Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

[Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode]

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.

Association of the 6-minute walking test ratio and difference with pulmonary function in patients with interstitial lung disease

OBJECTIVES

To examine the associations between 6-minute walk test (6MWT) and lung functions, blood gas analysis findings, fractional exhaled nitric oxide (FeNO), and hospital stay in interstitial lung disease (ILD).

METHODS

The present retrospective study included patients hospitalized in Beijing Hospital of Traditional Chinese Medicine, Capital Medical University between September 2018 and December 2019. The outcomes included the difference between the actual and predicted 6MWT values (6MWT difference) and the ratio of the actual to predicted 6MWT value (6MWT ratio).

RESULTS

This study included 137 patients. The predicted 6MWT value was 519±61 m and the actual 6MWT value was 449 (196.5,694)m. The 6MWT ratio was 84.7±177.6 and 6MWT difference was 73.9±95.1 m. Fractional exhaled nitric oxide (FeNO) (β= -2.157, standard error [SE]=0.836, p=0.014) and diffusing capacity of the lungs for carbon monoxide (DLCO) (β= -22.528, SE=7.48, p=0.004) had independent associations with 6MWT difference. The FeNO (β=0.403, SE=0.163, p=0.018) and DLCO (β=4.355, SE=1.458, p=0.005) had independent associations with 6MWT ratio.

CONCLUSION

In ILD, 6MWT difference and 6MWT ratio were associated with FeNO and DLCO. The 6MWT value was not associated with hospital stay. Therefore, the 6MWT might be a surrogate marker of pulmonary function in clinical ILD.