One of client was in the process of consolidating data and building analytical platform for Revenue Cycle Management and we were approached to provide a solution to extract data from their Cerner system.
With the help of our healthcare expertise and knowledge on Cerner Millennium system we provided a solution to extract and load operational, financial data to data lake using CCL (Cerner command language) and this project was executed in a record time of 3 weeks to meet customer product roadmap.
Chest X -Ray Classification uses combination of Deep Learning and Visual Analytics/Image Processing to recognize patterns of Chest X-Ray Diagnosis which in turn can serve as a learning feed for them so that they can highlight the abnormalities in new incoming real-time Chest X-Ray Digital images
We used ~12k images to train our networked model to achieve a reliable validated accuracy of ~95 % in detection of accurate diagnosis from digital Chest X-Ray images. We also hierarchically classify our severity position of image in three categories. For the initial purposes, we have used 13 diagnosis abnormalities and “No-findings” for the training purpose.
Our customer wanted to avoid collisions with other traffic participants by providing automatic braking request. We provided with EBA (Emergency braking assistant) solution that tries to decelerate vehicle by raising request to brake system in case of crash/traffic hazard. EBA algorithm analyses situation based on inputs given by environment model and plans activation of brake.
The objective was to attach the RFID stickers to the work orders that are landed on the aviation shopfloor so that they can be mapped and stored in the dashboard. Due to its traceability, the staff doesn’t have to spend a lot of effort in searching for it in every cycle rather can scan the tags and further track its location, position at just the tips of the fingers and it is now successfully live.
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