Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment

Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machi...
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Autor*in:	Kirk Roberts, PhD [verfasserIn] Aaron T. Chin, MD [verfasserIn] Klaus Loewy, MS [verfasserIn] Lisa Pompeii, PhD [verfasserIn] Harold Shin, MS [verfasserIn] Nicholas L. Rider, DO [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Natural language processing machine learning text mining inborn errors of immunity primary immunodeficiency diagnosis

Übergeordnetes Werk:	In: Journal of Allergy and Clinical Immunology: Global - Elsevier, 2023, 3(2024), 2, Seite 100224-
Übergeordnetes Werk:	volume:3 ; year:2024 ; number:2 ; pages:100224-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jacig.2024.100224

Katalog-ID:	DOAJ101457987

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245	1	0	\|a Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment
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520			\|a Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection.
650		4	\|a Natural language processing
650		4	\|a machine learning
650		4	\|a text mining
650		4	\|a inborn errors of immunity
650		4	\|a primary immunodeficiency
650		4	\|a diagnosis
653		0	\|a Immunologic diseases. Allergy
700	0		\|a Aaron T. Chin, MD \|e verfasserin \|4 aut
700	0		\|a Klaus Loewy, MS \|e verfasserin \|4 aut
700	0		\|a Lisa Pompeii, PhD \|e verfasserin \|4 aut
700	0		\|a Harold Shin, MS \|e verfasserin \|4 aut
700	0		\|a Nicholas L. Rider, DO \|e verfasserin \|4 aut
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allfields	10.1016/j.jacig.2024.100224 doi (DE-627)DOAJ101457987 (DE-599)DOAJ29e51555a77543379d8757b0f61c0338 DE-627 ger DE-627 rakwb eng RC581-607 Kirk Roberts, PhD verfasserin aut Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection. Natural language processing machine learning text mining inborn errors of immunity primary immunodeficiency diagnosis Immunologic diseases. Allergy Aaron T. Chin, MD verfasserin aut Klaus Loewy, MS verfasserin aut Lisa Pompeii, PhD verfasserin aut Harold Shin, MS verfasserin aut Nicholas L. Rider, DO verfasserin aut In Journal of Allergy and Clinical Immunology: Global Elsevier, 2023 3(2024), 2, Seite 100224- (DE-627)1786549182 (DE-600)3106741-4 27728293 nnns volume:3 year:2024 number:2 pages:100224- https://doi.org/10.1016/j.jacig.2024.100224 kostenfrei https://doaj.org/article/29e51555a77543379d8757b0f61c0338 kostenfrei http://www.sciencedirect.com/science/article/pii/S2772829324000201 kostenfrei https://doaj.org/toc/2772-8293 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2024 2 100224-
spelling	10.1016/j.jacig.2024.100224 doi (DE-627)DOAJ101457987 (DE-599)DOAJ29e51555a77543379d8757b0f61c0338 DE-627 ger DE-627 rakwb eng RC581-607 Kirk Roberts, PhD verfasserin aut Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection. Natural language processing machine learning text mining inborn errors of immunity primary immunodeficiency diagnosis Immunologic diseases. Allergy Aaron T. Chin, MD verfasserin aut Klaus Loewy, MS verfasserin aut Lisa Pompeii, PhD verfasserin aut Harold Shin, MS verfasserin aut Nicholas L. Rider, DO verfasserin aut In Journal of Allergy and Clinical Immunology: Global Elsevier, 2023 3(2024), 2, Seite 100224- (DE-627)1786549182 (DE-600)3106741-4 27728293 nnns volume:3 year:2024 number:2 pages:100224- https://doi.org/10.1016/j.jacig.2024.100224 kostenfrei https://doaj.org/article/29e51555a77543379d8757b0f61c0338 kostenfrei http://www.sciencedirect.com/science/article/pii/S2772829324000201 kostenfrei https://doaj.org/toc/2772-8293 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2024 2 100224-
allfields_unstemmed	10.1016/j.jacig.2024.100224 doi (DE-627)DOAJ101457987 (DE-599)DOAJ29e51555a77543379d8757b0f61c0338 DE-627 ger DE-627 rakwb eng RC581-607 Kirk Roberts, PhD verfasserin aut Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection. Natural language processing machine learning text mining inborn errors of immunity primary immunodeficiency diagnosis Immunologic diseases. Allergy Aaron T. Chin, MD verfasserin aut Klaus Loewy, MS verfasserin aut Lisa Pompeii, PhD verfasserin aut Harold Shin, MS verfasserin aut Nicholas L. Rider, DO verfasserin aut In Journal of Allergy and Clinical Immunology: Global Elsevier, 2023 3(2024), 2, Seite 100224- (DE-627)1786549182 (DE-600)3106741-4 27728293 nnns volume:3 year:2024 number:2 pages:100224- https://doi.org/10.1016/j.jacig.2024.100224 kostenfrei https://doaj.org/article/29e51555a77543379d8757b0f61c0338 kostenfrei http://www.sciencedirect.com/science/article/pii/S2772829324000201 kostenfrei https://doaj.org/toc/2772-8293 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2024 2 100224-
