Multipage Medical Document Classification of Printed and Scanned Pages Using Machine Learning Algorithms
Author(s):
Shreedhar Deshmukh
The rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) is redefining how healthcare organizations and BPO (Business Process Outsourcing) service providers operate. With increasing pressure to manage large volumes of patient documentation quickly and accurately—particularly for insurance and legal claims—many companies are turning to AI to streamline operations and enhance service delivery. A significant challenge in this space involves handling diverse medical records, which often include a combination of scanned handwritten notes, typed physician reports, lab test results, and radiology findings. These documents must be properly sorted, categorized, and chronologically arranged to reconstruct a clear patient history. Traditionally, this has been a time-consuming and error-prone manual process. Today, AI-driven solutions are stepping in to automate document classification using advanced natural language processing techniques such as TF-IDF, Count Vectorization, and word embeddings. These methods help machine learning models learn from historical data and accurately assign documents to relevant categories like diagnoses, prescriptions, and test results. We have tried to develop an application called auto index system, that classifies a medical document by analysing its content and categorizing it under predefined class (eg. Consultation, Anesthesia, Lab Report-Culture test, Radiology, office visit) topics and creating an index of pages falling under the category. We have plenty of classes to classify but fixed the scope to four-five predefined topics namely, Progress notes, consultation, CT, Lab reports, radiology. We use term frequency technology to convert and count number of words in the medical text documents and classify them based on weightage calculation.
