The healthcare industry has long been an early adopter of the most innovative technologies. Today artificial intelligence and its subset machine learning are on their way to becoming a key element in healthcare systems, from developing new medical procedures to handling patient data and records.
The increase in the number of machine learning applications allows us to look into the future where healthcare providers will use data and analytics to provide better services, improve processes, and automate tasks. Soon, machine learning-based applications working with real-time patient data will become commonplace, increasing the efficiency of treatment options and driving the cost of healthcare down.
But how exactly can machine learning be used in healthcare? Read this article to find out the most important and innovative applications of machine learning solutions in healthcare systems.
11 applications of machine learning in healthcare
- Handling administrative tasks with Natural Language Processing
- Tools for risk identification
- Accelerating medical research
- Diagnosis and disease identification
- Learning and manufacturing drugs
- Medical imaging diagnostics
- Health records
- Research and clinical trials
- Data crowdsourcing
- Outbreak prediction
1. Handling administrative tasks with Natural Language Processing
One of the greatest burdens physicians experience today is the organization and implementation of administrative tasks. By automating them, healthcare institutions could help solve the problem and allow physicians to do what they do best: spend more time with patients.
Here's an example of how machine learning and Natural Language Processing can help:
A large portion of administrative tasks involves reviewing and updating electronic health records. Nearly every hospital in the United States uses such a unified system, and most clinics do as well. Institutions could implement NLP-powered tools that would use algorithms to identify and categorize words and phrases to allow physicians to dictate notes directly to the system during patient visits. Afterward, doctors could review the charts and summaries compiled by the tool instead of having to read through all the notes and test results to understand the overall health of a patient.
The tool would help physicians spend less time maintaining the patient records, as a result allowing them to deliver better healthcare services to patients.
2. Tools for risk identification
Healthcare providers are now taking advantage of digital solutions built on top of machine learning models that use anomaly detection algorithms to predict events such as strokes, heart attacks, sepsis, and other serious complications. These tools pull data from historical patient records, daily evaluations, and real-time measurement of vital signs such as the heart rate or blood pressure. The tools can alert staff to imminent patient risks and allow them to take preventive actions.
For example, in El Camino Hospital, researchers combined electronic health records, nurse call data, and bed alarm data to develop a tool for predicting patient falls. This tool alerts staff when the patient is at high risk of falling so that they can immediately take action. The implementation of the tool helped to reduce falls by 39%.
3. Accelerating medical research
To keep up with the trends in particular areas of medical research, physicians and scientists need to read and process an overwhelming quantity of information. Scientists publish thousands of research papers every year, and keeping up with the most recent research is often challenging. By using a natural language processing (NLP) tool to parse literature, medical researchers could get valuable insights without having to read all of the articles on their own.
For example, researchers are from Ireland, and the United States collaborated on a study on adverse drug events using text mining, neural networks, and predictive analytics to analyze vast databases of medical literature as well as social media posts to understand how people are commenting on drug side effects. This technique allowed them to analyze over 300,000 articles from medical journals and 1.6 million comments on social media. To show the relationship between drugs and side effects, the team used some handy data visualization tools.
As you can imagine, carrying out a task of this magnitude manually would take the team years, if not decades.
4. Diagnosis and disease identification
One of the most important applications of machine learning algorithms in healthcare is related to the identification and diagnosis of diseases that are considered hard to diagnose. This can include cancers that are difficult to identify during their initial stages of genetic diseases.
An example of such an application is IBM Watson Genomics, which integrates cognitive computing with genome-based tumor sequencing to help physicians in making a fast diagnosis.
There are also tools that take advantage of artificial intelligence to develop therapeutic treatments in areas like oncology. The idea here is developing a commercially viable way to diagnose and provide treatment in clinical conditions by automating the process as much as possible.
5. Learning and manufacturing drugs
The early-stage drug discovery process is another area that can benefit a lot from machine learning. It's already dominated by R&D technologies such as precision medicine and next-generation sequencing, which are used to find alternative paths for therapy of multifactorial diseases.
