Advanced Data Extraction and Predictive Analytics for Hemodialyzers Using AI and OCR

The emphasis on medical technology has catalyzed increasing technology adoption in healthcare automation to manage patient data. Hemodialysis is a life-saving procedure in kidney disease requiring constant observation of such parameters as displayed on a hemodialysis machine. Paperbased data recording in these environments is tedious, timeconsuming, and susceptible to human errors mainly due to stress. This work proposes a method for extracting data from displays of a hemodialysis machine based on images from a mobile camera. With image analysis and Optical Character Recognition (OCR) technologies, the data, including temperature and conductivity, extracts and translates the values into JavaScript Object Notation (JSON). The algorithm has been developed to focus on plural machine models and not be limited to a specific format and screen sizes frequently used in clinical practice. In addition, the system uses artificial intelligence in predictive modelling to identify patterns and possible health issues and ensure professionals address them at the right time. The outcomes of the comparative analysis show that the mentioned scheme is more effective than the conventional paper-to-computer data managenent approach.