Electronic medical records (EMR) have largely replaced hand-written patient files in healthcare.The growing pool of EMR data presents a significant resource in medical research, but the U.S. Health Insurance Portability and Accountability Act (HIPAA) mandates redacting medical records before performing any analysis on the same. This process complicates obtaining medical data and can remove much useful information from the record. As part of a larger project involving ontologydriven medical processing, we employ a method of recognizing protected health information (PHI) that maps to ontological terms. We then use the relationships defined in the ontology to redact medical texts so that roles and semantics of terms are retained without compromising anonymity. The method is evaluated by clinical experts on several hundred medical documents, achieving up to a 98.8% f-score, and has already shown promise for retaining semantic information in later processing.

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Automatic suggesting models have been available already for item buying, films, and amusements. Demand for one such system for food delivery startup is picking up now. Main aim of this research work is to come up with design of one such recommender system for food delivery startup makes it to sustain in the business on long run through better understanding as well as retention of potential customers automatically. Zomato reviews data are taken for analyzing using some selected algorithms of machine learning. From them the best predictor model is picked up for building the final proposed engine. Underlying methodology goes like this, it first knows and learns about customers using past and near past data (training data) and using that knowledge it tries to draw results of current data. Accuracy of this result is then observed and pruned, if not up to considered bench mark, by employing bias-variance tradeoff as well as considering more reliable characteristics. Once classifier design with required level of accuracy is obtained and it can then be developed. The focus of the research is to design a system that facilitates automatic recommendation for food startup to know and retain its good restaurants aggregation as well as potential users.

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