Machine Learning in Tremor Analysis: Critique and Directions

Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.     

Fixed‐dose combination therapy to reduce the growing burden of cardiovascular disease in low‐ and middle‐income countries: feasibility and challenges

Cardiovascular disease (CVD) has become a major concern in low‐ and middle‐income countries, which bear about 80% of the cardiovascular mortality worldwide. Curbing the burden of CVD implies the management and control of many cardiovascular risk factors that act synergistically to increase cardiovascular mortality. Such actions may require expensive polymedications in a context of limited resources. Therefore, alternative solutions for CVD prevention in low‐ and middle‐income countries are urgently needed. In this context, the concept of a fixed‐dose combination therapy, a polypill composed of drugs known to effectively treat or prevent CVD, has been proposed as a scalable strategy to overcome nonadherence to polymedications and reduce costs. While this has recently been approved in more than 30 countries across America and Europe, there is a crucial need to analyze the potential benefits and challenges related to cardiovascular polypills implementation and vulgarization in low‐ and middle‐income countries, the epicenter of CVD.