Construction of longitudinal prediction targets using semisupervised learning

Reorienting Latent Variable Modeling for Supervised Learning

Despite its potentials benefits, using prediction targets generated based on latent variable (LV) modeling is not a common practice in supervised learning, a dominating framework for developing prediction models. In supervised learning, it is typically assumed that the outcome to be predicted is clear and readily available, and therefore validating outcomes before predicting them is a foreign concept and an unnecessary step. The usual goal of LV modeling is inference, and therefore using it in supervised learning and in the prediction context requires a major conceptual shift. This study lays out methodological adjustments and conceptual shifts necessary for integrating LV modeling into supervised learning. It is shown that such integration is possible by combining the traditions of LV modeling, psychometrics, and supervised learning. In this interdisciplinary learning framework, generating practical outcomes using LV modeling and systematically validating them based on clinical validators are the two main strategies. In the example using the data from the Longitudinal Assessment of Manic Symptoms (LAMS) Study, a large pool of candidate outcomes is generated by flexible LV modeling. It is demonstrated that this exploratory situation can be used as an opportunity to tailor desirable prediction targets taking advantage of contemporary science and clinical insights.

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

Background

Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.

Objective

This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.

Methods

The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.

Results

We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.

Conclusions

This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review (Preprint).. [DOI: 10.2196/preprints.29749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.

OBJECTIVE

This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.

METHODS

The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.

RESULTS

We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.

CONCLUSIONS

This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.

Can Machine Learning Improve Screening for Targeted Delinquency Prevention Programs?

The cost-effectiveness of targeted delinquency prevention programs for children depends on the accuracy of the screening process. Screening accuracy is often poor, resulting in wasted resources and missed opportunities to avert negative outcomes. This study examined whether screening approaches based on logistic regression or machine learning algorithms could improve accuracy relative to traditional sum-score approaches when identifying boys in the 5th grade (N = 1012) who would be repeatedly arrested for violent and serious crimes from ages 13 to 30. Screening algorithms were developed that incorporated facets of teacher-reported externalizing problems and other known risk factors (e.g., peer rejection). The predictive performance of these algorithms was evaluated and compared in holdout (i.e., test) data using the area under the receiver operating curve (AUROC) and Brier score. Both the logistic and machine learning methods yielded AUROC superior to traditional sum-score screening approaches when a broad set of risk factors for future delinquency was considered. However, this improvement was modest and was not present when using item-level information from a composite scale assessing externalizing problems. Contrary to expectations, machine learning algorithms performed no better than simple logistic models. There was a large apparent advantage of machine learning that disappeared after appropriate cross-validation, underscoring the importance of careful evaluation of these methods. Results suggest that screening using logistic regression could improve the cost-effectiveness of targeted delinquency prevention programs in some cases, but screening using machine learning would confer no marginal benefit under currently realistic conditions.

A Risk Calculator to Predict the Individual Risk of Conversion From Subthreshold Bipolar Symptoms to Bipolar Disorder I or II in Youth

OBJECTIVE

Youth with subthreshold mania are at increased risk of conversion to bipolar disorder (BP) I/II. Predictors for conversion have been published for the group as a whole. However, risk factors are heterogeneous, indicating the need for personalized risk assessment.

METHOD

One hundred forty youth with BP not otherwise specified (BP-NOS; 6-17 years old) followed through the Course and Outcome of Bipolar Youth (COBY) study with at least 1 follow-up assessment before conversion to BP-I/II were included. Youths were assessed on average every 7 months (median 11.5 years) using standard instruments. Risk predictors reported in the literature were used to build a 5-year risk calculator. Discrimination was measured using the time-dependent area under the curve after 1,000 bootstrap resamples. Calibration was evaluated by comparing observed with predicted probability of conversion. External validation was performed using an independent sample of 58 youths with BP-NOS recruited from the Pittsburgh Bipolar Offspring Study.

RESULTS

Seventy-five (53.6%) COBY youths with BP-NOS converted to BP-I/II, of which 57 (76.0%) converted within 5 years. Earlier-onset BP-NOS, familial hypomania/mania, and high mania, anxiety, and mood lability symptoms were important predictors of conversion. The calculator showed excellent consistency between the predicted and observed risks of conversion, good discrimination between converters and non-converters (area under the curve 0.71, CI 0.67-0.74), and a proportionally increasing rate of converters at each successive risk class. Discrimination in the external validation sample was good (area under the curve 0.75).

CONCLUSION

If replicated, the risk calculator would provide a useful tool to predict personalized risk of conversion from subsyndromal mania to BP-I/II and inform individualized interventions and research.