2022
DOI: 10.1016/j.ssmph.2022.101210
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Identifying individual social risk factors using unstructured data in electronic health records and their relationship with adverse clinical outcomes

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Cited by 2 publications
(2 citation statements)
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“…[40][41][42] NLP has emerged as an efficient mechanism for addressing the analytical challenges of extracting SDoH from medical notes. 38,[42][43][44][45][46][47][48][49] In terms of the methods for extracting SDoH information, rule-based, supervised machine learning, and deep learning approaches are the most commonly employed techniques encountered in the vast majority of studies, 42 while multiple publications have used unsupervised approaches or previously developed NLP infrastructure based on lexicons and rules. [50][51][52][53] Despite the growing literature on using NLP algorithms to identify social and behavioral factors for various healthcare outcomes, there is a paucity of investigations comparing various NLP approaches to leveraging SDoH information from unstructured EHRs specifically for patients with ADRD.…”
Section: Introductionmentioning
confidence: 99%
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“…[40][41][42] NLP has emerged as an efficient mechanism for addressing the analytical challenges of extracting SDoH from medical notes. 38,[42][43][44][45][46][47][48][49] In terms of the methods for extracting SDoH information, rule-based, supervised machine learning, and deep learning approaches are the most commonly employed techniques encountered in the vast majority of studies, 42 while multiple publications have used unsupervised approaches or previously developed NLP infrastructure based on lexicons and rules. [50][51][52][53] Despite the growing literature on using NLP algorithms to identify social and behavioral factors for various healthcare outcomes, there is a paucity of investigations comparing various NLP approaches to leveraging SDoH information from unstructured EHRs specifically for patients with ADRD.…”
Section: Introductionmentioning
confidence: 99%
“…SDoH items have been conventionally recorded in physician notes, nurse notes, and other types of unstructured free‐text narratives, but have not been given sufficient priority to be collected and recorded in structured EHR data 40–42 . NLP has emerged as an efficient mechanism for addressing the analytical challenges of extracting SDoH from medical notes 38,42–49 . In terms of the methods for extracting SDoH information, rule‐based, supervised machine learning, and deep learning approaches are the most commonly employed techniques encountered in the vast majority of studies, 42 while multiple publications have used unsupervised approaches or previously developed NLP infrastructure based on lexicons and rules 50–53 .…”
Section: Introductionmentioning
confidence: 99%