Abstract: A method of machine learning model training for automated entity update processing includes obtaining a training set of multiple historical claims documents, extracting text portions from the training set of multiple historical claims documents, and generating training feature vectors. The method includes supplying the training feature vectors to a treatment identification machine learning model to train the treatment identification machine learning model to generate a prediction output, obtaining a target claim document, extracting text portions and identified text features from the target claim document, supplying the extracted text portions and the identified text features from the target claim document to the machine learning model, and automatically processing an entity update associated with the target claim document based on the prediction output of the correct entity value for the target claim document.

Abstract: A computer implemented method of generating decision options. Sensor data is received from a plurality of sensors and presented visually to two or more users. Those users can then analyze the images and enter tag data which is received and stored along with the sensor data. The sensor and tag data are then input to a computer implemented decision support algorithm along with a stored operational plan. The algorithm then outputs one or more decision options which can assist a human decision maker in making a decision. The invention enables such a decision maker to make a decision quickly which complies with a previously stored operational plan and is likely to be correct since it is based on inputs from multiple human users. The invention is capable of being easily scaled to deal with a high volume of sensor data and a large number of users.

Abstract: A computer implemented method of generating decision options. Sensor data is received from a plurality of sensors and presented visually to two or more users. Those users can then analyse the images and enter tag data which is received and stored along with the sensor data. The sensor and tag data are then input to a computer implemented decision support algorithm along with a stored operational plan. The algorithm then outputs one or more decision options which can assist a human decision maker in making a decision. The invention enables such a decision maker to make a decision quickly which complies with a previously stored operational plan and is likely to be correct since it is based on inputs from multiple human users. The invention is capable of being easily scaled to deal with a high volume of sensor data and a large number of users.