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Nice summary of technology related benefits and challenges! The advancements of machine learning (i.e., wearable devices, artificial intelligence, telemedicine, etc.) are mostly less than a decade old and expanding as a result of provider shortages, Covid, high healthcarecosts, and other factors. While these technologies are here to stay, there are some reluctant to use them for a variety of reasons. In follow-up, how can we incentivize patients to use these technologies without completely eliminating the human interaction component (bedside manner) expected in healthcare? Follow-up thoughts or ideas welcome and count as an interaction post, Dr. Eric
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Application of Machine Learning
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Application of Machine Learning
Introduction
By improving remote patient monitoring systems, machine learning, a branch of
artificial intelligence, has enormous potential to transform the healthcare industry (Ashu &
Sharma, 2021). Using machine learning techniques for remote patient monitoring can
significantly enhance patient outcomes and resource usage in Saudi Arabia. Remote patient
monitoring enables medical professionals to monitor patients’ health conditions and take
appropriate action.
Machine Learning’s Function in Remote Patient Monitoring
Large volumes of patient data gathered via wearable technology, sensors, and other
remote monitoring equipment can be analyzed using machine learning algorithms (Usharani
et al., 2022). These algorithms process the data and find patterns and trends that allow for the
early identification of any health problems. This device could be used in Saudi Arabia to
remotely monitor patients’ vital signs and medication adherence. It could also be used to
monitor chronic illnesses like diabetes or hypertension. Medical personnel can be informed of
worsening situations via predictive algorithms (Ashu & Sharma, 2021). This allows for
prompt interventions and lowers the number of hospital admissions.
Obstacles in the Field of Biomedicine Research
When using machine learning in remote patient monitoring, biomedical researchers
encounter several difficulties despite the potentially fruitful uses. Ensuring the algorithms’
correctness and dependability is a significant challenge (Usharani et al., 2022). Biomedical
data is frequently varied, complex, and biased in many ways. In order to guarantee the
stability and efficacy of the algorithms, researchers must meticulously preprocess the data to
eliminate noise and irregularities.
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Concerns about data security and privacy are also crucial, particularly when handling
sensitive patient data (Ashu & Sharma, 2021). Strict ethical guidelines and legal requirements
must be followed by researchers in order to protect patient confidentiality and privacy. The
requirement for interdisciplinary cooperation between engineers, data scientists, and medical
specialists presents another difficulty. To create meaningful machine learning models that are
suited to healthcare requirements, effective communication and a thorough grasp of both
medical and technical factors are required.
Conclusion
In conclusion, Saudi Arabia’s healthcare system has a revolutionary possibility thanks
to machine learning in remote patient monitoring. Effective and safe implementation can be
achieved through interdisciplinary cooperation and adherence to ethical principles despite
obstacles, including data complexity and privacy issues. Overcoming these challenges will
enable Saudi Arabia’s healthcare system to use machine learning to deliver proactive,
individualized, and cost-effective care, thereby enhancing the general health of the country’s
population.
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References
Ashu, A., & Sharma, S. (2021). A novel approach of telemedicine for managing fetal
condition based on machine learning technology from IoT-based wearable medical
device. In Machine Learning and the Internet of Medical Things in Healthcare (pp.
113-134). Academic Press. https://doi.org/10.1016/B978-0-12-821229-5.00006-9
Usharani, S., Bala, P. M., Rajmohan, R., Kumar, T. A., & Selvi, S. A. (2022). Pregnancy
Women—Smart Care Intelligent Systems: Patient Condition Screening, Visualization
and Monitoring with Multimedia Technology. Intelligent Interactive Multimedia
Systems for e-Healthcare Applications, 147-169. https://doi.org/10.1007/978-981-166542-4_9
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