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Volumn 74, Issue 12, 2017, Pages 1183-1184

Bigger Data, Harder Questions-Opportunities Throughout Mental Health Care

Author keywords

[No Author keywords available]

Indexed keywords

BIPOLAR DEPRESSION; BIPOLAR DISORDER; CLINICAL DECISION MAKING; CLINICAL DECISION SUPPORT SYSTEM; CLINICAL FEATURE; CLINICAL OUTCOME; CLINICAL PRACTICE; CLINICAL RESEARCH; CLINICAL TRIAL (TOPIC); EDITORIAL; HEALTH CARE PERSONNEL; HUMAN; MACHINE LEARNING; MAJOR DEPRESSION; MENTAL HEALTH CARE; PATIENT COMPLIANCE; TREATMENT INDICATION; TREATMENT OUTCOME; MENTAL HEALTH SERVICE; METHODOLOGY; STANDARDS;

EID: 85039854014     PISSN: 2168622X     EISSN: None     Source Type: Journal    
DOI: 10.1001/jamapsychiatry.2017.3333     Document Type: Editorial
Times cited : (22)

References (7)
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    • Torous J, Baker JT.Why psychiatry needs data science and data science needs psychiatry: connecting with technology. JAMA Psychiatry. 2016;73(1):3-4. doi:10.1001/jamapsychiatry.2015.2622
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    • Torous, J.1    Baker, J.T.2
  • 2
    • 84945385256 scopus 로고    scopus 로고
    • Narrowing the gaps between what we knowand whatwe do in psychiatry
    • Rush AJ. Narrowing the gaps between what we knowand whatwe do in psychiatry. J Clin Psychiatry. 2015;76(10):1366-1372. doi:10.4088/JCP.15ac10309
    • (2015) J Clin Psychiatry , vol.76 , Issue.10 , pp. 1366-1372
    • Rush, A.J.1
  • 3
    • 33645000685 scopus 로고    scopus 로고
    • Clinical features of bipolar depression versus major depressive disorder in large multicenter trials
    • Perlis RH, Brown E, Baker RW, Nierenberg AA. Clinical features of bipolar depression versus major depressive disorder in large multicenter trials. Am J Psychiatry. 2006;163(2):225-231. doi:10.1176/appi .ajp.163.2.225
    • (2006) Am J Psychiatry , vol.163 , Issue.2 , pp. 225-231
    • Perlis, R.H.1    Brown, E.2    Baker, R.W.3    Nierenberg, A.A.4
  • 4
    • 84959571586 scopus 로고    scopus 로고
    • Cross-trial prediction of treatment outcome in depression: A machine learning approach
    • Chekroud AM, Zotti RJ, Shehzad Z, et al. Cross-trial prediction of treatment outcome in depression: A machine learning approach. Lancet Psychiatry. 2016;3(3):243-250. doi:10.1016/S2215-0366(15)00471-X
    • (2016) Lancet Psychiatry , vol.3 , Issue.3 , pp. 243-250
    • Chekroud, A.M.1    Zotti, R.J.2    Shehzad, Z.3
  • 5
    • 85017596150 scopus 로고    scopus 로고
    • Reevaluating the efficacy and predictability of antidepressant treatments: A symptom clustering approach
    • Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, McCarthy G. Reevaluating the efficacy and predictability of antidepressant treatments: A symptom clustering approach. JAMA Psychiatry. 2017;74(4):370-378. doi:10.1001/jamapsychiatry.2017.0025
    • (2017) JAMA Psychiatry , vol.74 , Issue.4 , pp. 370-378
    • Chekroud, A.M.1    Gueorguieva, R.2    Krumholz, H.M.3    Trivedi, M.H.4    Krystal, J.H.5    McCarthy, G.6
  • 6
    • 84968813824 scopus 로고    scopus 로고
    • Deep patient: An unsupervised representation to predict the future of patients from the electronic health records
    • 26094
    • Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016;6(26094):26094. doi:10.1038/srep26094
    • (2016) Sci Rep , vol.6 , pp. 26094
    • Miotto, R.1    Li, L.2    Kidd, B.A.3    Dudley, J.T.4
  • 7
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    • Evidence-based pragmatic psychiatry-A call to action [published online November 1, 2017]
    • doi: 10.1001/jamapsychiatry.2017.2439
    • Paulus MP. Evidence-based pragmatic psychiatry-a call to action [published online November 1, 2017]. JAMA Psychiatry. doi: 10.1001/jamapsychiatry.2017.2439
    • JAMA Psychiatry
    • Paulus, M.P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.