Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.
Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.
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DOI：
10.1038/s41598-018-25773-2
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发表时间：
2018-05-09
影响因子：
4.6
通讯作者：
Chandran D
中科院分区：
文献类型：
--
作者：
Fernandes AC;Dutta R;Velupillai S;Sanyal J;Stewart R;Chandran D
自杀预防的研究一直受到方法上的限制，如样本量小和回忆偏差。最近，自然语言处理（NLP）策略已与电子健康记录一起使用，以增加从自由文本笔记以及有关自杀倾向的结构化字段中提取的信息，这允许访问比以前可能的更大的队列。本文提出了两种新的NLP方法-基于规则的方法来分类自杀意念的存在和混合机器学习和基于规则的方法来识别精神病临床数据库中的自杀企图。这两个分类器在评估研究中的良好表现表明，它们可以用来准确地检测在这个精神病学数据库中的自由文本文件中提到的自杀意念和企图。这两种方法的新奇在于每个分类器的延展性，如果需要改进性能，或满足替代分类要求。算法还可以被调整以适应给定足够的临床记录实践知识的其他临床数据集的基础设施，而不依赖于医疗代码或已知风险因素的附加数据提取来预测自杀行为。
Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information extraction from free text notes as well as structured fields concerning suicidality and this allows access to much larger cohorts than previously possible. This paper presents two novel NLP approaches – a rule-based approach to classify the presence of suicide ideation and a hybrid machine learning and rule-based approach to identify suicide attempts in a psychiatric clinical database. Good performance of the two classifiers in the evaluation study suggest they can be used to accurately detect mentions of suicide ideation and attempt within free-text documents in this psychiatric database. The novelty of the two approaches lies in the malleability of each classifier if a need to refine performance, or meet alternate classification requirements arises. The algorithms can also be adapted to fit infrastructures of other clinical datasets given sufficient clinical recording practice knowledge, without dependency on medical codes or additional data extraction of known risk factors to predict suicidal behaviour.
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影响因子：
3.5
作者：
Fernandes AC;Cloete D;Broadbent MT;Hayes RD;Chang CK;Jackson RG;Roberts A;Tsang J;Soncul M;Liebscher J;Stewart R;Callard F
通讯作者：
Callard F
DOI：
10.1136/jamia.1994.95236146
发表时间：
1994-03-01
影响因子：
6.4
作者：
FRIEDMAN, C;ALDERSON, PO;JOHNSON, SB
通讯作者：
JOHNSON, SB
影响因子：
4.5
作者：
Juckett, David
通讯作者：
Juckett, David
影响因子：
4.5
作者：
Harkema H;Dowling JN;Thornblade T;Chapman WW
通讯作者：
Chapman WW
影响因子：
5.9
作者：
BECK, AT;KOVACS, M;WEISSMAN, A
通讯作者：
WEISSMAN, A