ArticleScientific reports2021
Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Tools to Predict Unilateral Primary Aldosteronism and Optimise Patient Selection for Adrenal Vein Sampling: A Systematic Review.Clinical endocrinology · 2025Pooled it
- Meta-analysis of blood parameters related to lipid and glucose metabolism between two subtypes of primary aldosteronism.Journal of clinical hypertension (Greenwich, Conn.) · 2023Pooled it
- Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications.The veterinary quarterly · 2026Review
- Molecular pathology of primary aldosteronism and hypercortisolism: Impact on adrenal surgery.iScience · 2026Review
- Machine learning-based prediction of primary aldosteronism subtype using comprehensive clinical features.Scientific reports · 2026Article
- Article
- Artificial intelligence in primary aldosteronism: current achievements and future challenges.Frontiers in molecular biosciences · 2025Review
- Validating Machine Learning Models Against the Saline Test Gold Standard for Primary Aldosteronism Diagnosis.JACC. Asia · 2024Article
- Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism.Hypertension (Dallas, Tex. : 1979) · 2024Article
- The impact of a primary aldosteronism predictive model in secondary hypertension decision support.JAMIA open · 2024Article
- Treating Primary Aldosteronism-Induced Hypertension: Novel Approaches and Future Outlooks.Endocrine reviews · 2024Article
- Machine learning-directed electrical impedance tomography to predict metabolically vulnerable plaques.Bioengineering & translational medicine · 2024Article
- Identifying primary aldosteronism patients who require adrenal venous sampling: a multi-center study.Scientific reports · 2023Article
- IdentifyingJACC. Asia · 2023Article
- O tempora, o mores: The Age We Live In, Machine Learning, Hypertension, and Primary Aldosteronism.JACC. Asia · 2023Article
- Recent progress in the diagnosis and treatment of primary aldosteronism.Hypertension research : official journal of the Japanese Society of Hypertension · 2023Review
- Machine learning-based model for prediction and feature analysis of recurrence in pancreatic neuroendocrine tumors G1/G2.Journal of gastroenterology · 2023Article
- A hyperaldosteronism subtypes predictive model using ensemble learning.Scientific reports · 2023Article
- Detection of factors affecting kidney function using machine learning methods.Scientific reports · 2022Article
- Primary aldosteronism - a multidimensional syndrome.Nature reviews. Endocrinology · 2022Review
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Primary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who should be referred to specialized centers. The aim of this retrospective cross-sectional study was to develop a predictive model for subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Overall, 91 patients with unilateral and 138 patients with bilateral PA were randomly assigned to the training and test cohorts. Four supervised machine learning classifiers; logistic regression, support vector machines, random forests (RF), and gradient boosting decision trees, were used to develop predictive models from 21 clinical variables. The accuracy and the area under the receiver operating characteristic curve (AUC) for predicting of subtype diagnosis of PA in the test cohort were compared among the optimized classifiers. Of the four classifiers, the accuracy and AUC were highest in RF, with 95.7% and 0.990, respectively. Serum potassium, plasma aldosterone, and serum sodium levels were highlighted as important variables in this model. For feature-selected RF with the three variables, the accuracy and AUC were 89.1% and 0.950, respectively. With an independent external PA cohort, we confirmed a similar accuracy for feature-selected RF (accuracy: 85.1%). Machine learning models developed using blood test can help predict subtype diagnosis of PA in general practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.