Mixture Models: Inference and Applications to Clustering.

Geary, D. N.

doi:10.2307/2982840

Cited by 18 publications

(11 citation statements)

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“…The GMM is a probabilistic model that assumes data are generated from a finite set of Gaussian distributions. Gaussian mixture probability density is the weighted sum of k component Gaussian densities [ 30 ]. The GMM can address correlation between attributes by selecting the optimal covariance matrix for each cluster and has been used in previous behavioral clustering problems [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis

Zhou¹,

Lamichhane²,

Ben‐Zeev³

et al. 2022

JMIR Mhealth Uhealth

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Background Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse. Objective In this study, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data for relapse prediction tasks. The identified clusters can represent different routine behavioral trends related to daily living of patients and atypical behavioral trends associated with impending relapse. Methods We used the mobile sensing data obtained from the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (ambient light, sound, conversation, acceleration, etc) obtained from 63 patients with schizophrenia, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian mixture model (GMM) and partition around medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data, and thus, provide different behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using balanced random forest. The personalization was performed by identifying optimal features for a given patient based on a personalization subset consisting of other patients of similar age. Results The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active days but with low communication, etc). Although GMM-based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread, likely indicating heterogeneous behavioral characterizations. On the other hand, PAM model-based clusters had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were observed in the obtained behavioral representation features from the clustering models. The clustering model-based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.23 for the relapse prediction task in a leave-one-patient-out evaluation setting. The obtained F2 score was significantly higher than that of a random classification baseline with an average F2 score of 0.042. Conclusions Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine and atypical behavioral trends. In this study, we used GMM-based and PAM-based cluster models to obtain behavioral trends in patients with schizophrenia. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful in enabling timely interventions.

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Section: Methodsmentioning

confidence: 99%

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis

Zhou¹,

Lamichhane²,

Ben‐Zeev³

et al. 2022

JMIR Mhealth Uhealth

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“…The GMM is a probabilistic model that assumes data is generated from a finite set of Gaussian distributions. A Gaussian mixture probability density is the weighted sum of k component Gaussian densities [26]. In this study, we used the GMM implementation from the scikit-learn package in Python to obtain a clustering model for the mobile sensing data [27].…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis (Preprint)

Zhou¹,

Lamichhane²,

Ben‐Zeev³

et al. 2021

Preprint

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BACKGROUND Behavioral representations obtained from mobile sensing data could be helpful for the prediction of an oncoming psychotic relapse in schizophrenia patients and delivery of timely interventions to mitigate such relapse. OBJECTIVE In this work, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters could represent different routine behavioral trends related to daily living of patients as well as atypical behavioral trends associated with impending relapse. METHODS We used the mobile sensing data obtained in the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (e.g. ambient light, sound/conversation, acceleration etc.) obtained from a total of 63 schizophrenia patients, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data and thus provide differing behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using Balanced Random Forest. The personalization was done by identifying optimal features for a given patient based on a personalization subset consisting of other patients who are of similar age. RESULTS The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active but with low communications days, etc.). While GMM based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread likely indicating heterogeneous behavioral characterizations. PAM model based clusters on the other hand had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were seen in the obtained behavioral representation features from the clustering models. The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation setting. This obtained F2 score is significantly higher than a random classification baseline with an average F2 score of 0.042. CONCLUSIONS Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine as well as atypical behavioral trends. In this work, we used GMM and PAM-based cluster models to obtain behavioral trends in schizophrenia patients. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful to enable timely interventions.

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“…However, as in k-means, one needs to specify the number of clusters/components a priori in GMM. The optimal value can be determined by minimizing the Bayesian Information Criterion (BIC) [48] metric which considers both covariance type and the number of components. The BIC is a penalty term for the possible likelihood increment when adding more parameters into the model.…”

Section: Stage Ii: Clustering Featurized Structuresmentioning

confidence: 99%

Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering

et al. 2020

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Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. In this paper, we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. Evaluations are related on both benchmark and CASP target proteins. Structure ensembles subjected to the proposed approach and the source code of the proposed approach are publicly-available at the links provided in Section 1.

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Mixture Models: Inference and Applications to Clustering.

Abstract: 17. Mixture Models: Inference and Applications to Clustering (Statistics: Textbooks and Monographs Series, Vol. 84). By G. J. McLachlan and K. E. Basford. Dekker, New York, 1988. xii + 254 pp. $83.50.

Cited by 18 publications

References 0 publications

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis (Preprint)

Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering

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