Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study.
Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study.
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DOI：
10.2196/10144
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发表时间：
2018-06-22
影响因子：
5.2
通讯作者：
Travieso CM
中科院分区：
文献类型：
--
作者：
DelPozo-Banos M;John A;Petkov N;Berridge DM;Southern K;LLoyd K;Jones C;Spencer S;Travieso CM
每年，全世界约有80万人死于自杀，占每100例死亡中的1-2例。它总是一个悲剧性的事件，对家庭，朋友，社区和卫生专业人员产生巨大的影响。不幸的是，自杀预防和风险评估工具的发展一直受到潜在机制的复杂性和一个人的动机和意图的动态性质的阻碍。许多死于自杀的人在前一年与卫生服务部门有过接触，但确定那些风险最大的人仍然是一个挑战。探讨使用人工神经网络与常规收集的电子健康记录，以支持识别那些在与卫生服务接触的自杀高危人群的可行性。使用英国安全匿名信息链接数据库，我们提取了2001年至2015年期间死于自杀的人的数据和配对对照。通过查看初级（全科）和二级（入院）电子健康记录，我们构建了一个二进制特征向量，对死亡前不同时间的风险因素进行编码。风险因素包括：全科联系和住院;精神健康问题诊断;伤害和中毒;药物滥用;虐待;睡眠障碍;以及阿片类药物和精神药物处方。基本的人工神经网络被训练来区分自杀病例和配对对照。我们将输出分数解释为估计的自杀风险。通过10 × 10倍重复交叉验证评估系统性能，并通过表示病例和对照组之间的估计风险分布以及估计风险之间的因素分布来研究其行为。我们总共抽取了2604例自杀病例和20对对照。我们最好的系统达到了平均错误率为26.78%（SD 1.46; 64.57%的灵敏度和81.86%的特异性）。虽然对照组的分布集中在估计风险< 0.5左右，但病例几乎均匀分布在0和1之间。精神药物、抑郁和焦虑以及自我伤害的处方使估计风险增加约0.4。至少有95%的这些因素被确定为自杀病例。尽管实现的系统的简单性，所提出的方法获得的准确性像其他公布的方法的基础上，专门的问卷调查生成的数据。大多数错误来自自杀病例所显示的模式的异质性，其中一些与配对对照组的模式相同。精神药物、抑郁和焦虑以及自我伤害的处方与较高的估计风险评分密切相关，其次是住院和长期药物和酒精滥用。其他风险因素如睡眠障碍和虐待有更复杂的影响。
Each year, approximately 800,000 people die by suicide worldwide, accounting for 1–2 in every 100 deaths. It is always a tragic event with a huge impact on family, friends, the community and health professionals. Unfortunately, suicide prevention and the development of risk assessment tools have been hindered by the complexity of the underlying mechanisms and the dynamic nature of a person’s motivation and intent. Many of those who die by suicide had contact with health services in the preceding year but identifying those most at risk remains a challenge. To explore the feasibility of using artificial neural networks with routinely collected electronic health records to support the identification of those at high risk of suicide when in contact with health services. Using the Secure Anonymised Information Linkage Databank UK, we extracted the data of those who died by suicide between 2001 and 2015 and paired controls. Looking at primary (general practice) and secondary (hospital admissions) electronic health records, we built a binary feature vector coding the presence of risk factors at different times prior to death. Risk factors included: general practice contact and hospital admission; diagnosis of mental health issues; injury and poisoning; substance misuse; maltreatment; sleep disorders; and the prescription of opiates and psychotropics. Basic artificial neural networks were trained to differentiate between the suicide cases and paired controls. We interpreted the output score as the estimated suicide risk. System performance was assessed with 10x10-fold repeated cross-validation, and its behavior was studied by representing the distribution of estimated risk across the cases and controls, and the distribution of factors across estimated risks. We extracted a total of 2604 suicide cases and 20 paired controls per case. Our best system attained a mean error rate of 26.78% (SD 1.46; 64.57% of sensitivity and 81.86% of specificity). While the distribution of controls was concentrated around estimated risks < 0.5, cases were almost uniformly distributed between 0 and 1. Prescription of psychotropics, depression and anxiety, and self-harm increased the estimated risk by ~0.4. At least 95% of those presenting these factors were identified as suicide cases. Despite the simplicity of the implemented system, the proposed methodology obtained an accuracy like other published methods based on specialized questionnaire generated data. Most of the errors came from the heterogeneity of patterns shown by suicide cases, some of which were identical to those of the paired controls. Prescription of psychotropics, depression and anxiety, and self-harm were strongly linked with higher estimated risk scores, followed by hospital admission and long-term drug and alcohol misuse. Other risk factors like sleep disorders and maltreatment had more complex effects.
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影响因子：
3.5
作者：
Lyons RA;Jones KH;John G;Brooks CJ;Verplancke JP;Ford DV;Brown G;Leake K
通讯作者：
Leake K
影响因子：
4.5
作者：
McKenzie, Kirsten;Scott, Debbie A.
通讯作者：
Scott, Debbie A.
影响因子：
5.5
作者：
Glenn CR;Nock MK
通讯作者：
Nock MK
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
4.6
作者：
Miotto R;Li L;Kidd BA;Dudley JT
通讯作者：
Dudley JT