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Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?
Danijela Tasić1, Katarina Đorđević2, Slobodanka Galović2
1UCC Niš, Clinic of Nephrology, Medical Faculty, University of Niš, 18000 Niš, Serbia.
Diagnostics (Basel, Switzerland)
|December 23, 2022
Summary
Artificial intelligence models can predict cardiorenal balance by analyzing electrolytes and volemia. This approach aids in identifying patients at risk for electrolyte disturbances and hypervolemia, improving preventative strategies.
Area of Science:
- Cardiorenal Medicine
- Artificial Intelligence in Healthcare
- Biomarker Discovery
Background:
- Current clinical markers fail to differentiate permanent renal function impairment.
- Assessing cardiorenal balance requires evaluating volemia, sodium, and potassium levels.
- Understanding electrolyte interdependence is crucial for patient management.
Purpose of the Study:
- To investigate volemia and electrolyte status in cardiorenal patients using AI.
- To analyze the relationship between B-type natriuretic peptide, electrolytes, and cardiac function.
- To develop a predictive model for identifying patients at risk of cardiorenal complications.
Main Methods:
- Utilized an adaptive neuro fuzzy inference system (ANFIS) algorithm.
- Analyzed five key variables: B-type natriuretic peptide, sodium, potassium, ejection fraction, and EPI creatinine-cystatin C.
- Employed random regression for prognostic biomarker identification.
Main Results:
- B-type natriuretic peptide significantly influenced ejection fraction.
- Elevated sodium (Na+) and potassium (K+) levels correlate with patient deterioration and vital endangerment.
- The AI model successfully identified prognostic biomarkers for risk prediction.
Conclusions:
- AI-driven analysis of volemia and electrolytes offers a superior approach to traditional diagnostics.
- The model aids in optimizing preventative strategies and intensive monitoring for at-risk patients.
- Accurate and rapid diagnostic interpretation facilitates better patient treatment planning.
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