Effective care planning requires care managers to understand patient health status and needs to deliver appropriate patient support. The proliferation of healthcare data including massive volumes of clinical free text documents, creates a significant challenge for care managers, but a major opportunity for advanced clinical analytics. Novel Artificial Intelligence (AI)-driven solutions can help optimize care planning, reducing inefficiency and increasing focus on the most salient information, leading to improved patient outcomes. This talk will focus on various deep learning-based clinical natural language processing use cases developed as part of our advanced care planning initiatives.
- Effective care planning requires care managers to understand patient health status and needs to deliver appropriate support
- Clinical domain has unique challenges such as massive structured/unstructured data, redundancy, limited interoperability, widespread use of acronyms etc.
- AI-augmented solutions can help optimize care planning, reducing inefficiency and increasing focus on the most salient information leading to improved patient outcomes
Dr. Sadid Hasan is a Senior Director for AI at CVS Health leading the team responsible for AI-enabled clinical care plan initiatives in Aetna. His recent work involves solving problems related to clinical information extraction, paraphrase generation, natural language inference, and clinical question answering using Deep Learning. Sadid has over 60 peer-reviewed publications in the top NLP/Machine Learning venues, where he also regularly serves as a program committee member/area chair including ACL, IJCAI, EMNLP, NeurIPS, ICML, COLING, NAACL, AMIA, MLHC, MEDINFO, ICLR, ClinicalNLP, TKDE, JAIR etc.