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Generating Positive Psychosis Symptom Keywords from Electronic Health Records

Research output: Chapter in Book/Report/Conference proceedingConference paper

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Generating Positive Psychosis Symptom Keywords from Electronic Health Records. / Viani, Natalia; Patel, Rashmi; Stewart, Robert; Velupillai, Sumithra.

Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. ed. / David Riaño; Szymon Wilk; Annette ten Teije. 2019. p. 298-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference paper

Harvard

Viani, N, Patel, R, Stewart, R & Velupillai, S 2019, Generating Positive Psychosis Symptom Keywords from Electronic Health Records. in D Riaño, S Wilk & A ten Teije (eds), Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11526 LNAI, pp. 298-303. https://doi.org/10.1007/978-3-030-21642-9_38

APA

Viani, N., Patel, R., Stewart, R., & Velupillai, S. (2019). Generating Positive Psychosis Symptom Keywords from Electronic Health Records. In D. Riaño, S. Wilk, & A. ten Teije (Eds.), Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings (pp. 298-303). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI). https://doi.org/10.1007/978-3-030-21642-9_38

Vancouver

Viani N, Patel R, Stewart R, Velupillai S. Generating Positive Psychosis Symptom Keywords from Electronic Health Records. In Riaño D, Wilk S, ten Teije A, editors, Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. 2019. p. 298-303. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-21642-9_38

Author

Viani, Natalia ; Patel, Rashmi ; Stewart, Robert ; Velupillai, Sumithra. / Generating Positive Psychosis Symptom Keywords from Electronic Health Records. Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. editor / David Riaño ; Szymon Wilk ; Annette ten Teije. 2019. pp. 298-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex Download

@inbook{1d1462c8d0364d77836bd22450d0f0a0,
title = "Generating Positive Psychosis Symptom Keywords from Electronic Health Records",
abstract = "The development of Natural Language Processing (NLP) solutions for information extraction from electronic health records (EHRs) has grown in recent years, as most clinically relevant information in EHRs is documented only in free text. One of the core tasks for any NLP system is to extract clinically relevant concepts such as symptoms. This information can then be used for more complex problems such as determining symptom onset, which requires temporal information. In the mental health domain, comprehensive vocabularies for specific disorders are scarce, and rarely contain keywords that reflect real-world terminology use. We explore the use of embedding techniques to automatically generate lexical variants of psychosis symptoms into vocabularies, that can be used in complex downstream NLP tasks. We study the impact of the underlying text material on generating useful lexical entries, experimenting with different corpora and with unigram/bigram models. We also propose a method to automatically compute thresholds for choosing the most relevant terms. Our main contribution is a systematic study of unsupervised vocabulary generation using different corpora for an understudied clinical use-case. Resulting lexicons are publicly available.",
keywords = "Electronic health records, Embedding models, Natural language processing, Schizophrenia",
author = "Natalia Viani and Rashmi Patel and Robert Stewart and Sumithra Velupillai",
year = "2019",
month = "5",
day = "30",
doi = "10.1007/978-3-030-21642-9_38",
language = "English",
isbn = "9783030216412",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "298--303",
editor = "David Ria{\~n}o and Szymon Wilk and {ten Teije}, Annette",
booktitle = "Artificial Intelligence in Medicine",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Generating Positive Psychosis Symptom Keywords from Electronic Health Records

AU - Viani, Natalia

AU - Patel, Rashmi

AU - Stewart, Robert

AU - Velupillai, Sumithra

PY - 2019/5/30

Y1 - 2019/5/30

N2 - The development of Natural Language Processing (NLP) solutions for information extraction from electronic health records (EHRs) has grown in recent years, as most clinically relevant information in EHRs is documented only in free text. One of the core tasks for any NLP system is to extract clinically relevant concepts such as symptoms. This information can then be used for more complex problems such as determining symptom onset, which requires temporal information. In the mental health domain, comprehensive vocabularies for specific disorders are scarce, and rarely contain keywords that reflect real-world terminology use. We explore the use of embedding techniques to automatically generate lexical variants of psychosis symptoms into vocabularies, that can be used in complex downstream NLP tasks. We study the impact of the underlying text material on generating useful lexical entries, experimenting with different corpora and with unigram/bigram models. We also propose a method to automatically compute thresholds for choosing the most relevant terms. Our main contribution is a systematic study of unsupervised vocabulary generation using different corpora for an understudied clinical use-case. Resulting lexicons are publicly available.

AB - The development of Natural Language Processing (NLP) solutions for information extraction from electronic health records (EHRs) has grown in recent years, as most clinically relevant information in EHRs is documented only in free text. One of the core tasks for any NLP system is to extract clinically relevant concepts such as symptoms. This information can then be used for more complex problems such as determining symptom onset, which requires temporal information. In the mental health domain, comprehensive vocabularies for specific disorders are scarce, and rarely contain keywords that reflect real-world terminology use. We explore the use of embedding techniques to automatically generate lexical variants of psychosis symptoms into vocabularies, that can be used in complex downstream NLP tasks. We study the impact of the underlying text material on generating useful lexical entries, experimenting with different corpora and with unigram/bigram models. We also propose a method to automatically compute thresholds for choosing the most relevant terms. Our main contribution is a systematic study of unsupervised vocabulary generation using different corpora for an understudied clinical use-case. Resulting lexicons are publicly available.

KW - Electronic health records

KW - Embedding models

KW - Natural language processing

KW - Schizophrenia

UR - http://www.scopus.com/inward/record.url?scp=85068332441&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-21642-9_38

DO - 10.1007/978-3-030-21642-9_38

M3 - Conference paper

SN - 9783030216412

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 298

EP - 303

BT - Artificial Intelligence in Medicine

A2 - Riaño, David

A2 - Wilk, Szymon

A2 - ten Teije, Annette

ER -

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