Bridging information gaps in menopause status classification through natural language processing
Published by Oxford University Press on behalf of the American Medical Informatics Association 2024..
Objective: To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient's menopausal status.
Materials and methods: A rule-based NLP system was designed to capture evidence of a patient's menopause status including dates of a patient's last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. NLP-derived output was used in combination with structured EHR data to classify a patient's menopausal status. NLP processing and patient classification were performed on a cohort of 307 512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA).
Results: NLP was validated at 99.6% precision. Including the NLP-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81 173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167 804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data.
Discussion: By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis.
Conclusion: NLP can be used to identify concepts relevant to a patient's menopausal status in clinical notes. Adding NLP-derived data to an algorithm classifying a patient's menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:7 |
|---|---|
| Enthalten in: |
JAMIA open - 7(2024), 1 vom: 23. März, Seite ooae013 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Eyre, Hannah [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Journal Article |
|---|
| Anmerkungen: |
Date Revised 01.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1093/jamiaopen/ooae013 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM369095871 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM369095871 | ||
| 003 | DE-627 | ||
| 005 | 20240301232913.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240301s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1093/jamiaopen/ooae013 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1313.xml |
| 035 | |a (DE-627)NLM369095871 | ||
| 035 | |a (NLM)38419670 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Eyre, Hannah |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Bridging information gaps in menopause status classification through natural language processing |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 01.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Published by Oxford University Press on behalf of the American Medical Informatics Association 2024. | ||
| 520 | |a Objective: To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient's menopausal status | ||
| 520 | |a Materials and methods: A rule-based NLP system was designed to capture evidence of a patient's menopause status including dates of a patient's last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. NLP-derived output was used in combination with structured EHR data to classify a patient's menopausal status. NLP processing and patient classification were performed on a cohort of 307 512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA) | ||
| 520 | |a Results: NLP was validated at 99.6% precision. Including the NLP-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81 173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167 804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data | ||
| 520 | |a Discussion: By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis | ||
| 520 | |a Conclusion: NLP can be used to identify concepts relevant to a patient's menopausal status in clinical notes. Adding NLP-derived data to an algorithm classifying a patient's menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a menopause | |
| 650 | 4 | |a natural language processing | |
| 650 | 4 | |a phenotyping | |
| 650 | 4 | |a women’s health | |
| 700 | 1 | |a Alba, Patrick R |e verfasserin |4 aut | |
| 700 | 1 | |a Gibson, Carolyn J |e verfasserin |4 aut | |
| 700 | 1 | |a Gatsby, Elise |e verfasserin |4 aut | |
| 700 | 1 | |a Lynch, Kristine E |e verfasserin |4 aut | |
| 700 | 1 | |a Patterson, Olga V |e verfasserin |4 aut | |
| 700 | 1 | |a DuVall, Scott L |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t JAMIA open |d 2018 |g 7(2024), 1 vom: 23. März, Seite ooae013 |w (DE-627)NLM290202396 |x 2574-2531 |7 nnns |
| 773 | 1 | 8 | |g volume:7 |g year:2024 |g number:1 |g day:23 |g month:03 |g pages:ooae013 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1093/jamiaopen/ooae013 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 7 |j 2024 |e 1 |b 23 |c 03 |h ooae013 | ||