Shashank Agarwal
Currently, Shashank works in Google Research as part of Google's LearnLM effort, where he leads a team to improve Educational use cases in Gemini.
Previously, Shashank worked in the Google for Health division in the Bay Area where his work focused on developing AI and NLP-based solutions to improve physicians' productivity. He completed his PhD in Medical Informatics from University of Wisconsin-Milwaukee. His research involved Information Extraction from published literature.
Previously, Shashank worked in the Google for Health division in the Bay Area where his work focused on developing AI and NLP-based solutions to improve physicians' productivity. He completed his PhD in Medical Informatics from University of Wisconsin-Milwaukee. His research involved Information Extraction from published literature.
Authored Publications
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Building a Clinically-Focused Problem List From Medical Notes
Ayelet Benjamini
Birju Patel
Cathy Cheung
Hengrui Liu
Liwen Xu
Peter Clardy
Rachana Fellinger
LOUHI 2022: The 13th International Workshop on Health Text Mining and Information Analysis (2022)
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Clinical notes often contain vital information not observed in other structured data, but their unstructured nature can lead to critical patient-related information being lost. To make sure this valuable information is utilized for patient care, algorithms that summarize notes into a problem list are often proposed. Focusing on identifying medically-relevant entities in the free-form text, these solutions are often detached from a canonical ontology and do not allow downstream use of the detected text-spans. As a solution, we present here a system for generating a canonical problem list from medical notes, consisting of two major stages. At the first stage, annotation, we use a transformer model to detect all clinical conditions which are mentioned in a single note. These clinical conditions are then grounded to a predefined ontology, and are linked to spans in the text. At the second stage, summarization, we aggregate over the set of clinical conditions detected on all of the patient's note, and produce a concise patient summary that organizes their important conditions.
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