2 edition of Classification and prediction of outcome of depression found in the catalog.
Classification and prediction of outcome of depression
|Statement||Chairman: J. Angst.|
|Series||Symposia medica Hoechst -- 8, Symposia medica Hoechst -- 8, Symposia medica Hoechst -- 8|
|The Physical Object|
|Pagination||xii, 313 p. :|
|Number of Pages||313|
Psychosocial Predictors of Outcome in Depression. Robert M. A. Hirschfeld. INTRODUCTION. Identification of predictors of outcome is useful because they provide clues to the etiology of depression and its pathogenesis. They are also useful clinically because they enable the physician to formulate a more accurate prognosis. Identified studies based on best practices in the field are evaluated. We found 16 studies predicting outcomes (such as remission) and identifying clusters in patients with identified studies are mostly still in proof-of-concept phase, with small datasets, lack of external validation, and providing single performance metrics.
The classification tree analysis predicting 8-month outcome generated a tree with three splits and four terminal nodes. Percentage reduction in purging frequency over the first 4 weeks of treatment emerged as the first significant predictor (r=–, N=, pCited by: The authors found that the rs variant within the corticotropin-releasing hormone binding protein (CRHBP) gene predicted antidepressant outcomes for remission, response, and symptom change. Patients homozygous for the G allele of rs had greater remission rates, response rates, and symptom by: 9.
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Get this from a library. Classification and prediction of outcome of depression: symposium Schloss Reinhartshausen/Rhein, September 23rdth, [Jules Angst;]. Jules Angst, editor: Classification and Prediction of Outcome of Depression () Barry Blackwell’s review of Katherine Eban’s Bottle of Lies.
The Inside Story of the Generic Drug Boom. brain imaging classification and predictions, and provide an overview of studies, spe ‐ cifically for MDD, that have used magnetic resonance imaging data to either (a) clas‐ sify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients.
Classification and prediction of outcome of depression book, challenges, future directions, andCited by: THE CLASSIFICATION OF DEPRESSION E.S. PAYKEL Department ofPsychiatry, St George's Hospital Medical School, LondonSW17ORE, UK Current concepts ofclassification The classification of depression is a broad topic, sufficient to keep armies of psychiatrists disputing happily for years.
This paper will focus particularly on the concept of anxious Cited by: Improving the Health of Future Generations. Home; About UK Biobank. Key UK Biobank Contacts. UK Biobank Board; Steering Committee. Machine learning in major depression: From classification to treatment outcome prediction.
Gao S(1)(2), Calhoun VD(3)(4), Sui J(1)(2)(5). Author information: (1)Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Cited by: In major depressive disorder, prediction of treatment outcome is an important goal because most patients do not reach remission with their first course of treatment.
We searched PubMed from inception to Aug 6, with the terms (“depression” OR “major depressive disorder”) AND “prediction” AND “outcome” in any field, with no language by: In this study, we review popular machine‐learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual by: A comparison of the Outcome Questionnaire‐45 and Outcome Questionnaire‐30 in classification and prediction of treatment outcome Article in Clinical Psychology & Psychotherapy 13(6) - TRDwas defined by a Montgomery and Åsberg Depression Rating Scale (MADRS) score ≥22 after at least two antidepressive trials.
Response was defined by a decline in MADRSscore by ≥50% and below a threshold of Logistic regression was applied to replicate predictors for TRDamong 16 clinical variables in by: Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification Author links open overlay panel Lana Kambeitz-Ilankovic a Eva M.
Meisenzahl a Carlos Cabral a Sebastian von Saldern a Joseph Kambeitz a Peter Falkai a Hans-Jürgen Cited by: A preliminary study. In Classification and Prediction of Outcome of Depression, Symposium Medicum Hoechst No. 8, pp. –44 Google Scholar Åsberg, M., Cronholm, B., Sjövist, F.
and Tuck, D. Relationship between plasma level and therapeutic effect of by: 1. Although fMRI and sMRI biomarkers have been found to be associated with depression, there are additional studies that have shown the relevance of DTI biomarkers.
41, 47, 58, 93 Additionally, nonimaging measures have also been used in depression. Thus, it is important to study how multimodal MRI in conjunction with nonimaging features affects prediction models of depression.
Cited by: The current study retrospectively examines the outcomes of patients who received group CBT for depression at a psychiatric outpatient clinic between. The present study compared LR and LDA for classification of subjects to groups having different conditions using continuous and categorical variables and different indices to increase accuracy of the prediction based on sample size.
Specifically, real data used to predict depression in cancer patients undergoing chemotherapy and radiotherapy. Prediction of Infertility Treatment Outcomes Using Classification Trees Article (PDF Available) in Studies in Logic 47(60) December with 56 Reads How we measure 'reads'.
Severity of symptoms at the start of treatment is supposed to be an important predictor of treatment outcome as found for social anxiety , depression  and adjustment disorders , but not. BEHAVIOR THERCognitive-Behavioral Group Treatment of Adolescent Depression: Prediction of Outcome GREGORY CLARKE Oregon Health Sciences University HYMAN HOPS, PETER M.
LEWINSOHN, JUDY ANDREWS, JOHN R. SEELEY, JULIE WILLIAMS Oregon Research Institute This paper attempts to identify variables which distinguish depressed adolescents Cited by: In addition, SVM was applied to gray matter images only and therefore it was unclear whether white matter images also contained important information for outcome prediction; indeed a number of studies suggest that response to antidepressant medication is associated with differences in white matter (Xia et al.,Godin et al.,Maller Cited by: The accuracy of prediction was higher in the drug-specific analyses.
In the escitalopram-treated group, 24 out of variables in combination III explained % of variance in MADRS outcome, with BDI item indecisiveness, SCAN hopelessness and preoccupation with death, HRSD items work and interests and depressed mood, and LTE-Q problems with close people contributing most strongly to the Cited by:.
Predicting Generalized Anxiety Disorder Among Women Using Decision Tree Based Classification Article in International Journal of Business Information Systems 29(1):1 January with Reads.Conclusion.
Depression by either measure was a frequent, substantial, and independent predictor of poor outcomes at 3 and 12 months after stroke. Stroke outcomes studies should further examine the predictive value of assessing both depressive symptoms at the time of the stroke and lifetime history of by: The interpretation of the weights in logistic regression differs from the interpretation of the weights in linear regression, since the outcome in logistic regression is a probability between 0 and 1.
The weights do not influence the probability linearly any longer. The weighted sum is transformed by the logistic function to a probability.