The healthcare sector has been the front runner in adopting digital transformation across the board. Right now, machine learning (ML), a subset of artificial intelligence, is playing a key role to address health-related areas. This would include having the ability to extract, share & leverage health data and records, development of new medical procedures & even the treatment of chronic diseases. From enhancing operations at a lower cost to improving care quality, ML is revolutionizing every aspect of healthcare with limited human intervention!
And, with the amount of data generated for each patient, ML algorithms in healthcare certainly have great potential. Therefore, it is not surprising why we’re witnessing multiple successful ML applications in healthcare right now. In this article, we will explore the role of machine learning in healthcare with its real-world applications and advantages. Let’s begin!
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With the healthcare industry moving towards value-based care, creating a system that is focused on lowering costs seems counterintuitive. However, this two-pronged goal is achievable when healthcare organizations have end-to-end visibility into clinical quality measures and the expenses associated with it. This is where machine learning can be leveraged to accelerate routine workflows, data management, drug development, diagnosis, treatment & regulatory activities in healthcare. Here’s how!
Below are some of the prominent machine learning use cases in healthcare that you can consider while assessing the potential of this technology.
Leverage our technical prowess & industry experience to build an ML-based solution that helps you serve better.
Using ML in healthcare operations can prove to be extremely beneficial to organizations. While a healthcare professional and a machine learning algorithm are most likely to achieve the same conclusion based on the same data sets, the use of ML ensures getting results much faster & allowing to start the treatment earlier.
Listed below are key benefits of leveraging ML for the healthcare sector;
The following ML algorithms are transforming the healthcare landscape by efficiently addressing various medical challenges:
Imagine a Machine Learning algorithm that behaves a bit like the human brain. That’s ANN for you. These networks sift through data with minimal human intervention, making them perfect for healthcare applications like medical imaging in radiology and recognizing text and speech.
When it comes to predicting binary outcomes, logistic regression is the most popular and preferred ML algorithm. It’s not only easy to implement but also reveals the significance of each variable in the final prediction. Healthcare professionals can rely on it for tasks such as medical diagnosis, identifying at-risk patients, and tailoring behavior plans.
SVMs are ideally used for addressing classification challenges that require you to classify data with missing values from incomplete datasets.
To put it simply, the further the data points appear from the Y-axis on the graph below, the higher the possibility that they belong to the respective classes. In healthcare, SVMs find their place in drug development, predicting medication adherence, and segmenting images and text.
You can leverage our custom healthcare software development experience to build patient-centric digital solutions and improve operational efficiency & treatment outcomes. We leverage the latest in technology to help you develop and implement a wide range of solutions including but not limited to:
A UK-based medical service provider wanted to improve their clinical efficiency and patient care. They turned to Rishabh for creating a system that offers real-time human activity recognition within their existing clinical systems. This would enable the client to provide better diagnosis & treatment for critical care patients.
We created ML-based data models for all their connected devices for real-time recording and analysis of patient health. This AI-powered patient monitoring system enabled continuous monitoring of patients and centralized health management.
Machine learning is all set to be an indispensable technology to the sustainable success of the healthcare sector. Hence, healthcare professionals and clinicians must start utilizing this technology to work in their favor. It is high time they start leveraging the algorithms to optimize their data-driven tasks, risk scoring, clinical decision support & early alert mechanism as some of the significant areas of development. And, to do that there’s a clear need for these organizations to leverage the next-gen data analytics to stay competitive.