***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: A genetic risk score could be beneficial in assisting clinical diagnosis for complex diseases with high heritability . With large-scale genome-wide association ( GWA ) data , the current study constructed a genetic risk model with a machine learning approach for  bipolar disorder ( BPD ) . The GWA data set of BPD from the Genetic Association Information Network was used as the training data for model construction , and the Systematic Treatment Enhancement Program ( STEP ) GWA data were used as the validation data set . A r and om forest algorithm was applied for pre-filtered markers , and variable importance indices were assessed . 289 c and i date markers were selected by r and om forest procedures with good discriminability ; the area under the receiver operating characteristic curve was 0.944 ( 0.935–0.953 ) in the training set and 0.702 ( 0.681–0.723 ) in the STEP data set . Using a score with the cutoff of 184 , the sensitivity and specificity for BPD was 0.777 and 0.854 , respectively . Pathway analyses revealed important biological pathways for identified genes . In conclusion , the present study identified informative genetic markers to differentiate BPD from healthy controls  with acceptable discriminability in the validation data set . In the future , diagnosis classification can be further improved by assessing more comprehensive clinical risk factors and jointly analysing them with genetic data in large sample Background Mood disorders are highly heritable forms of major mental illness . A major breakthrough in elucidating the genetic architecture of mood disorders was anticipated with the advent of genome-wide association studies ( GWAS ) . However , to date few susceptibility loci have been conclusively identified . The genetic etiology of mood disorders appears to be quite complex , and as a result , alternative approaches for analyzing GWAS data are needed . Recently , a polygenic scoring approach that captures the effects of alleles across multiple loci was successfully applied to the analysis of GWAS data in schizophrenia and bipolar disorder ( BP ) . However , this method may be overly simplistic in its approach to the complexity of genetic effects . Data mining methods are available that may be applied to analyze the high dimensional data generated by GWAS of complex psychiatric disorders . Results We sought to compare the performance of five data mining methods , namely , Bayesian networks , support vector machine , r and om forest , radial basis function network , and logistic regression , against the polygenic scoring approach in the analysis of  GWAS data on BP . The different classification methods were trained on GWAS data sets from the Bipolar Genome Study ( 2191 cases with BP and 1434 controls ) and their ability to accurately classify case/control status was tested on a GWAS data set from the Wellcome Trust Case Control Consortium . Conclusion The performance of the classifiers in the test data set was evaluated by comparing area under the receiver operating characteristic curves . Bayesian networks performed the best of all the data mining classifiers , but none of these did significantly better than the polygenic score approach . We further examined a subset of single-nucleotide polymorphisms ( SNPs ) in genes that are expressed in the brain , under the hypothesis that these might be most relevant to BP susceptibility , but all the classifiers performed worse with this reduced set of SNPs . The discriminative accuracy of all of these methods is unlikely to be of diagnostic or clinical utility at the present time . Further research is needed to develop strategies for selecting sets of SNPs likely to be relevant to disease susceptibility and to determine if other data mining classifiers that utilize other algorithms for inferring relationships among the sets of SNPs may perform better OBJECTIVES Recent research indicates that Heart Rate Variability ( HRV ) is affected in Bipolar Disorders ( BD ) patients . To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course , we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state . METHODS Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project . We extracted time-domain , frequency-domain and non-linear HRV-features and trained a Support Vector Machine ( SVM ) to classify HRV-features according to mood state . To evaluate the influence of adjacent mood states , we trained SVM with different HRV-feature sets : 1 ) belonging to each mood state considered alone ; 2 ) belonging to each mood state and normalized using information from the preceding mood state ; 3 ) belonging to each mood state and normalized using information from the preceding and subsequent mood states ; 4 ) belonging to each mood state and normalized using information from two r and omly chosen states . RESULTS SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered . CONCLUSIONS Although preliminary and in need of replications our results suggest for the first time that psychophysiological states in BD contain information related to the subsequent ones . Such characteristic may be used to improve clinical management and to develop algorithms to predict clinical course and mood switches in individual patients ABSTRACT Improved clinical care for Bipolar Disorder ( BD ) relies on the identification of diagnostic markers that can reliably detect disease‐related signals in clinical ly heterogeneous population s. At the very least , diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology , most commonly Major Depressive Disorder ( MDD ) . These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient . We therefore applied pattern classification to task‐based functional magnetic resonance imaging ( fMRI ) data of the n‐back working memory task , to test their predictive value in differentiating patients with BD ( n=30 ) from healthy individuals ( n=30 ) and from patients ’ relatives who were either diagnosed with MDD ( n=30 ) or were free of any personal lifetime history of psychopathology ( n=30 ) . Diagnostic stability in these groups was confirmed with 4‐year prospect i ve follow‐up . Task‐based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers ( GPC ) , a machine learning approach to detecting multivariate patterns in neuroimaging data sets . Consistent significant classification results were only obtained using data from the 3‐back versus 0‐back contrast . Using contrast , patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5 % , sensitivity of 84.6 % and specificity of 92.3 % . Classification accuracy , sensitivity and specificity when comparing patients with BD to their relatives with MDD , were respectively 73.1 % , 53.9 % and 94.5 % . Classification accuracy , sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8 % , 72.7 % and 90.9 % . We show that significant individual classification can be achieved using whole brain pattern analysis of task‐based working memory fMRI data . The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis . HighlightsNeuroimaging may aid diagnosis and prognosis in situations of clinical uncertainty . We test the translational potential of Gaussian Process Classifiers ( GPC ) in Bipolar Disorder (BD).We evaluate GPC performance in differentiating BD patients from their MDD and healthy relatives . Classification was based on task‐based functional MRI data of working memory . The GPC approach achieved high classification accuracy ( over 70 % ) and specificity ( over 90 % ) Background Late onset bipolar disorder ( LOBD ) is often difficult to distinguish from degenerative dementias , such as Alzheimer disease ( AD ) , due to comorbidities and common cognitive symptoms . Moreover , LOBD prevalence in the elder population is not negligible and it is increasing . Both pathologies share pathophysiological neuroinflammation features . Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment . Objective The aim of this study is to assess the relative significance of clinical observations , neuropsychological tests , and specific blood plasma biomarkers ( inflammatory and neurotrophic ) , separately and combined , in the differential diagnosis of LOBD versus AD . It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis ( CAD ) systems based on these variables . Material s A sample of healthy controls ( HC ) ( n = 26 ) , AD patients ( n = 37 ) , and LOBD patients ( n = 32 ) was recruited at the Alava University Hospital .  Clinical observations , neuropsychological tests , and plasma biomarkers were measured at recruitment time . Methods We applied multivariate machine learning classification methods to discriminate subjects from HC , AD , and LOBD population s in the study . We analyzed , for each classification contrast , feature sets combining clinical observations , neuropsychological measures , and biological markers , including inflammation biomarkers . Furthermore , we analyzed reduced feature sets containing variables with significative differences determined by a Welch ’s t-test . Furthermore , a battery of classifier architectures were applied , encompassing linear and non-linear Support Vector Machines ( SVM ) , R and om Forests ( RF ) , Classification and regression trees ( CART ) , and their performance was evaluated in a leave-one-out ( LOO ) cross-validation scheme . Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance . Results Welch ’s t-test found one biomarker ( Malondialdehyde ) with significative differences ( p < 0.001 ) in LOBD vs. AD contrast . Classification results with the best features are as follows : discrimination of HC vs. AD patients reaches accuracy 97.21 % and AUC 98.17 % . Discrimination of LOBD vs. AD patients reaches accuracy 90.26 % and AUC 89.57 % . Discrimination of HC vs LOBD patients achieves accuracy 95.76 % and AUC 88.46 % . Conclusion It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables . Clinical observations provide the greatest discrimination . Neuropsychological tests are improved by the addition of biomarkers , and both contribute significantly to improve the overall predictive performance CONTEXT Estimates of lifetime risk of suicide in mental disorders were based on selected sample s with incomplete follow-up . OBJECTIVE To estimate , in a national cohort , the absolute risk of suicide within 36 years after the first psychiatric contact . DESIGN Prospect i ve study of incident cases followed up for as long as 36 years . Median follow-up was 18 years . SETTING Individual data drawn from Danish longitudinal registers . PARTICIPANTS A total of 176,347 persons born from January 1 , 1955 , through December 31 , 1991 , were followed up from their first contact with secondary mental health services after 15 years of age until death , emigration , disappearance , or the end of 2006 . For each participant , 5 matched control individuals were included . MAIN OUTCOME MEASURES Absolute risk of suicide in percentage of individuals up to 36 years after the first contact . RESULTS Among men , the absolute risk of suicide ( 95 % confidence interval [ CI ] ) was highest for bipolar disorder , ( 7.77 % ; 6.01%-10.05 % ) , followed by unipolar affective disorder ( 6.67 % ; 5.72%-7.78 % ) and schizophrenia ( 6.55 % ; 5.85%-7.34 % ) . Among women , the highest risk was found among women with schizophrenia ( 4.91 % ; 95 % CI , 4.03%-5.98 % ) , followed by bipolar disorder ( 4.78 % ; 3.48%-6.56 % ) . In the nonpsychiatric population , the risk was 0.72 % ( 95 % CI , 0.61%-0.86 % ) for men and 0.26 % ( 0.20%-0.35 % ) for women . Comorbid substance abuse and comorbid unipolar affective disorder significantly increased the risk . The co-occurrence of deliberate self-harm increased the risk approximately 2-fold . Men with bipolar disorder and deliberate self-harm had the highest risk ( 17.08 % ; 95 % CI , 11.19%-26.07 % ) . CONCLUSIONS This is the first analysis of the absolute risk of suicide in a total national cohort of individuals followed up from the first psychiatric contact , and it represents , to our knowledge , the hitherto largest sample with the longest and most complete follow-up . Our estimates are lower than those most often cited , but they are still substantial and indicate the continuous need for prevention of suicide among people with mental disorders Although lithium preparations remain first-line treatment for bipolar disorder , risk for development of renal insufficiency may discourage their use . Estimating such risk could allow more informed decisions and facilitate development of prevention strategies . We utilized electronic health records from a large New Engl and health-care system between 2006 and 2013 to identify patients aged 18 years or older with a lithium prescription .  Renal insufficiency was identified using the presence of renal failure by ICD9 code or laboratory-confirmed glomerular filtration rate below 60 ml/min . Logistic regression was used to build a predictive model in a r and om two-thirds of the cohort , which was tested in the remaining one-third . Risks associated with aspects of pharmacotherapy were also examined in the full cohort . We identified 1445 adult lithium-treated patients with renal insufficiency , matched by risk set sampling 1 : 3 with 4306 lithium-exposed patients without renal insufficiency . In regression models , features associated with risk included older age , female sex , history of smoking , history of hy 
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We concluded that given the clinical heterogeneity of sample s of patients with BD , machine learning techniques may provide clinicians and research ers with important insights in fields such as diagnosis , personalized treatment and prognosis orientation