Analysis of depression trajectory patterns using collaborative learning

A scoping review of the clinical application of machine learning in data-driven population segmentation analysis

OBJECTIVE

Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations.

MATERIALS AND METHODS

MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included.

RESULTS

We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population.

DISCUSSION

Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling.

CONCLUSION

Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods' comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.

A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data

BACKGROUND

Depression (Major Depressive Disorder) is one of the most common mental illnesses. According to the World Health Organization, more than 300 million people in the world are affected. A first depressive episode can be solved by a spontaneous remission within 6 to 12 months. It has been shown that depression affects speech production and facial expressions. Although numerous studies are proposed in the literature for depression recognition using audiovisual cues, depression relapse using audiovisual cues has not been studied in the literature.

METHOD

In this paper, we propose a deep learning-based approach for depression recognition and depression relapse prediction using audiovisual data. For more versatility and reusability, the proposed approach is based on a Model of Normality inspired framework where we define depression relapse by the closeness of the audiovisual patterns of a subject after a symptom-free period to the audiovisual patterns of depressed subjects. A model of Normality is an anomaly detection distance-based approach that computes a distance of normality between the deep audiovisual encoding of a test sample and a learned representation from audiovisual encodings of anomaly-free data.

RESULTS

The proposed approach shows a very promising results with an accuracy of 87.4% and a F1-score of 82.3% for relapse/depression prediction using a Leave-One-Subject-Out training strategy on the DAIC-Woz dataset.

CONCLUSION

The proposed model of normality-based framework is accurate in detecting depression and in predicting depression relapse. A prospective monitoring system is proposed for assisting depressed patients. The proposed framework is easily extensible and others modalities will be integrated in future works.

A Study on the Relationship between Depression Change Types and Suicide Ideation before and after COVID-19

Background: The purpose of this study is to explore and categorize changes in depression, and investigate the relationship between suicidal ideations before and after the COVID-19 pandemic. Methods: In this study, data from the Korea Welfare Panel Study (KoWePS) was used and included 8692 adults, 19 years of age or older, who could estimate the change in depression from 2017 (12th) to 2021 (16th) for final analysis. Depression change was classified into two types, ‘low-level ascending’ type (n = 7809, 80.9%), and ‘increasing after reduction’ type (n = 883, 10.2%). The Firth Method was used to examine the relationship between depression change types and suicidal ideation. Results: The lower the equivalized annual income and the lower the educational level, and the likelihood of belonging to the ‘increasing after reduction’ type, compared to the ‘low-level ascending’ type, the greater the probability of having suicidal ideation. Conclusion: The significant impact of socioeconomic status (income and educational background) on suicidal ideation indicates the need to consider how epidemics affect inequality in society. This study is expected to provide a deeper understanding of depression, as well as to establish a foundation for long-term prevention of the rapid increase in suicide rates after COVID-19.

DIP: Natural history model for major depression with incidence and prevalence

BACKGROUND

Major depression is a treatable disease, and untreated depression can lead to serious health complications. Therefore, prevention, early identification, and treatment efforts are essential. Natural history models can be utilized to make informed decisions about interventions and treatments of major depression.

METHODS

We propose a natural history model of major depression. We use steady-state analysis to study the discrete-time Markov chain model. For this purpose, we solved the system of linear equations and tested the parameter and transition probabilities empirically.

RESULTS

We showed that bias in parameters might collectively cause a significant mismatch in a model. If incidence is correct, then lifetime prevalence is 33.2% for females and 20.5% for males, which is higher than reported values. If prevalence is correct, then incidence is .0008 for females and .00065 for males, which is lower than reported values. The model can achieve feasibility if incidence is at low levels and recall bias of the lifetime prevalence is quantified to be 31.9% for females and 16.3% for males.

LIMITATIONS

This model is limited to major depression, and patients who have other types of depression are assumed healthy. We assume that transition probabilities (except incidence rates) are correct.

CONCLUSION

We constructed a preliminary model for the natural history of major depression. We determined the lifetime prevalences are underestimated and the average incidence rates may be underestimated for males. We conclude that recall bias needs to be accounted for in modeling or burden estimates, where the recall bias should increase with age.

Machine learning discovery of longitudinal patterns of depression and suicidal ideation

Background and aim

Depression is often accompanied by thoughts of self-harm, which are a strong predictor of subsequent suicide attempt and suicide death. Few empirical data are available regarding the temporal correlation between depression symptoms and suicidal ideation. We investigated the anecdotal concern that suicidal ideation may increase during a period of depression improvement.

Data

Longitudinal Patient Health Questionnaire (PHQ)-9 is a questionnaire of 9 multiple-choice questions to assess the frequency of depressive symptoms within the previous two weeks. We analyzed a chronic depression treatment population’s electronic health record (EHR) data, containing 610 patients’ longitudinal PHQ-9 scores (62% age 45 and older; 68% female) within 40 weeks.

Methods

The irregular and sparse EHR data were transformed into continuous trajectories using Gaussian process regression. We first estimated the correlations between the symptoms (total score of the first 8 questions; PHQ-8) and suicide ideation (9th question score; Item 9) using the cross-correlation function. We then used an artificial neural network (ANN) to discover subtypes of depression patterns from the fitted depression trajectories. In addition, we conducted a separate analysis using the unfitted raw PHQ scores to examine PHQ-8’s and Item 9’s pattern changes.

Results

Results showed that the majority of patients’ PHQ-8 and Item 9 scores displayed strong temporal correlations. We found five patterns in the PHQ-8 and the Item 9 trajectories. We also found 8% - 13% of the patients have experienced an increase in suicidal ideation during the improvement of their PHQ-8. Using a trajectory-based method for subtype pattern detection in depression progression, we provided a better understanding of temporal correlations between depression symptoms over time.

How data science can advance mental health research

Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.

Data-based Decision Rules to Personalize Depression Follow-up

Depression is a common mental illness with complex and heterogeneous progression dynamics. Risk grouping of depression treatment population based on their longitudinal patterns has the potential to enable cost-effective monitoring policy design. This paper establishes a rule-based method to identify a set of risk predictive patterns from person-level longitudinal disease measurements by integrating the data transformation, rule discovery and rule evaluation. We further extend the identified rules to create rule-based monitoring strategies to adaptively monitor individuals with different disease severities. We applied the rule-based method on an electronic health record (EHR) dataset of depression treatment population containing person-level longitudinal Patient Health Questionnaire (PHQ)-9 scores for assessing depression severity. 12 risk predictive rules are identified, and the rule-based prognostic model based on identified rules enables more accurate prediction of disease severity than other prognostic models including RuleFit, logistic regression and Support Vector Machine. Two rule-based monitoring strategies outperform the latest PHQ-9 based monitoring strategy by providing higher sensitivity and specificity. The rule-based method can lead to a better understanding of disease dynamics, achieving more accurate prognostics of disease progressions, personalizing follow-up intervals, and designing cost-effective monitoring of patients in clinical practice.