Since machine learning techniques are based on unsupervised learning (identifying patterns in data without providing any predictions), they can be very useful not only for discovering new drugs but also personalizing drug combinations for specific cases.
6. Medical imaging diagnostics
Machine learning and deep learning brought us breakthrough technology called computer vision. Many tech companies around the world are now busy developing tools that provide diagnostics for image analysis for physicians. As machine learning algorithms become more widespread and grow in their capacity, we are going to see an increasing number of data sources from various medical imagery.
For example, one of the most significant applications we have seen so far is the analysis of skin images that aim to identify skin cancer. In several studies, such tools were proven to be more accurate than physicians – they reached 87-95% accuracy while, while dermatologists have 65% to 85% accuracy rate in detecting melanomas.
Treatments are most effective when they're combined with individual health factors. That's why machine learning and its predictive analytics component can play such a huge role in personalized treatments. Currently, physicians can choose from a limited set of diagnoses or estimate the risk to their patients on the basis of their symptomatic history and the available genetic information. In the future, machine learning tools will be able to leverage patient medical history to generate multiple treatment options.
We're also going to see more devices and biosensors with advanced health measurement capabilities appear on the market. They will allow for data to become readily available for ML-based technologies.
8. Health records
Maintaining and updating health records is a time-consuming and expensive process. It's true that technology has played an essential role in facilitating the data entry process. However, the majority of processes still take a lot of time to complete because they need to be done manually. This is where machine learning comes in. It promises to save time, money, and effort. For example, document classification methods that use vector machines and machine learning-based OCR recognition are now appearing on the market.
An example of that is Google Cloud Vision API or Matlab machine learning-based handwriting recognition technology. Various institutions are now developing the next generation of smart health records that will incorporate ML tools from the ground up to help in the clinical treatment suggestions and diagnosis.
9. Research and clinical trials
Another area that stands to benefit a lot from machine learning is the field of clinical trials and research. Clinical trials are very expensive and take even years to complete. By applying ML-based predictive analytics to identify potential clinical trial candidates, researchers can narrow down their pool from a wide range of data points such as social media, previous doctor visits, and others.
Another way to use machine learning in this context is for real-time monitoring of the trial participants. Such tools can also help researchers to find the best sample size to be tested and take advantage of electronic records to reduce database errors.
10. Data crowdsourcing
The medical field has recently discovered crowdsourcing, and today researchers and practitioners use the technique to access massive amounts of data people upload based on their consent. Such live health data comes with great implications on how medicine will function in the future.
Take this as an example: Apple's Research Kit allows its users to access interactive applications that apply ML-based facial recognition to treat Asperger's and Parkinson's disease. Another interesting example is a recent collaboration of IBM with Metatron to create a tool that would decipher, accumulate, and share diabetes and insulin data in real-time based on crowdsourced information.
Given the advances that are happening in the Internet of Things field, the healthcare industry might still be on its way to discovering new ways of using the data and improving the overall performance of diagnostics.
11. Outbreak prediction
Machine learning-based tools are now also being used to monitor and predict outbreaks around the world. Did you know that BlueDot, a specialized tool for monitoring potential outbreaks, predicted the spread of the coronavirus before it has been officially announced?
Scientists can assess a massive amount of data today collected from real-time social media feeds, satellites, website information, and institutional records. Networks can help to make sense of this information and predict anything from malaria outbreaks to severe infectious diseases. Predicting such outbreaks is especially valuable for Third World countries that lack medical infrastructure and educational systems.
Machine learning in healthcare – conclusion
Artificial intelligence and machine learning will impact both physicians and hospitals in the near future. They're going to play a critical role in clinical decision support, disease identification, and tailoring treatment plans to ensure the best outcomes possible. We are also going to use machine learning-based tools to offer different treatment options, personalized treatments, and improving the overall efficiency of hospitals and healthcare systems while reducing the cost of care.
Do you have any questions about how healthcare companies can use technology to their advantage? Get in touch with our consultants. We have a lot of experience in assisting healthcare institutions in digital transformation.