Inferring disease subtypes from clusters in explanation space

Schulz, Marc-André; Chapman-Rounds, Matt; Verma, Manisha; Bzdok, Danilo; Georgatzis, Konstantinos

doi:10.1038/s41598-020-68858-7

Cited by 23 publications

(15 citation statements)

References 21 publications

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“…Finally, new studies should use more sophisticated model explanations to identify the specific brain areas or networks that are driving the age gap at the individual level. 70,71 In conclusion, this systematic review and meta-analysis found evidence to support the hypothesis of significant accelerated brain aging in SCZ, BD, and MDD, with SCZ presenting the largest effect, followed by BD and MDD. The fact that brain-PAD differences are more pronounced in older subjects indicates a greater impact associated with cumulative illness burden.…”

Section: Discussionmentioning

confidence: 63%

Brain age in mood and psychotic disorders: a systematic review and meta‐analysis

Ballester

Romano

Cardoso

et al. 2021

Acta Psychiatr Scand

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Objective: To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders.Methods:A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder.Results: A total of 18 papers were included. The random-effects model metaanalysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals. Conclusion:Individuals with mood and psychotic disorders may undergo a pro-cess of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.

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Section: Discussionmentioning

confidence: 63%

Brain age in mood and psychotic disorders: a systematic review and meta‐analysis

Ballester

Romano

Cardoso

et al. 2021

Acta Psychiatr Scand

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“…Recently, new local explanation methods have been developed, including SHapley Additive exPlanations (SHAP), to explain variable contributions at the individual level [42]. Adaptations of this, such as TreeExplainer, leverage the internal structure of treebased models to efficiently compute local explanations using Shapley values [43].…”

Section: Discussionmentioning

confidence: 99%

Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy

et al. 2022

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Although reducing criminal outcomes in individuals with mental illness have long been a priority for governments worldwide, there is still a lack of objective and highly accurate tools that can predict these events at an individual level. Predictive machine learning models may provide a unique opportunity to identify those at the highest risk of criminal activity and facilitate personalized rehabilitation strategies. Therefore, this systematic review and meta-analysis aims to describe the diagnostic accuracy of studies using machine learning techniques to predict criminal and violent outcomes in psychiatry. We performed meta-analyses using the mada, meta, and dmetatools packages in R to predict criminal and violent outcomes in psychiatric patients (n = 2428) (Registration Number: CRD42019127169) by searching PubMed, Scopus, and Web of Science for articles published in any language up to April 2022. Twenty studies were included in the systematic review. Overall, studies used single-nucleotide polymorphisms, text analysis, psychometric scales, hospital records, and resting-state regional cerebral blood flow to build predictive models. Of the studies described in the systematic review, nine were included in the present meta-analysis. The area under the curve (AUC) for predicting violent and criminal outcomes in psychiatry was 0.816 (95% Confidence Interval (CI): 70.57–88.15), with a partial AUC of 0.773, and average sensitivity of 73.33% (95% CI: 64.09–79.63), and average specificity of 72.90% (95% CI: 63.98–79.66), respectively. Furthermore, the pooled accuracy across models was 71.45% (95% CI: 60.88–83.86), with a tau squared (τ2) of 0.0424 (95% CI: 0.0184–0.1553). Based on available evidence, we suggest that prospective models include evidence-based risk factors identified in prior actuarial models. Moreover, there is a need for a greater emphasis on identifying biological features and incorporating novel variables which have not been explored in prior literature. Furthermore, available models remain preliminary, and prospective validation with independent datasets, and across cultures, will be required prior to clinical implementation. Nonetheless, predictive machine learning models hold promise in providing clinicians and researchers with actionable tools to improve how we prevent, detect, or intervene in relevant crime and violent-related outcomes in psychiatry.

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“…To select disease-specific variability, recent contributions propose hybrid approaches integrating a supervised task (patient vs. controls) to the clustering problem. In [22], authors propose a hybrid method for disease-subtyping in precision medicine. Their implementation consists of training a Random Forest supervised classifier (healthy vs. diseased) and then apply SHAP algorithm [11,10] to get explanation values from Random Forest classifiers.…”

Section: Related Workmentioning

confidence: 99%

UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning

Louiset¹,

Gori²,

Dufumier³

et al. 2021

Preprint

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Subtype Discovery consists in finding interpretable and consistent subparts of a dataset, which are also relevant to a certain supervised task. From a mathematical point of view, this can be defined as a clustering task driven by supervised learning in order to uncover subgroups in line with the supervised prediction. In this paper, we propose a general Expectation-Maximization ensemble framework entitled UCSL (Unsupervised Clustering driven by Supervised Learning). Our method is generic, it can integrate any clustering method and can be driven by both binary classification and regression. We propose to construct a non-linear model by merging multiple linear estimators, one per cluster. Each hyperplane is estimated so that it correctly discriminates -or predictonly one cluster. We use SVC or Logistic Regression for classification and SVR for regression. Furthermore, to perform cluster analysis within a more suitable space, we also propose a dimension-reduction algorithm that projects the data onto an orthonormal space relevant to the supervised task. We analyze the robustness and generalization capability of our algorithm using synthetic and experimental datasets. In particular, we validate its ability to identify suitable consistent sub-types by conducting a psychiatric-diseases cluster analysis with known ground-truth labels. The gain of the proposed method over previous state-of-theart techniques is about +1.9 points in terms of balanced accuracy. Finally, we make codes and examples available in a scikit-learn-compatible Python package. https://github.com/neurospin-projects/2021 rlouiset ucsl/

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Inferring disease subtypes from clusters in explanation space

Cited by 23 publications

References 21 publications

Brain age in mood and psychotic disorders: a systematic review and meta‐analysis

Brain age in mood and psychotic disorders: a systematic review and meta‐analysis

Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy

UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning

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