A network-based classification framework for predicting treatment response of schizophrenia patients

Esfahlani, Farnaz Zamani; Visser, Katherine Frost; Strauss, Gregory P.; Sayama, Hiroki

doi:10.1016/j.eswa.2018.05.005

Cited by 25 publications

(13 citation statements)

References 31 publications

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“…On the one hand, patients who did not respond to treatment had a greater baseline strength of associations within the networks in comparison to patients who responded. This finding, consistent with previous theoretical and empirical studies 15,65 , underlines the importance of evaluating patients' baseline network of symptomatic associations as predictor of treatment outcome, considering that having a strongly interconnected symptom structure at baseline seems to impede the success of a multimodal inpatient treatment for chronic pain. A more intense repeated-measures analysis of the associations between symptoms at baseline within each patient might not only provide personalized prognostic data (i.e.…”

Section: Comparison Between Network Of Responders Versus Non-responderssupporting

confidence: 89%

The Complex Interplay of Pain, Depression, and Anxiety Symptoms in Patients With Chronic Pain

Penedo

Rubel

Blättler

et al. 2019

The Clinical Journal of Pain

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Objectives: This study aimed to analyze the associations among depressive/anxiety and pain symptoms in patients diagnosed with chronic pain. Methods: Four hundred and fifty-four inpatients who were consecutively admitted in a multimodal 3-weeks treatment in a tertiary psychosomatic university clinic completed 25 items from the Brief Pain Inventory and the Hospital Anxiety and Depression Scale at baseline and after treatment termination.Associations among symptoms were explored by network analyses using the graphical least absolute shrinkage and selection operator to estimate their partial correlations, while Extended Bayesian Information Criterion was used to select the best network solution for the data. We explored symptoms' centrality and expected influence within the network as well as the minimum spanning tree for the network. Results: Besides expected associations within depressive/anxiety and pain symptoms, the estimated network showed several local associations between depressive and pain interference symptoms. The lacks of being cheerful and of laughing are two of the depressive symptoms that showed the greatest associations with pain interference and a strong centrality within the network. Sleep problems were both associated with anxiety/depressive symptoms and pain intensity symptoms. Although at posttreatment, most of the symptoms showed a significant decrease, the strength of the associations between the symptoms within the network were significantly higher than at baseline. Discussion: The results support focusing psychosocial interventions in chronic pain treatment not only on reducing pain, anxiety and sleep symptoms but also on enhancing positive affect. Future research is needed to replicate these findings using repeated withinperson measures designs.

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Section: Comparison Between Network Of Responders Versus Non-responderssupporting

confidence: 89%

The Complex Interplay of Pain, Depression, and Anxiety Symptoms in Patients With Chronic Pain

Penedo

Rubel

Blättler

et al. 2019

The Clinical Journal of Pain

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“…Network analysis therefore provides an approach that is complementary with CFA, but not redundant, as these 2 analyses can sometimes yield different results. 34,36 Data from 2 studies were analyzed using network analysis to evaluate the latent structure of negative symptoms. The first study included an American sample of outpatients with SZ (n = 201), and the second included a larger validation sample of SZ outpatients from the Italian national study (n = 912).…”

Section: Introductionmentioning

confidence: 99%

Network Analysis Reveals the Latent Structure of Negative Symptoms in Schizophrenia

Strauss

Esfahlani

Galderisi

et al. 2018

Schizophrenia Bulletin

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Prior studies using exploratory factor analysis provide evidence that negative symptoms are best conceptualized as 2 dimensions reflecting diminished motivation and expression. However, the 2-dimensional model has yet to be evaluated using more complex mathematical techniques capable of testing structure. In the current study, network analysis was applied to evaluate the latent structure of negative symptoms using a community-detection algorithm. Two studies were conducted that included outpatients with schizophrenia (SZ; Study 1: n = 201; Study 2: n = 912) who were rated on the Brief Negative Symptom Scale (BNSS). In both studies, network analysis indicated that the 13 BNSS items divided into 6 negative symptom domains consisting of anhedonia, avolition, asociality, blunted affect, alogia, and lack of normal distress. Separation of these domains was statistically significant with reference to a null model of randomized networks. There has been a recent trend toward conceptualizing the latent structure of negative symptoms in relation to 2 distinct dimensions reflecting diminished expression and motivation. However, the current results obtained using network analysis suggest that the 2-dimensional conceptualization is not complex enough to capture the nature of the negative symptom construct. Similar to recent confirmatory factor analysis studies, network analysis revealed that the latent structure of negative symptom is best conceptualized in relation to the 5 domains identified in the 2005 National Institute of Mental Health consensus development conference (anhedonia, avolition, asociality, blunted affect, and alogia) and potentially a sixth domain consisting of lack of normal distress. Findings have implications for identifying pathophysiological mechanisms and targeted treatments.

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“…Because of contrasts in their implicit processes, the two common feature selection approaches may have their novel predispositions that conceivably lead to dissimilar order viability. The ANOVA,

, and ReliefF algorithms [ [56] , [57] , [58] , [59] , [60] ], are used as a base to reduce the number of features (criteria) that are most valuable to a model. The results of the three algorithms are presented in Fig.…”

Section: Methodsmentioning

confidence: 99%

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

Albahri

Hamid

Albahri

et al. 2021

Artificial Intelligence in Medicine

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Highlights Develop a new framework that can handle the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers. The most important laboratory criteria were selected and implemented based on two feature selection approaches (i.e. data-driven and knowledge-driven). A new decision matrix was developed based on a crossover of (i) multi-laboratory characteristics criteria and (ii) lists of infected patients for patient prioritisation using integration Entropy-TOPSIS methods. Advantages of new framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

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A network-based classification framework for predicting treatment response of schizophrenia patients

Cited by 25 publications

References 31 publications

The Complex Interplay of Pain, Depression, and Anxiety Symptoms in Patients With Chronic Pain

The Complex Interplay of Pain, Depression, and Anxiety Symptoms in Patients With Chronic Pain

Network Analysis Reveals the Latent Structure of Negative Symptoms in Schizophrenia

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

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