@inproceedings{feder-etal-2022-building,
title = "Building a Clinically-Focused Problem List From Medical Notes",
author = "Feder, Amir and
Laish, Itay and
Agarwal, Shashank and
Lerner, Uri and
Atias, Avel and
Cheung, Cathy and
Clardy, Peter and
Peled-Cohen, Alon and
Fellinger, Rachana and
Liu, Hengrui and
Huong Nguyen, Lan and
Patel, Birju and
Potikha, Natan and
Taubenfeld, Amir and
Xu, Liwen and
Yang, Seung Doo and
Benjamini, Ayelet and
Hassidim, Avinatan",
booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.louhi-1.8",
doi = "10.18653/v1/2022.louhi-1.8",
pages = "60--68",
abstract = "Clinical notes often contain useful information not documented in structured data, but their unstructured nature can lead to critical patient-related 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 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. 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.",
}
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<abstract>Clinical notes often contain useful information not documented in structured data, but their unstructured nature can lead to critical patient-related 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 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. 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.</abstract>
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%0 Conference Proceedings
%T Building a Clinically-Focused Problem List From Medical Notes
%A Feder, Amir
%A Laish, Itay
%A Agarwal, Shashank
%A Lerner, Uri
%A Atias, Avel
%A Cheung, Cathy
%A Clardy, Peter
%A Peled-Cohen, Alon
%A Fellinger, Rachana
%A Liu, Hengrui
%A Huong Nguyen, Lan
%A Patel, Birju
%A Potikha, Natan
%A Taubenfeld, Amir
%A Xu, Liwen
%A Yang, Seung Doo
%A Benjamini, Ayelet
%A Hassidim, Avinatan
%S Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F feder-etal-2022-building
%X Clinical notes often contain useful information not documented in structured data, but their unstructured nature can lead to critical patient-related 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 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. 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.
%R 10.18653/v1/2022.louhi-1.8
%U https://aclanthology.org/2022.louhi-1.8
%U https://doi.org/10.18653/v1/2022.louhi-1.8
%P 60-68
Markdown (Informal)
[Building a Clinically-Focused Problem List From Medical Notes](https://aclanthology.org/2022.louhi-1.8) (Feder et al., Louhi 2022)
ACL
- Amir Feder, Itay Laish, Shashank Agarwal, Uri Lerner, Avel Atias, Cathy Cheung, Peter Clardy, Alon Peled-Cohen, Rachana Fellinger, Hengrui Liu, Lan Huong Nguyen, Birju Patel, Natan Potikha, Amir Taubenfeld, Liwen Xu, Seung Doo Yang, Ayelet Benjamini, and Avinatan Hassidim. 2022. Building a Clinically-Focused Problem List From Medical Notes. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI), pages 60–68, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.