Facebook language predicts depression in medical records.
Facebook language predicts depression in medical records.
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DOI：
10.1073/pnas.1802331115
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发表时间：
2018-10-30
影响因子：
11.1
通讯作者：
Schwartz HA
中科院分区：
文献类型：
--
作者：
Eichstaedt JC;Smith RJ;Merchant RM;Ungar LH;Crutchley P;Preoţiuc-Pietro D;Asch DA;Schwartz HA
抑郁症是致残和可治疗的，但诊断不足。在这项研究中，我们表明，同意用户在Facebook上分享的内容可以预测他们的医疗记录中未来抑郁症的发生。抑郁症的语言预测包括对典型症状的参考，包括悲伤，孤独，敌意，沉思和自我参考增加。这项研究表明，对社交媒体数据的分析可以用来筛选同意抑郁症的个人。此外，社交媒体内容可以为临床医生指出抑郁症的具体症状。抑郁症是最普遍的精神疾病，诊断和治疗不足，突出表明需要扩大目前筛查方法的范围。在这里，我们使用来自同意个人的Facebook帖子的语言来预测电子医疗记录中记录的抑郁症。我们访问了683名访问大型城市学术急诊室的患者的Facebook状态历史，其中114人在医疗记录中被诊断为抑郁症。仅使用他们第一次诊断抑郁症之前的语言，我们就可以以相当的准确度识别抑郁症患者[曲线下面积（AUC）= 0.69]，近似匹配以医疗记录为基准的筛选调查的准确性。将Facebook数据限制在第一次记录的抑郁症诊断之前的6个月内，对于那些拥有足够Facebook数据的用户来说，预测准确率更高（AUC = 0.72）。显著预测未来的抑郁状态是可能的，只要3个月前，其第一个文件。我们发现，抑郁症的语言预测包括情感（悲伤），人际（孤独，敌意）和认知（专注于自我，沉思）过程。通过社交媒体对同意的个人进行非侵入性抑郁评估可能成为可行的，作为对现有筛查和监测程序的可扩展补充。
Depression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference. This study suggests that an analysis of social media data could be used to screen consenting individuals for depression. Further, social media content may point clinicians to specific symptoms of depression. Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy [area under the curve (AUC) = 0.69], approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures.
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影响因子：
39
作者：
Grande, David;Mitra, Nandita;Shah, Anand;Wan, Fei;Asch, David A.
通讯作者：
Asch, David A.
DOI：
10.1073/pnas.1218772110
发表时间：
2013-04-09
影响因子：
11.1
作者：
Kosinski, Michal;Stillwell, David;Graepel, Thore
通讯作者：
Graepel, Thore
影响因子：
4.4
作者：
Kumar, A;Clark, S;Camargo, CA
通讯作者：
Camargo, CA
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：
6
作者：
Blei, DM;Ng, AY;Jordan, MI
通讯作者：
Jordan, MI