allfieldsGer	10.1016/j.jacig.2024.100224 doi (DE-627)DOAJ101457987 (DE-599)DOAJ29e51555a77543379d8757b0f61c0338 DE-627 ger DE-627 rakwb eng RC581-607 Kirk Roberts, PhD verfasserin aut Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection. Natural language processing machine learning text mining inborn errors of immunity primary immunodeficiency diagnosis Immunologic diseases. Allergy Aaron T. Chin, MD verfasserin aut Klaus Loewy, MS verfasserin aut Lisa Pompeii, PhD verfasserin aut Harold Shin, MS verfasserin aut Nicholas L. Rider, DO verfasserin aut In Journal of Allergy and Clinical Immunology: Global Elsevier, 2023 3(2024), 2, Seite 100224- (DE-627)1786549182 (DE-600)3106741-4 27728293 nnns volume:3 year:2024 number:2 pages:100224- https://doi.org/10.1016/j.jacig.2024.100224 kostenfrei https://doaj.org/article/29e51555a77543379d8757b0f61c0338 kostenfrei http://www.sciencedirect.com/science/article/pii/S2772829324000201 kostenfrei https://doaj.org/toc/2772-8293 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2024 2 100224-
allfieldsSound	10.1016/j.jacig.2024.100224 doi (DE-627)DOAJ101457987 (DE-599)DOAJ29e51555a77543379d8757b0f61c0338 DE-627 ger DE-627 rakwb eng RC581-607 Kirk Roberts, PhD verfasserin aut Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection. Natural language processing machine learning text mining inborn errors of immunity primary immunodeficiency diagnosis Immunologic diseases. Allergy Aaron T. Chin, MD verfasserin aut Klaus Loewy, MS verfasserin aut Lisa Pompeii, PhD verfasserin aut Harold Shin, MS verfasserin aut Nicholas L. Rider, DO verfasserin aut In Journal of Allergy and Clinical Immunology: Global Elsevier, 2023 3(2024), 2, Seite 100224- (DE-627)1786549182 (DE-600)3106741-4 27728293 nnns volume:3 year:2024 number:2 pages:100224- https://doi.org/10.1016/j.jacig.2024.100224 kostenfrei https://doaj.org/article/29e51555a77543379d8757b0f61c0338 kostenfrei http://www.sciencedirect.com/science/article/pii/S2772829324000201 kostenfrei https://doaj.org/toc/2772-8293 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 3 2024 2 100224-
language	English
source	In Journal of Allergy and Clinical Immunology: Global 3(2024), 2, Seite 100224- volume:3 year:2024 number:2 pages:100224-
sourceStr	In Journal of Allergy and Clinical Immunology: Global 3(2024), 2, Seite 100224- volume:3 year:2024 number:2 pages:100224-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Natural language processing machine learning text mining inborn errors of immunity primary immunodeficiency diagnosis Immunologic diseases. Allergy
isfreeaccess_bool	true
container_title	Journal of Allergy and Clinical Immunology: Global
authorswithroles_txt_mv	Kirk Roberts, PhD @@aut@@ Aaron T. Chin, MD @@aut@@ Klaus Loewy, MS @@aut@@ Lisa Pompeii, PhD @@aut@@ Harold Shin, MS @@aut@@ Nicholas L. Rider, DO @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
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id	DOAJ101457987
language_de	englisch
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callnumber-first	R - Medicine
author	Kirk Roberts, PhD
spellingShingle	Kirk Roberts, PhD misc RC581-607 misc Natural language processing misc machine learning misc text mining misc inborn errors of immunity misc primary immunodeficiency misc diagnosis misc Immunologic diseases. Allergy Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment
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topic_title	RC581-607 Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment Natural language processing machine learning text mining inborn errors of immunity primary immunodeficiency diagnosis
topic	misc RC581-607 misc Natural language processing misc machine learning misc text mining misc inborn errors of immunity misc primary immunodeficiency misc diagnosis misc Immunologic diseases. Allergy
topic_unstemmed	misc RC581-607 misc Natural language processing misc machine learning misc text mining misc inborn errors of immunity misc primary immunodeficiency misc diagnosis misc Immunologic diseases. Allergy
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title	Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment
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title_full	Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment
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title_sort	natural language processing of clinical notes enables early inborn error of immunity risk ascertainment
callnumber	RC581-607
title_auth	Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment
abstract	Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection.
abstractGer	Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection.
abstract_unstemmed	Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date. Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey. Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center’s electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis. Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve < 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI. Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection.
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title_short	Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment
url	https://doi.org/10.1016/j.jacig.2024.100224 https://doaj.org/article/29e51555a77543379d8757b0f61c0338 http://www.sciencedirect.com/science/article/pii/S2772829324000201 https://doaj.org/toc/2772-8293
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Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment

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