Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories

Sonia Priou; Natalia Viani; Veshalee Vernugopan; Chloe Tytherleigh; Faduma Abdalla Hassan; Rina Dutta; Trudie Chalder; Sumithra Velupillai

doi:10.3233/SHTI200130

Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories

Sonia Priou, Natalia Viani, Veshalee Vernugopan, Chloe Tytherleigh, Faduma Abdalla Hassan, Rina Dutta, Trudie Chalder, Sumithra Velupillai

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Chronic fatigue syndrome (CFS) is a long-term illness with a wide range of symptoms and condition trajectories. To improve the understanding of these, automated analysis of large amounts of patient data holds promise. Routinely documented assessments are useful for large-scale analysis, however relevant information is mainly in free text. As a first step to extract symptom and condition trajectories, natural language processing (NLP) methods are useful to identify important textual content and relevant information. In this paper, we propose an agnostic NLP method of extracting segments of patients' clinical histories in CFS assessments. Moreover, we present initial results on the advantage of using these segments to quantify and analyse the presence of certain clinically relevant concepts.

Original language	English
Title of host publication	Studies in Health Technology and Informatics
Publisher	IOS Press
Pages	98-102
Number of pages	5
Volume	270
DOIs	https://doi.org/10.3233/SHTI200130
Publication status	Published - 16 Jun 2020

Publication series

Name	STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS
Publisher	IOS Press
ISSN (Print)	0926-9630

Keywords

Chronic Fatigue Syndrome
Clinical Informatics
Electronic Health Records
Natural Language Processing

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10.3233/SHTI200130

Cite this

Priou, S., Viani, N., Vernugopan, V., Tytherleigh, C., Hassan, F. A., Dutta, R., Chalder, T., & Velupillai, S. (2020). Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories. In Studies in Health Technology and Informatics (Vol. 270, pp. 98-102). (STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS). IOS Press. https://doi.org/10.3233/SHTI200130

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abstract = "Chronic fatigue syndrome (CFS) is a long-term illness with a wide range of symptoms and condition trajectories. To improve the understanding of these, automated analysis of large amounts of patient data holds promise. Routinely documented assessments are useful for large-scale analysis, however relevant information is mainly in free text. As a first step to extract symptom and condition trajectories, natural language processing (NLP) methods are useful to identify important textual content and relevant information. In this paper, we propose an agnostic NLP method of extracting segments of patients' clinical histories in CFS assessments. Moreover, we present initial results on the advantage of using these segments to quantify and analyse the presence of certain clinically relevant concepts.",

keywords = "Chronic Fatigue Syndrome, Clinical Informatics, Electronic Health Records, Natural Language Processing",

author = "Sonia Priou and Natalia Viani and Veshalee Vernugopan and Chloe Tytherleigh and Hassan, {Faduma Abdalla} and Rina Dutta and Trudie Chalder and Sumithra Velupillai",

year = "2020",

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doi = "10.3233/SHTI200130",

language = "English",

volume = "270",

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Priou, S, Viani, N, Vernugopan, V, Tytherleigh, C, Hassan, FA, Dutta, R , Chalder, T & Velupillai, S 2020, Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories. in Studies in Health Technology and Informatics. vol. 270, STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS, IOS Press, pp. 98-102. https://doi.org/10.3233/SHTI200130

Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories. / Priou, Sonia; Viani, Natalia; Vernugopan, Veshalee et al.
Studies in Health Technology and Informatics. Vol. 270 IOS Press, 2020. p. 98-102 (STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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T1 - Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories

AU - Priou, Sonia

AU - Viani, Natalia

AU - Vernugopan, Veshalee

AU - Tytherleigh, Chloe

AU - Hassan, Faduma Abdalla

AU - Dutta, Rina

AU - Chalder, Trudie

AU - Velupillai, Sumithra

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KW - Clinical Informatics

KW - Electronic Health Records

KW - Natural Language Processing

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Clinical History Segment Extraction from Chronic Fatigue Syndrome Assessments to Model Disease Trajectories

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