Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study

Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining...
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Autor*in:	Wendy L.M. Leurs [verfasserIn] Loes A.S. Lammers [verfasserIn] Wilma N. Compagner [verfasserIn] Marjolein Groeneveld [verfasserIn] Erik H.H.M. Korsten [verfasserIn] Carolien M.J. van der Linden [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Text mining Natural language processing In-hospital falls Fall prevention Nursing notes Older adults

Übergeordnetes Werk:	In: Aging and Health Research - Elsevier, 2021, 2(2022), 2, Seite 100078-
Übergeordnetes Werk:	volume:2 ; year:2022 ; number:2 ; pages:100078-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ahr.2022.100078

Katalog-ID:	DOAJ022258450

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520			\|a Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall.
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650		4	\|a Natural language processing
650		4	\|a In-hospital falls
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650		4	\|a Older adults
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700	0		\|a Marjolein Groeneveld \|e verfasserin \|4 aut
700	0		\|a Erik H.H.M. Korsten \|e verfasserin \|4 aut
700	0		\|a Carolien M.J. van der Linden \|e verfasserin \|4 aut
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allfields	10.1016/j.ahr.2022.100078 doi (DE-627)DOAJ022258450 (DE-599)DOAJ081c4d0d9b6c45a7a700b056d7fa776e DE-627 ger DE-627 rakwb eng RC952-954.6 Wendy L.M. Leurs verfasserin aut Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall. Text mining Natural language processing In-hospital falls Fall prevention Nursing notes Older adults Geriatrics Loes A.S. Lammers verfasserin aut Wilma N. Compagner verfasserin aut Marjolein Groeneveld verfasserin aut Erik H.H.M. Korsten verfasserin aut Carolien M.J. van der Linden verfasserin aut In Aging and Health Research Elsevier, 2021 2(2022), 2, Seite 100078- (DE-627)176129976X 26670321 nnns volume:2 year:2022 number:2 pages:100078- https://doi.org/10.1016/j.ahr.2022.100078 kostenfrei https://doaj.org/article/081c4d0d9b6c45a7a700b056d7fa776e kostenfrei http://www.sciencedirect.com/science/article/pii/S2667032122000245 kostenfrei https://doaj.org/toc/2667-0321 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 2 2022 2 100078-
spelling	10.1016/j.ahr.2022.100078 doi (DE-627)DOAJ022258450 (DE-599)DOAJ081c4d0d9b6c45a7a700b056d7fa776e DE-627 ger DE-627 rakwb eng RC952-954.6 Wendy L.M. Leurs verfasserin aut Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall. Text mining Natural language processing In-hospital falls Fall prevention Nursing notes Older adults Geriatrics Loes A.S. Lammers verfasserin aut Wilma N. Compagner verfasserin aut Marjolein Groeneveld verfasserin aut Erik H.H.M. Korsten verfasserin aut Carolien M.J. van der Linden verfasserin aut In Aging and Health Research Elsevier, 2021 2(2022), 2, Seite 100078- (DE-627)176129976X 26670321 nnns volume:2 year:2022 number:2 pages:100078- https://doi.org/10.1016/j.ahr.2022.100078 kostenfrei https://doaj.org/article/081c4d0d9b6c45a7a700b056d7fa776e kostenfrei http://www.sciencedirect.com/science/article/pii/S2667032122000245 kostenfrei https://doaj.org/toc/2667-0321 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 2 2022 2 100078-
allfields_unstemmed	10.1016/j.ahr.2022.100078 doi (DE-627)DOAJ022258450 (DE-599)DOAJ081c4d0d9b6c45a7a700b056d7fa776e DE-627 ger DE-627 rakwb eng RC952-954.6 Wendy L.M. Leurs verfasserin aut Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall. Text mining Natural language processing In-hospital falls Fall prevention Nursing notes Older adults Geriatrics Loes A.S. Lammers verfasserin aut Wilma N. Compagner verfasserin aut Marjolein Groeneveld verfasserin aut Erik H.H.M. Korsten verfasserin aut Carolien M.J. van der Linden verfasserin aut In Aging and Health Research Elsevier, 2021 2(2022), 2, Seite 100078- (DE-627)176129976X 26670321 nnns volume:2 year:2022 number:2 pages:100078- https://doi.org/10.1016/j.ahr.2022.100078 kostenfrei https://doaj.org/article/081c4d0d9b6c45a7a700b056d7fa776e kostenfrei http://www.sciencedirect.com/science/article/pii/S2667032122000245 kostenfrei https://doaj.org/toc/2667-0321 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 2 2022 2 100078-
allfieldsGer	10.1016/j.ahr.2022.100078 doi (DE-627)DOAJ022258450 (DE-599)DOAJ081c4d0d9b6c45a7a700b056d7fa776e DE-627 ger DE-627 rakwb eng RC952-954.6 Wendy L.M. Leurs verfasserin aut Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall. Text mining Natural language processing In-hospital falls Fall prevention Nursing notes Older adults Geriatrics Loes A.S. Lammers verfasserin aut Wilma N. Compagner verfasserin aut Marjolein Groeneveld verfasserin aut Erik H.H.M. Korsten verfasserin aut Carolien M.J. van der Linden verfasserin aut In Aging and Health Research Elsevier, 2021 2(2022), 2, Seite 100078- (DE-627)176129976X 26670321 nnns volume:2 year:2022 number:2 pages:100078- https://doi.org/10.1016/j.ahr.2022.100078 kostenfrei https://doaj.org/article/081c4d0d9b6c45a7a700b056d7fa776e kostenfrei http://www.sciencedirect.com/science/article/pii/S2667032122000245 kostenfrei https://doaj.org/toc/2667-0321 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 2 2022 2 100078-
