Identifying clinical course patterns in SMS data using cluster analysis. - Test2 - elhamkhani - TriplyDB

Identifying clinical course patterns in SMS data using cluster analysis.

Identifying clinical course patterns in SMS data using cluster analysis.

http://www.wikidata.org/entity/Q34322746

about

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB.Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.Feasibility, acceptability and validity of SMS text messaging for measuring change in depression during a randomised controlled trial.Validity of single item responses to short message service texts to monitor depression: an mHealth sub-study of the UK ACUDep trial.Degenerative Pathways of Lumbar Motion Segments--A Comparison in Two Samples of Patients with Persistent Low Back Pain.Cancer, Warts, or Asymptomatic Infections: Clinical Presentation Matches Codon Usage Preferences in Human Papillomaviruses Psychophysiological responses to visceral and somatic pain in functional chest pain identify clinically relevant pain clusters.Long-term trajectories of patients with neck pain and low back pain presenting to chiropractic care: A latent class growth analysis.What have we learned from ten years of trajectory research in low back pain?Transitioning from Acute to Chronic Pain: An Examination of Different Trajectories of Low-Back Pain.

P2860

Q30856873-88E0872C-7CB3-4802-9C11-F937FD0347B3 Q31034668-9993B4B9-B9C1-411F-A88D-924731F247E1 Q35336562-887F5635-34CF-4BF9-B08C-E15D5C8CD1FD Q35725032-07F97031-B3F3-40A5-BB08-2FA44B0CA71F Q35903087-706A46F4-D51C-439A-9725-A3932E2B67F1 Q36023244-4F236312-B637-4741-9001-D2A8BAD6E560 Q39331045-CB9DCDAB-29A6-4A93-BAE3-6BCD0EE213A4 Q40058072-D8E00EB4-6F99-4BED-80CD-EB0C00A530C2 Q42566655-B5C290AC-3983-41F3-A139-B672130CAF0F Q55430008-02B8D182-3B56-4324-86F9-F9D2FFE69A35

P2860

Identifying clinical course patterns in SMS data using cluster analysis.

http://www.wikidata.org/entity/Q34322746

description

2012 nî lūn-bûn

@nan

2012 թուականի Յուլիսին հրատարակուած գիտական յօդուած

@hyw

2012 թվականի հուլիսին հրատարակված գիտական հոդված

@hy

2012年の論文

@ja

2012年論文

@yue

2012年論文

@zh-hant

2012年論文

@zh-hk

2012年論文

@zh-mo

2012年論文

@zh-tw

2012年论文

@wuu

name

Identifying clinical course patterns in SMS data using cluster analysis.

@ast

Identifying clinical course patterns in SMS data using cluster analysis.

@en

Identifying clinical course patterns in SMS data using cluster analysis.

@nl

type

label

Identifying clinical course patterns in SMS data using cluster analysis.

@ast

Identifying clinical course patterns in SMS data using cluster analysis.

@en

Identifying clinical course patterns in SMS data using cluster analysis.

@nl

prefLabel

Identifying clinical course patterns in SMS data using cluster analysis.

@ast

Identifying clinical course patterns in SMS data using cluster analysis.

@en

Identifying clinical course patterns in SMS data using cluster analysis.

@nl

P1433

Q34322746-763E0FF0-E388-497F-9025-A500D876DC2B

P1476

Q34322746-060838DC-9395-4BF2-A786-C278155A2216

P2860

Q34322746-0A14F39F-89FB-4957-A843-1399C0F769E0

Q34322746-32F81F94-C28F-408A-B8CE-D1BBDCAC365E

Q34322746-330C578F-DD01-4F51-BD36-496B017191B2

Q34322746-34E65673-BA0A-4246-A750-C90D45D54030

Q34322746-581494BC-F92E-4F76-B0A1-05B31DC04218

Q34322746-6520DB23-A35F-4097-83BD-D25F5C70B1A2

Q34322746-6E090334-67F1-4E7F-8DA4-7F424B60D374

Q34322746-720771A2-E3BA-4DE3-BB5F-00CDF07F04CA

Q34322746-7DD5684B-73C8-4161-9F10-16B47CC6B8A3

Q34322746-98AFBD88-1E74-4B49-AFCA-D647A238255A

P2888

Q34322746-F3B708F9-D339-46AA-BDD2-6EAECCD5089E

P304

Q34322746-EF716867-188B-4EBB-BD24-729BCA5EDDD7

P31

Q34322746-4C732485-3212-4C47-A5BA-D90873BE83C4

P356

Q34322746-0D90D6A6-948D-43DA-9356-68729D71A41F

P433

Q34322746-5D9CCFBF-5FD0-4AE6-8F64-049050F60441

P478

Q34322746-94BD5CA4-2835-415D-99C3-21291EC7CA89

P50

Q34322746-D114E01C-D7C2-4C2C-BDB7-4A27A902905A

Q34322746-D545281E-45B0-4A17-9B14-B876E234E9E5

P577

Q34322746-AFE6F48B-DB05-4B0B-8651-5861B28276AD

P5875

Q34322746-80AB5835-FD2C-4289-9AE6-054D6B3BA5C3

P6179

Q34322746-1D200C43-A185-4959-9896-742DA55F133B

P698

Q34322746-B88131DA-99DD-4EE9-BA35-FDEB4C6D844B

P932

Q34322746-BA888A6D-0B61-4F0A-8E45-13FC775E4AF1

P356

2045-709X-20-20

P1433

Chiropractic & manual therapies

P1476

Identifying clinical course patterns in SMS data using cluster analysis.

@en

P2860

Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data

Comparison between data obtained through real-time data capture by SMS and a retrospective telephone interview.

The Nordic back pain subpopulation program: course patterns established through weekly follow-ups in patients treated for low back pain.

Clustering patients on the basis of their individual course of low back pain over a six month period.

Prevalence of pain-free weeks in chiropractic subjects with low back pain - a longitudinal study using data gathered with text messages.

Rest versus exercise as treatment for patients with low back pain and Modic changes. A randomized controlled clinical trial.

A simulation study of the number of events per variable in logistic regression analysis.

Trajectories of pain in adolescents: a prospective cohort study.

Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial.

The recovery patterns of back pain among workers with compensated occupational back injuries

P2888

2045-709x-20-20

P304

20

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1186/2045-709X-20-20

http://www.w3.org/2001/XMLSchema#string

P433

1

http://www.w3.org/2001/XMLSchema#string

P478

20

http://www.w3.org/2001/XMLSchema#string

P50

P577

2012-07-02T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

228099900

http://www.w3.org/2001/XMLSchema#string

P6179

1014349172

http://www.w3.org/2001/XMLSchema#string

P698

22748197

http://www.w3.org/2001/XMLSchema#string

P932

3502127

http://www.w3.org/2001/XMLSchema#string