being, and to reduce costs, efforts, and time consumption for both patients and physicians. The study's results need to be validated on a larger scale.
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