Explainable Deep Learning for Automated Extraction from Free-Text Medical Reports to enable Precision Healthcare
Although individual cancer care is standardized to a certain degree, considerable heterogeneity has been observed in healthcare delivery, uptake, and response to therapy.
These differences can have an impact on the patient journey experiences, outcomes, resource utilization, and cost of care. However, comprehensive attempts to study oncology patient populations is challenging because longitudinal data exist in multiple disparate source systems and formats, including unstructured data.
Recent advances in deep learning have raised the bar on achievable accuracy for tasks like named entity recognition, assertion status detection, entity resolution, and others, using novel healthcare-specific networks and models.
In this talk, Vishakha Sharma will share how Roche applies Spark NLP for healthcare to extract clinical knowledge from pathology, radiology, and genomics reports.
Principal Data Scientist at Roche
Vishakha Sharma is a principal data scientist in Roche diagnostics information solutions, where she leads advanced analytics initiatives such as natural language processing (NLP) and machine learning (ML) to discover key insights improving NAVIFY product portfolio, leading to better and more efficient patient care.
Vishakha has authored 40+ peer-reviewed publications and proceedings and has given 15+ invited talks. She serves on the program committee of the ACM-W, NeurIPS, AMIA, and ACM-BCB.
Her research work has been funded by the NIH Big Data to Knowledge (BD2K) initiative to build an NLP precision medicine software. Vishakha is a senior member of the Association for Computing Machinery (ACM) and IEEE. She holds a Ph.D. in computer science.