allfieldsSound	10.1016/j.ahr.2022.100078 doi (DE-627)DOAJ022258450 (DE-599)DOAJ081c4d0d9b6c45a7a700b056d7fa776e DE-627 ger DE-627 rakwb eng RC952-954.6 Wendy L.M. Leurs verfasserin aut Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall. Text mining Natural language processing In-hospital falls Fall prevention Nursing notes Older adults Geriatrics Loes A.S. Lammers verfasserin aut Wilma N. Compagner verfasserin aut Marjolein Groeneveld verfasserin aut Erik H.H.M. Korsten verfasserin aut Carolien M.J. van der Linden verfasserin aut In Aging and Health Research Elsevier, 2021 2(2022), 2, Seite 100078- (DE-627)176129976X 26670321 nnns volume:2 year:2022 number:2 pages:100078- https://doi.org/10.1016/j.ahr.2022.100078 kostenfrei https://doaj.org/article/081c4d0d9b6c45a7a700b056d7fa776e kostenfrei http://www.sciencedirect.com/science/article/pii/S2667032122000245 kostenfrei https://doaj.org/toc/2667-0321 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 2 2022 2 100078-
language	English
source	In Aging and Health Research 2(2022), 2, Seite 100078- volume:2 year:2022 number:2 pages:100078-
sourceStr	In Aging and Health Research 2(2022), 2, Seite 100078- volume:2 year:2022 number:2 pages:100078-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Text mining Natural language processing In-hospital falls Fall prevention Nursing notes Older adults Geriatrics
isfreeaccess_bool	true
container_title	Aging and Health Research
authorswithroles_txt_mv	Wendy L.M. Leurs @@aut@@ Loes A.S. Lammers @@aut@@ Wilma N. Compagner @@aut@@ Marjolein Groeneveld @@aut@@ Erik H.H.M. Korsten @@aut@@ Carolien M.J. van der Linden @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	176129976X
id	DOAJ022258450
language_de	englisch
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Leurs</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. 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callnumber-first	R - Medicine
author	Wendy L.M. Leurs
spellingShingle	Wendy L.M. Leurs misc RC952-954.6 misc Text mining misc Natural language processing misc In-hospital falls misc Fall prevention misc Nursing notes misc Older adults misc Geriatrics Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study
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topic_title	RC952-954.6 Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study Text mining Natural language processing In-hospital falls Fall prevention Nursing notes Older adults
topic	misc RC952-954.6 misc Text mining misc Natural language processing misc In-hospital falls misc Fall prevention misc Nursing notes misc Older adults misc Geriatrics
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title	Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study
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title_full	Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study
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author_browse	Wendy L.M. Leurs Loes A.S. Lammers Wilma N. Compagner Marjolein Groeneveld Erik H.H.M. Korsten Carolien M.J. van der Linden
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title_sort	text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: a case-control pilot study
callnumber	RC952-954.6
title_auth	Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study
abstract	Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall.
abstractGer	Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall.
abstract_unstemmed	Background: Falls are common in hospitalized patients, especially in older adults. Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall.
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title_short	Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study
url	https://doi.org/10.1016/j.ahr.2022.100078 https://doaj.org/article/081c4d0d9b6c45a7a700b056d7fa776e http://www.sciencedirect.com/science/article/pii/S2667032122000245 https://doaj.org/toc/2667-0321
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Currently, risk assessment tools lack specificity and sensitivity to be clinically useful. Recently it was discovered free text in nursing notes contains valuable information on fall risk factors. We used text mining techniques to search for any text characteristics (not only related to known risk factors) associated with falls. Methods: In this retrospective case-control pilot study, hospitalized patients aged ≥70 years who experienced a fall were included using incident reports. Controls were matched for sex, age and length of stay. Data were collected from free text in nursing notes 72 h prior to the fall and for similar hours in controls. Number of words, frequencies of single words and word combinations were calculated and compared between both groups. Results: 19 fallers and 19 non-fallers were included, with a total of 362 nursing notes. More words were used in nursing notes in fallers in total (10,523 vs 7,510, p < 0.01) and per nursing note (median 47 vs 34.5, p < 0.01). More unique words were used in fallers (2,465 vs 1,887, p < 0.01). 21 words were associated with falling, including words describing fall prevention and delirium. 8 words and 6 combinations of words were associated with not falling. Conclusions: Text mining in nursing notes can help to find words used more frequently in patients who experienced a fall and is thus a promising method to identify older adults at high risk for a fall.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Text mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Natural language processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">In-hospital falls</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fall prevention</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nursing notes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Older adults</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geriatrics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Loes A.S. 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Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study

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