Big data are coming to psychiatry: a general introduction

Monteith, Scott; Glenn, Tasha; Geddes, John; Bauer, Michael

doi:10.1186/s40345-015-0038-9

Cited by 78 publications

(67 citation statements)

References 108 publications

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“…융합되어 건강 빅데이터로 재생산된다 (Monteith et al, 2015). 모바일 헬스(m-Health)는 모바일 기기를 통해 개 개인의 건강 데이터를 실시간으로 모니터링하고 수집하여 정상인 단위의 데이터를 축적할 수 있는 새로운 길을 열어주 었다 (Bakker et al, 2016).…”

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Mobile Health (m-health) on Mental Health

Jang

Cho

2016

Korean J Str Res

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Recently, the demand for mental health services using information and communication technology (ICT) such as mobile communication and social network service (SNS) is increasing. Mobile health (m-Health) and health big data are expected to play a major role in driving the future healthcare paradigm. In particular, the value of applications utilizing smart devices including smartphone and wearable devices is increasing day by day. Mobile applications that can be applied to the patient to restore disease in the neuropsychiatric area can be easily provided, and clinicians can also use the clinic in patient diagnosis, evaluation, and treatment. In practical use, it is still a step in the process, so it is necessary to check the stability of the system that operates the data and prevent infringement and leakage of personal information that may occur later. In addition, it is necessary to establish the effectiveness and credibility based on the basis of practical use of mobile health (m-Health) and health big data. With technological advances being made day by day, mental health care workers should be aware of this trend and have an active interest.

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Mobile Health (m-health) on Mental Health

Jang

Cho

2016

Korean J Str Res

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“…The wide implementation of EHR systems provides a novel opportunity to psychiatric research and practice to embrace the "big data" era with rapidly accumulating psychiatric notes available digitally. 22 Both the performance of exact matching and inexact matching are reported (%).…”

Section: Discussionmentioning

confidence: 99%

Psychiatric stressor recognition from clinical notes to reveal association with suicide

Zhang

et al. 2018

Health Informatics J

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Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden globally. One important type of suicide risk factor is psychiatric stress. Prior studies mainly use survey data to investigate the association between suicide and stressors. Very few studies have investigated stressor data in electronic health records, mostly due to the data being recorded in narrative text. This study takes the initiative to automatically extract and classify psychiatric stressors from clinical text using natural language processing–based methods. Suicidal behaviors were also identified by keywords. Then, a statistical association analysis between suicide ideations/attempts and stressors extracted from a clinical corpus is conducted. Experimental results show that our natural language processing method could recognize stressor entities with an F-measure of 89.01 percent. Mentions of suicidal behaviors were identified with an F-measure of 97.3 percent. The top three significant stressors associated with suicide are health, pressure, and death, which are similar to previous studies. This study demonstrates the feasibility of using natural language processing approaches to unlock information from psychiatric notes in electronic health record, to facilitate large-scale studies about associations between suicide and psychiatric stressors.

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“…• Patient empowerment to engage with care decision--making facilitated through improved access to data via technology and data--enabled platforms (e.g. apps, internet, remote monitoring) which facilitate more timely communications with healthcare providers on possible care pathways (Griffiths et al 2012;Hollis et al 2015;Monteith et al 2015;Murdoch & Detsky 2013). An interviewee (INT06) highlighted that this may also enhance citizen autonomy.…”

Section: Who Could Benefit From Health Data?mentioning

confidence: 99%