Predictive system for medical program inclusion based on doctor notations

The challenge was that the doctor notations were in text format, making it difficult to extract relevant information. Our solution was built on NLP system using Bert models that could transform the textual data into numerical vectors and identify relevant features for inclusion in the predictive model.

After conducting exploratory data analysis, our team of experts utilized a range of machine learning techniques, including Logistic Regression, Random Forest, Naive Bayes, and Bagging, to develop a classification model that could accurately predict a patient's eligibility for a medical program based on doctor notations. We also optimized the pipeline and cleaned the code to ensure efficient and accurate predictions. To further enhance the system's accuracy, we integrated Yolov5, Pose-Net, Torch, Tensorflow, and Tf-Lite (for transfer model to Nvidia Jetson) to improve the prediction capabilities.

Upon completion of the project the client - got the access to an advanced accounting system and internet shop engine - benefited from streamlined financial processes, improved data analysis, and enhanced retail operations - the integration of AI and ML technologies provided valuable - insights, optimized decision-making, and boosted overall efficiency.