At Droice, we leverage massive repositories of clinical text to build deep learning/NLP solutions to help clinicians make better decisions for individual patients. With the widespread adoption of electronic medical records (EMRs) and recent advances in machine learning, natural language processing has come to the forefront in clinical AI. Despite the challenges of working with unstructured text, doctors’ notes and other clinical text contains some of the richest information about a patient. However, building systems that can work with clinical text in languages other than English remains a challenge to this day. In this talk, we will present several real-world use cases of NLP-powered solutions in several languages.
Harshit Saxena is the Chief of Product at Droice Labs, where he manages a team that builds and integrates cutting-edge AI solutions in large healthcare enterprises. Previously, he led product integration and development teams at GE and Oracle. Harshit specialized in machine learning in his MS at Columbia University and worked alongside with industry and academia on computer vision, Augmented Reality & 3D mapping technologies. Previously, he has worked with the UN Millenium Villages project, where he designed, built, and deployed scalable software solutions for several countries in Africa enabling drinking water and electricity access for millions of individuals.
At Droice, Tasha manages the AI team to develop state of the art technology that understands a doctor’s thought process through natural language understanding. Prior to joining the machine learning lab at Columbia, she was focused on core research involving mathematical modeling enabling brain computer interfaces. Tasha is a physicist who graduated from Brown University, where she researched the function of neural circuits using optogenetics and mathematical modeling. Tasha believes that the next breakthrough in AI will be the elimination of the black-box behavior of today’s AI.