Evaluation of word embedding models to extract and predict surgical data in breast cancer

Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and...
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Autor*in:	Sgroi, Giuseppe [verfasserIn] Russo, Giulia Maglia, Anna Catanuto, Giuseppe Barry, Peter Karakatsanis, Andreas Rocco, Nicola Pappalardo, Francesco

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Machine learning Word embeddings Word2Vec Breast cancer Natural language processing

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 22(2022), Suppl 14 vom: 16. Nov.
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:Suppl 14 ; day:16 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s12859-022-05038-6

Katalog-ID:	SPR051140098

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520			\|a Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data.
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allfields	10.1186/s12859-022-05038-6 doi (DE-627)SPR051140098 (SPR)s12859-022-05038-6-e DE-627 ger DE-627 rakwb eng Sgroi, Giuseppe verfasserin aut Evaluation of word embedding models to extract and predict surgical data in breast cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data. Machine learning (dpeaa)DE-He213 Word embeddings (dpeaa)DE-He213 Word2Vec (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Russo, Giulia aut Maglia, Anna aut Catanuto, Giuseppe aut Barry, Peter aut Karakatsanis, Andreas aut Rocco, Nicola aut Pappalardo, Francesco aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 22(2022), Suppl 14 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:22 year:2022 number:Suppl 14 day:16 month:11 https://dx.doi.org/10.1186/s12859-022-05038-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_70 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 Suppl 14 16 11
spelling	10.1186/s12859-022-05038-6 doi (DE-627)SPR051140098 (SPR)s12859-022-05038-6-e DE-627 ger DE-627 rakwb eng Sgroi, Giuseppe verfasserin aut Evaluation of word embedding models to extract and predict surgical data in breast cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data. Machine learning (dpeaa)DE-He213 Word embeddings (dpeaa)DE-He213 Word2Vec (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Russo, Giulia aut Maglia, Anna aut Catanuto, Giuseppe aut Barry, Peter aut Karakatsanis, Andreas aut Rocco, Nicola aut Pappalardo, Francesco aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 22(2022), Suppl 14 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:22 year:2022 number:Suppl 14 day:16 month:11 https://dx.doi.org/10.1186/s12859-022-05038-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_70 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 Suppl 14 16 11
allfields_unstemmed	10.1186/s12859-022-05038-6 doi (DE-627)SPR051140098 (SPR)s12859-022-05038-6-e DE-627 ger DE-627 rakwb eng Sgroi, Giuseppe verfasserin aut Evaluation of word embedding models to extract and predict surgical data in breast cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data. Machine learning (dpeaa)DE-He213 Word embeddings (dpeaa)DE-He213 Word2Vec (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Russo, Giulia aut Maglia, Anna aut Catanuto, Giuseppe aut Barry, Peter aut Karakatsanis, Andreas aut Rocco, Nicola aut Pappalardo, Francesco aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 22(2022), Suppl 14 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:22 year:2022 number:Suppl 14 day:16 month:11 https://dx.doi.org/10.1186/s12859-022-05038-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_70 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 Suppl 14 16 11
allfieldsGer	10.1186/s12859-022-05038-6 doi (DE-627)SPR051140098 (SPR)s12859-022-05038-6-e DE-627 ger DE-627 rakwb eng Sgroi, Giuseppe verfasserin aut Evaluation of word embedding models to extract and predict surgical data in breast cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data. Machine learning (dpeaa)DE-He213 Word embeddings (dpeaa)DE-He213 Word2Vec (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Russo, Giulia aut Maglia, Anna aut Catanuto, Giuseppe aut Barry, Peter aut Karakatsanis, Andreas aut Rocco, Nicola aut Pappalardo, Francesco aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 22(2022), Suppl 14 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:22 year:2022 number:Suppl 14 day:16 month:11 https://dx.doi.org/10.1186/s12859-022-05038-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_70 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 Suppl 14 16 11
allfieldsSound	10.1186/s12859-022-05038-6 doi (DE-627)SPR051140098 (SPR)s12859-022-05038-6-e DE-627 ger DE-627 rakwb eng Sgroi, Giuseppe verfasserin aut Evaluation of word embedding models to extract and predict surgical data in breast cancer 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data. Machine learning (dpeaa)DE-He213 Word embeddings (dpeaa)DE-He213 Word2Vec (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Russo, Giulia aut Maglia, Anna aut Catanuto, Giuseppe aut Barry, Peter aut Karakatsanis, Andreas aut Rocco, Nicola aut Pappalardo, Francesco aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 22(2022), Suppl 14 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:22 year:2022 number:Suppl 14 day:16 month:11 https://dx.doi.org/10.1186/s12859-022-05038-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_70 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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 Suppl 14 16 11
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author	Sgroi, Giuseppe
spellingShingle	Sgroi, Giuseppe misc Machine learning misc Word embeddings misc Word2Vec misc Breast cancer misc Natural language processing Evaluation of word embedding models to extract and predict surgical data in breast cancer
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topic_title	Evaluation of word embedding models to extract and predict surgical data in breast cancer Machine learning (dpeaa)DE-He213 Word embeddings (dpeaa)DE-He213 Word2Vec (dpeaa)DE-He213 Breast cancer (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213
topic	misc Machine learning misc Word embeddings misc Word2Vec misc Breast cancer misc Natural language processing
topic_unstemmed	misc Machine learning misc Word embeddings misc Word2Vec misc Breast cancer misc Natural language processing
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title	Evaluation of word embedding models to extract and predict surgical data in breast cancer
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title_full	Evaluation of word embedding models to extract and predict surgical data in breast cancer
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author_browse	Sgroi, Giuseppe Russo, Giulia Maglia, Anna Catanuto, Giuseppe Barry, Peter Karakatsanis, Andreas Rocco, Nicola Pappalardo, Francesco
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title_sort	evaluation of word embedding models to extract and predict surgical data in breast cancer
title_auth	Evaluation of word embedding models to extract and predict surgical data in breast cancer
abstract	Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data. © The Author(s) 2022
abstractGer	Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data. © The Author(s) 2022
abstract_unstemmed	Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data. © The Author(s) 2022
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title_short	Evaluation of word embedding models to extract and predict surgical data in breast cancer
url	https://dx.doi.org/10.1186/s12859-022-05038-6
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author2	Russo, Giulia Maglia, Anna Catanuto, Giuseppe Barry, Peter Karakatsanis, Andreas Rocco, Nicola Pappalardo, Francesco
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However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. 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Evaluation of word embedding models to extract and predict surgical data in breast cancer

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