TY - CHAP
T1 - Annotating Temporal Relations to Determine the Onset of Psychosis Symptoms
AU - Viani, Natalia
AU - Kam, Joyce
AU - Yin, Lucia
AU - Verma, Somain
AU - Stewart, Robert James
AU - Patel, Rashmi
AU - Velupillai, Sumithra Ulrika
PY - 2019/8/21
Y1 - 2019/8/21
N2 - For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extracted to support clinical research. To achieve this, natural language processing (NLP) methods can be used. Development and evaluation of NLP systems requires manually annotated corpora. We present a corpus of mental health records annotated with temporal relations for psychosis symptoms. We propose a methodology for document selection and manual annotation to detect symptom onset information, and develop an annotated corpus. To assess the utility of the created corpus, we propose a pilot NLP system. To the best of our knowledge, this is the first temporally-annotated corpus tailored to a specific clinical use-case.
AB - For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extracted to support clinical research. To achieve this, natural language processing (NLP) methods can be used. Development and evaluation of NLP systems requires manually annotated corpora. We present a corpus of mental health records annotated with temporal relations for psychosis symptoms. We propose a methodology for document selection and manual annotation to detect symptom onset information, and develop an annotated corpus. To assess the utility of the created corpus, we propose a pilot NLP system. To the best of our knowledge, this is the first temporally-annotated corpus tailored to a specific clinical use-case.
KW - Electronic Health Records
KW - Natural Language Processing
KW - Schizophrenia
UR - http://www.scopus.com/inward/record.url?scp=85071455534&partnerID=8YFLogxK
U2 - 10.3233/SHTI190255
DO - 10.3233/SHTI190255
M3 - Conference paper
T3 - Studies in Health Technology and Informatics
SP - 418
EP - 422
BT - MEDINFO 2019
A2 - Seroussi, Brigitte
A2 - Ohno-Machado, Lucila
A2 - Ohno-Machado, Lucila
A2 - Seroussi, Brigitte
ER -