Data Cleansing Using Artificial Intelligence: A Case Study on Reducing Errors in Healthcare Analytics
Keywords:
data cleansing, artificial intelligence, healthcare analyticsAbstract
Healthcare analytics data purification case study assists AI. Data quality is crucial to healthcare analytics because inaccurate, incomplete, or inconsistent data may damage patient care and management. Normal data cleaning fails owing of healthcare data's bulk, complexity, and unpredictability. The study fixes healthcare datasets using machine learning, NLP, and AI.
The case study covers AI-driven data cleansing. AI algorithms find EHR, medical note, imaging, and lab mistakes. The study examines algorithms that identify missing data, duplication, formatting, and logic errors. AI algorithms correct big healthcare datasets.
Healthcare analytics investigates AI-based data purifying mistakes. Precision, recall, accuracy, and F1-score evaluate AI error correction. Research shows that cleaning healthcare analytics data improves prediction models, clinical decision support, and outcomes. AI-based data purification saves time and money and lets doctors focus on patients.
This case study sanitizes physician notes and medical data using NLP. Ambiguity-resolving NLP algorithms improve clinical free-text insights. The case study suggests AI-based data purification may improve healthcare.
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