Clinical notes often contain useful information not documented in structured data, but their unstructured nature can lead to critical patientrelated information being missed. To increase the likelihood that this valuable information is utilized for patient care, algorithms that summarize notes into a problem list have been proposed. Focused on identifying medicallyrelevant 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. Mitigating these issues, 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 develop a novel algorithm that aggregates over the set of clinical conditions detected on all of the patient's notes, and produce a concise patient summary that organizes their most important conditions.
|Title of host publication||LOUHI 2022 - 13th International Workshop on Health Text Mining and Information Analysis, Proceedings of the Workshop|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||9|
|State||Published - 2022|
|Event||13th International Workshop on Health Text Mining and Information Analysis, LOUHI 2022, co-located with EMNLP 2022 - Abu Dhabi, United Arab Emirates|
Duration: 7 Dec 2022 → …
|Name||LOUHI 2022 - 13th International Workshop on Health Text Mining and Information Analysis, Proceedings of the Workshop|
|Conference||13th International Workshop on Health Text Mining and Information Analysis, LOUHI 2022, co-located with EMNLP 2022|
|Country/Territory||United Arab Emirates|
|Period||7/12/22 → …|
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