A roadmap for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders

ABSTRACT

Objectives

Applying machine-learning methodology to clinical data could present a promising avenue for predicting outcomes in patients receiving treatment for psychiatric disorders. However, preserving privacy when working with patient data remains a critical concern.

Methods

In showcasing how machine-learning can be used to build a clinically relevant prediction model on clinical data, we apply two commonly used machine-learning algorithms (Random Forest and least absolute shrinkage and selection operator) to routine outcome monitoring data collected from 593 patients with eating disorders to predict absence of reliable improvement 12 months after entering outpatient treatment.

Results

An RF model trained on data collected at baseline and after three months made 31.3% fewer errors in predicting lack of reliable improvement at 12 months, in comparison with chance. Adding data from a six-month follow-up resulted in only marginal improvements to accuracy.

Conclusion

We were able to build and validate a model that could aid clinicians and researchers in more accurately predicting treatment response in patients with EDs. We also demonstrated how this could be done without compromising privacy. ML presents a promising approach to developing accurate prediction models for psychiatric disorders such as ED.

KEYWORDS:

• Eating disorder
• machine learning
• prediction
• synthetic data
• treatment success

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Data availability statement

The informed consent was based on another study (de Vos et al., 2021), approved by the Behavioural, Management and Social Sciences Ethics Committee of the University of Twente, in which patients could opt for the possibility to include their ROM data (anonymized) for other scientific studies. Patients who signed for this were included in this study. Due to privacy regulation, the data cannot be made publicly available.

Author contributions statement

V Svendsen, J Lokkerbol and B Wijnen were involved in all stages of developing the code, prognostic model, interpretation and writing of the article.

J A DeVos and R Veenstra were involved in generating synthetic data and running analyses on the patient data. Moreover, they were responsible for clinical interpretation of the results and writing of the manuscript.

Additional information

Funding

One author is supported by The Netherlands Organisation for Health Research and Development (ZonMw) Mental Healthcare Fellowship.