Healthcare Applications of Machine Learning

Care providers must handle myriad divergent data and try to make sense of similarities, differences, and patterns in disease states or symptomatology across patients. This becomes nearly impossible when dealing with huge amounts of complex data. Machine learning in the healthcare industry seems to be up to the job. It has the capacity to sort through massive datasets and determine personalized treatment plans that could mean the difference between life and death.

According to a 2020 study by International Data Corporation, 50% of hospitals are already using artificial intelligence (AI) while the other half are looking to implement the technology in the next 24 months. Although adoption of AI in healthcare continues to rise, we still have a long way to go before seeing the tech’s full potential. 

The world of machine learning used to lack actionable frameworks and was mostly viewed as a mythical panacea. Now, more outcome-driven guidelines and practical examples give way to solutions that are expected to bring meaningful impact to patient experiences, decision-making by hospital leadership, and operational outcomes.

Types of machine learning

Machine learning starts with developing foundational models for solving problems. An algorithm then alters the foundation for the model as it sifts through data and uncovers patterns. 

There are four types of machine learning:

Supervised learning. In supervised learning, algorithms work with a training dataset that is fully labeled. 

Unsupervised learning. In unsupervised learning, algorithms use unlabeled data, leaving them to discover patterns in the dataset without any idea of the results.

Semi-supervised learning. Semi-supervised learning marries supervised and unsupervised learning, using a combination of classified and unclassified data.

Reinforcement learning. Reinforcement learning uses a reward system to teach algorithms. Desired outputs are rewarded, and the undesired outputs are punished. 

Thanks to various types of algorithms, ML solutions have become more than just wishful thinking for healthcare. Now let’s take a peek at some practical applications of machine learning in healthcare.

Machine learning applications in healthcare

Now, you may be wondering how to use machine learning in healthcare. Anywhere there’s complex data to manipulate, you can count on machine learning to do the job.

Predictive analytics

Powered by machine learning, predictive analytics uses historical data to forecast future outcomes. It recently proved its merit in a healthcare setting when the National Minority Quality Forum (NMQF) released the COVID-19 Index, a tool that predicts pandemic surges.

NMQF Webinar: COVID-19 Community Healthcare Worker Outreach and Education Program

Machine learning in healthcare analytics leverages data to improve care delivery. Penn Medicine, for one, has been relying on Palliative Connect. This program uses predictive technology to promptly develop a prognosis score so that the healthcare team can target palliative consultations toward high-risk individuals.

Disease identification and diagnosis

Capable of studying data from thousands of patients, machine learning can help identify diseases that are hard for doctors to diagnose or difficult to spot during their initial stages. 

Case in point, the Massachusetts Institute of Technology developed a revolutionary deep learning model that can anticipate the onset of breast cancer years beforehand. Another tool, IBM’s Watson for Genomics, goes beyond just diagnosing; it accelerates the speed of sifting through databases to identify the best treatment options based on a patient’s DNA.

Medical imaging control

It’s not easy for doctors to work through the plethora of pixels produced by imaging technologies. By invoking one or more algorithms to process complex visual information, machine learning greatly improves the efficiency of clinical professionals.

One noteworthy application of machine learning in medical imaging is the analysis of skin images to detect skin cancer. Without medical imaging, dermatologists are only accurate 65% to 80% of the time. With the imaging supporting their trained eye, the accuracy rate increases to 75% to 84%.

Robotic patient support tasks

No longer just confined to the operating room, robots are now being used to enhance patient care. Mabu, for one, is making strides in providing care at home without the presence of a doctor. This yellow robotic machine combines learning from medical best practices and physician-patient interactions to help people deal with the physiological symptoms and psychological barriers that make certain chronic diseases hard to manage.

Mabu, the personal health care robot | ZDNet

Personalized consultations

Patients receive the best care when medical decisions are made based on a patient’s unique characteristics instead of just relying on population averages. To provide individualized prognoses, healthcare practitioners need to cross-reference information from various sources, such as electronic health records and genetic data. Although highly preferred, this process proves to be time-consuming.

Leveraging datasets at a scale that’s beyond human capacities to digest, machine learning paves the way for precision medicine. This emerging approach analyzes complex datasets and integrates multimodal data to provide patient-tailored diagnoses.

Communication with patients

Research conducted by Texas A&M highlights the importance of effective communication with patients. It asserts that regular screening could prevent about 1.7 million new cancer cases per year in the US. Also, effective direct-to-patient outreach marketing can encourage people to participate in these preemptive examinations. Machine learning plays a part by helping practitioners examine the effectiveness of their outreach programs and improve them accordingly. 

ML also offers operational improvements to healthcare systems. For instance, it can help tackle appointment no-show rates by predicting which patients are likely to bail. This information then allows healthcare systems to reach out to the patients with appointment reminders and reassign the slot when necessary. Healthcare practitioners can also use machine learning to provide data-driven recommendations on which worklists to prioritize based on empirical data gathered from hospitals. 

Benefits of machine learning to healthcare 

Machine learning derives its value from algorithms that provide insights and improve understanding and decision-making. It has much to offer to a sector that deals with matters of life and death. 

The healthcare industry benefits from machine learning that analyzes patient-doctor interactions and identifies combinations of treatments that provide the highest success rates. Integrating this information with data on resource availability can result in optimal delivery of the regimen. 

The best part is that machine learning adapts and improves when exposed to new data and information. This continuous feedback loop guarantees increasingly reliable results and decision-making over time.

Improve care, save lives!

Medical practitioners can’t afford to sit on their laurels, not when people’s lives or well-being are at stake. The health sector must be quick to adopt technological developments that promise to improve the way things are done. And the application of ML in healthcare holds the potential to accelerate diagnoses and treatments of even rare diseases like acute hepatic porphyria.  

Are you ready to take your healthcare processes to the next level? Reach out to us at Postindustria. We’ll help you unlock the potential of machine learning to ensure optimal operations and services for patients.

Learn how AI transforms healthcare

Download the eBook