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Importance of Machine Learning in Healthcare Monitoring

The healthcare industry has been a forerunner in adopting digital transformation across the board. Machine learning (ML), a type of artificial intelligence, is currently playing a critical role in addressing health-related issues. 

This would include the ability to extract, exchange, and use health data and records, as well as the development of novel medical treatments and even the treatment of chronic conditions. From improving operations at a reduced cost to improving care quality, machine learning is transforming every aspect of healthcare with minimal human interaction.


What is Machine Learning?

Machine Learning (ML) is a phrase that is perhaps more commonly used than understood. ML is most simply characterized as the application of various statistical approaches to make predictions and judgments based on similarities between what is being examined and what has previously been seen.

Artificial intelligence (AI) is ideally represented by an application that can learn, dynamically updating and altering its rules based on performance evaluation. However, the ML currently used in monitoring systems lacks this learning potential. The application's aims (identification of clinical anomalies) remain constant in an ML-based monitoring system, but the precise nature of the approach to achieving those goals varies.

ML applications are built using a variety of methodologies. While ML lacks many of the nuances of human intelligence, it does have advantages: the potential for incredibly powerful memory and computational power, as well as the ability to continually study vast volumes of data while discovering subtle and dynamic patterns that humans would likely miss.


Impact of Machine Learning on Healthcare

With the healthcare sector shifting toward value-based treatment, developing a system centered on cost reduction appears contradictory. This two-pronged goal, however, is attainable when healthcare companies have end-to-end visibility into clinical quality metrics and the costs associated with them. In healthcare, machine learning can be used to expedite normal workflows, data management, medication development, diagnosis, treatment, and regulatory tasks. ML tools are positioned to provide even more value to this process such as:

  • Recommendations: Machine learning algorithms can extract and convey critical medical information without you having to actively look for it.
  • Classification: Assists in identifying and labeling the type of medical issue or ailment that a patient is dealing with.
  • Prediction: Using historical and present data, as well as common tendencies, sophisticated algorithms can forecast how future developments and events will occur.
  • Clustering: Can be used to group together comparable medical cases for the purposes of evaluating patterns and conducting research.
  • Ranking: Assists in extracting the most relevant information first, making the search quick and simple.
  • Detecting Anomalies: Allows for quick identification of specimens that deviate from common patterns, allowing for timely intervention. Automation: The ability to automate conventional, repetitive healthcare processes such as appointment scheduling, inventory management, and data entry.

The ability to cure complex diseases and the quality of healthcare services are constantly improving. As a result, in recent years, machine learning (ML) has been successfully integrated into pediatric care to anticipate the best and most personalized treatments for children. Since the outbreak of the COVID-19 epidemic, ML has been thrown into the spotlight.


Why Apply Machine Learning to Monitoring?

There are a number of monitor-related ML applications currently in the market that provides the following clinical usefulness:

  • Sepsis Identification and Clinical Deterioration: Recently, machine learning algorithms incorporating high-resolution vital signs and EHR data have demonstrated in multiple retrospective cohorts the ability to accurately predict the onset of sepsis 4-12 hours before clinical identification.
  • Reducing false alarms: ML has the potential ability to make the interpretation of designated ‘abnormal’ values smarter (i.e. improve what we now called alarms) as well as give interpretive meaning to patterns within developing/dynamic and combinatorial/complex physiological values.
  • Sedation Management in the ICU: While the risks of prolonged sedation in the ICU have been widely documented, the use of sedative medicines remains a cornerstone of critically ill patient management. Continuous data streams from ICU patients may enable for real-time study of patient sedation levels and may aid in the optimization of sedative medicine administration.

There are many aspects that machine learning provides in value in terms of monitoring for healthcare; improving clinical decision support, workflow efficiencies, and detection of specific cardiac variables, skin temperature, SPO2, ECG, Posture, Pulse rate, arrhythmias, fall detection and respiration rate and many more. It's important to make sure that your patient monitoring systems are capable of the following features to maximize machine learning's potential in healthcare. 


Sources: primary application of ML,health%2C and detect other issues.


Filed Under: Machine Learning