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Zhuparris, A.; De Goede, A.A.; Yocarini, I.E.; Kraaij, W.; Groeneveld, G.J.; Doll, R.J. Machine Learning Algorithms in Developing CNS Biomarkers. Encyclopedia. Available online: (accessed on 04 December 2023).
Zhuparris A, De Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Algorithms in Developing CNS Biomarkers. Encyclopedia. Available at: Accessed December 04, 2023.
Zhuparris, Ahnjili, Annika A. De Goede, Iris E. Yocarini, Wessel Kraaij, Geert Jan Groeneveld, Robert Jan Doll. "Machine Learning Algorithms in Developing CNS Biomarkers" Encyclopedia, (accessed December 04, 2023).
Zhuparris, A., De Goede, A.A., Yocarini, I.E., Kraaij, W., Groeneveld, G.J., & Doll, R.J.(2023, June 07). Machine Learning Algorithms in Developing CNS Biomarkers. In Encyclopedia.
Zhuparris, Ahnjili, et al. "Machine Learning Algorithms in Developing CNS Biomarkers." Encyclopedia. Web. 07 June, 2023.
Machine Learning Algorithms in Developing CNS Biomarkers

Drawing from an extensive review of 66 publications, a comprehensive overview of the diverse approaches to creating mHealth-based biomarkers using machine learning is presented herein. By exploring the current landscape of biomarker development using mHealth technologies and machine learning, researchers aim to provide valuable insights into this rapidly evolving field. By doing so, researchers reflect on current challenges in this field and propose recommendations for ensuring the development of accurate, reliable, and interpretable biomarkers.

machine learning biomarker wearables smartphones mHealth remote monitoring central nervous system clinical trials

1. Introduction

1.1. Motivation

Disorders that are affected by the Central Nervous System (CNS), such as Parkinson’s Disease (PD) and Alzheimer’s Disease (AD), have a significant impact on the quality of life of patients. These disorders are often progressive and chronic, making long-term monitoring essential for assessing disease progression and treatment effects. However, the current methods for monitoring disease activity are often limited by accessibility, cost, and patient compliance [1][2]. Limited accessibility to clinics or disease monitoring devices may hinder the regular and consistent monitoring of a patient’s condition, especially for patients living in remote areas or for those who have mobility limitations. Clinical trials incur costs related to personnel, infrastructure, and equipment. A qualified healthcare team, including clinical raters, physicians, and nurses, contributes to personnel costs through salaries, training, and administrative support. Trials involving specialized equipment for measuring biomarkers can significantly impact the budget due to costs associated with procurement, maintenance, calibration, and upgrades. Furthermore, infrastructure costs may increase as suitable facilities are required for data collection during patient visits and equipment storage. Patient compliance poses challenges for disease monitoring, as some methods require patients to adhere to strict protocols, collect data at specific time intervals, or perform certain tasks that can be challenging for patients to execute. Low or no compliance can lead to incomplete or unreliable monitoring results, which in turn can hinder the reliability of the assessments. Given these limitations, there is a growing interest in exploring alternative approaches to monitoring CNS disorders that can overcome these challenges. The increasing adoption of smartphones and wearables among patients and researchers offers a promising avenue for remote monitoring.
Patient-generated data from smartphones, wearables, and other remote monitoring devices can potentially complement or supplement clinical visits by providing data during evidence gaps between visits. As the promise of mobile Health (mHealth) technologies is to provide more sensitive, ecologically valid, and frequent measures of disease activity, the data collected may enable the development and validation of novel biomarkers. The development of novel ‘digital biomarkers’ using data collected from electronic Health (eHealth) and mHealth device sensors (such as accelerometers, GPS, and microphones) offers a scalable opportunity for the continuous collection of data regarding behavioral and physiological activity under free-living conditions. Previous clinical studies have demonstrated the benefits of smartphone and wearable sensors to monitor and estimate symptom severity associated with a wide range of diseases and disorders, including cardiovascular diseases [3], mood disorders [4], and neurodegenerative disorders [5][6]. These sensors can capture a range of physiological and behavioral data, including movement, heart rate, sleep, and cognitive function, providing a wealth of information that can be used to develop biomarkers for CNS disorders in particular. These longitudinal and unobtrusive measurements are highly valuable for clinical research, providing a scalable opportunity for measuring behavioral and physiological activity in real-time. However, these approaches may carry potential pitfalls as the data sourced from these devices can be large, complex, and highly variable in terms of availability, quality, and synchronicity, which can therefore complicate analysis and interpretation [7][8]. Machine Learning (ML) may provide a solution to processing heterogenous and large datasets, identifying meaningful patterns within the datasets, and predicting complex clinical outcomes from the data. However, the complexities involved in developing biomarkers using these new technologies need to be addressed. While these tools can aid the discovery of novel and important digital biomarkers, the lack of standardization, validation, and transparency of the ML pipelines used can pose challenges for clinical, scientific, and regulatory committees.

1.2. What Is Machine Learning

In clinical research, one of the primary objectives is to understand the relationship between a set of observable variables (features) and one or more outcomes. Building a statistical model that captures the relationship between these variables and the corresponding outputs facilitates the attainment of this understanding [9]. Once this model is built, it can be used to predict the value of an output based on the features.
ML is a powerful tool for clinical research as it can be used to build statistical models. A ML model consists of a set of tunable parameters and a ML algorithm that enables the generation of outputs based on given inputs and selected parameters. Although ML algorithms are fundamentally statistical learning algorithms, ML and traditional statistical learning algorithms can differ in their objectives. Traditional statistical learning aims to create a statistical model that represents causal inference from a sample, while ML aims to build generalizable predictive models that can be used to make accurate predictions on previously unseen data [10][11]. However, it is essential to recognize that while ML models can identify relationships between variables and outcomes, they may not necessarily identify a causal link between them. This is because even though these models may achieve good performances, it is crucial to ensure that their predictions are based on relevant features rather than spurious correlations. This enables the researchers to gain meaningful insights from ML models while also being aware of their inherent limitations.
While ML is not a substitute for the clinical evaluation of patients, it can provide valuable insights into a patient’s clinical profile. ML can help to identify relevant features that clinicians may not have considered, leading to better diagnosis, treatment, and patient outcomes. Additionally, ML can help to avoid common pitfalls observed in clinical decision making by removing bias, reducing human error, and improving the accuracy of predictions [12][13][14][15]. As the volume of data generated for clinical trials and outside clinical settings continues to grow, ML’s support in processing data and informing the decision-making process becomes necessary. ML can help to uncover insights from large and complex datasets that would be difficult or impossible to identify manually.
To develop an effective ML model, it is necessary to follow a rigorous and standardized procedure. This is where ML pipelines come in. Table 1 showcases an exemplary ML pipeline, which serves as a systematic framework for automating and standardizing the model generation process. The pipeline encompasses multiple stages to ensure an organized and efficient approach to model development. First, defining the study objective guides the subsequent stages and ensures the final model meets the desired goals. Second, raw data must be preprocessed to remove errors, inconsistencies, missing data, or outliers. Third, feature extraction and selection identifies quantifiable characteristics of the data relevant to the study objective and extracts them for use in the ML model. Fourth, ML algorithms are applied to learn patterns and relationships between features, with optimal configurations identified through iterative processes until desired performance metrics are achieved. Finally, the model is validated against a new dataset that is not used in training to ensure generalizability. Effective reporting and assessment of ML procedures must be established to ensure transparency, reliability, and reproducibility.
Table 1. Representation of a standard machine learning pipeline.


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