5 Innovative Use Cases of Machine Learning in Healthcare

Machine Learning (ML) is one of the most versatile technologies of Artificial Intelligence (AI). The healthIT sector has realized the importance of ML in the prediction, management, and treatment of various health issues. These days, researchers are coming up with some innovative ways in which machine learning is improving the quality of patient care.

In this article, we will be sharing 5 innovative (and latest) researches that exemplify the potential of machine learning to transform digital healthcare platforms. Let’s get started. 

• Machine Learning to Predict Dialysis or Death in COVID Patients 

According to a new study published in the Clinical Journal of the American Society of Nephrology, researchers have developed a machine learning model that uses predictive analytics to detect if a COVID-19 patient is at risk of death or dialysis treatment. 

When patients are hospitalized with COVID-19 infection, there are chances of acute kidney injury. The ML model enables hospitals to make informed decisions about where they need to allocate dialysis resources. 

The research was conducted using the EHR data of Mount Sinai hospitals. There were 5 ML models that were developed, out of which the XGBoost model without imputation proved to be effective in making effective predictions.

For the Machine Learning model, EHR data from the first 12 hours of patients’ admission was used and the ML algorithms were able to predict death or dialysis at one, three, five, and seven days after a patient was hospitalized. 

• Machine Learning to Predict Clinical Lab Test Results

In a new study published in Nature Medicine, researchers have developed that applying ML algorithms to wearable device data helps to predict clinical lab measurements, without having the need to visit the doctor. 

Devices such as Fitbit or smartwatches have the ability to measure changes in the health-vitals over a prolonged period. As a result, it is easy to determine when there is a variation from the natural baseline. Such outcomes are not possible with a one-time lab test. 

The study was conducted on 54 patients for 3 years wherein the patients wore a smartwatch to track their heart rate, sweat gland activation, skin temperature, etc. For all the patients, researchers found that the smartwatch data provides better insights into patients’ health. For example, for patients with low sweat gland activation, the doctors could easily predict dehydration. Moreover, when clinical and smartwatch measurements were compared, the data largely matched up.

• Machine Learning to Boost Patient Satisfaction Program 

The Institute of Electrical and Electronics Engineers’ Journal of Biomedical and Health Informatics conducted a study wherein they utilized EHR data to assess patient satisfaction with the care system and produce actionable insights. 

Patient healthcare is a long process. It’s a journey wherein they need to interact with multiple healthcare professionals, across different services. 

The researchers used a database from the electronic health records and conducted a survey. This data is pushed against ML algorithms which further helps in making helpful recommendations to improve patient care.

• Machine Learning to Measure Unconsciousness Under Anesthesia

Machine Learning can be helpful in gauging unconsciousness in patients under anesthesia. This would allow the anesthesiologists to manage the drug dosage. Although the anesthetic drugs act on the brain, anesthesiologists rely on heart rate, movement, and respiratory rate to judge whether a patient is unconscious to the desired degree. 

A team at MIT and Massachusetts General Hospital (MGH) showcased ML algorithms that offer anesthesiologists the ability to maintain unconsciousness with just the right amount of drug. Such measures help in maintaining postoperative outcomes such as delirium. 

• Machine Learning to Choose Relevant Test for Chest Pain 

Machine Learning can be extremely helpful to clinicians in choosing the right imaging chest if the patient has chest pain due to coronary artery disease. Researchers from Yale introduced a machine learning tool called ASSIST which had algorithms aiming to focus on the long-term outcome for a patient. 

Functional testing, also known as a stress test examines reduced blood flow to the heart to detect coronary disease. Anatomical testing, another type of chest test identifies blockage in blood vessels. The ML algorithms analyze the patient’s condition and recommend the test to each patient accordingly. 

Conclusion:

The 5 use cases of Machine Learning shared above are just a glimpse of what potential does this technology holds. ML can be utilized in more advanced and innovative ways. If you do have an idea where machine learning can be utilized to improve the patient care system, then our health-tech team can help you to take it further. Check out our healthcare development services to understand how our team can help you in different ways to level up your patient-care services.