The healthcare sector currently generates up to 30% of global data, research suggests. By 2025, healthcare data will be accumulating at a CAGR of 36%, faster than data from media and finance. In order to process and make sense of such copious amounts of information, the use of AI algorithms in healthcare needs to increase dramatically.
Artificial intelligence has redefined data analytics in multiple industries. Machine-learning-enabled computer systems are capable of sifting through loads of data, discerning patterns, and drawing conclusions. They do their work at lightning speed and with amazing accuracy.
This article explores the AI algorithms used in healthcare and the possibilities they offer. But first, let’s define the main concepts.
Artificial intelligence aims to train computer systems to mimic human cognitive skills. The field lies at the intersection of computer science, big data analytics, and mathematics. These are some of the most common tasks, among many others, that AI is used for in healthcare:
Let’s quickly talk about the kinds of artificial intelligence applied to all of those needs.
AI has branched out into many subdisciplines, but the following three are notable for healthcare:
Machine learning
ML deals with large datasets that need to be labeled and structured by a human operator before processing. It uses mathematical algorithms to sift through that data and makes inferences based on preset criteria.
Deep learning
Deep learning is a subfield of machine learning that requires less human supervision, especially at the data input stage. DL is a scalable version of ML that can process larger, less structured datasets than ML can. Deep learning algorithms are capable of finding patterns in data on their own.
Natural Language Processing
Natural language processing, or NLP, is another subdiscipline of AI and machine learning. NLP concerns itself with text and speech recognition, and is instrumental in working with unstructured data.
Before we move on to specific AI algorithms for healthcare, it’s important to understand the distinction between the types of data AI systems can ingest.
AI algorithms are essentially filters, and some algorithms require a specific type of input material.
Structured data refers to information that has already been identified and organized. For instance, data stored in EHRs, with clearly identified fields and labels are structured data. When an algorithm processes structured data, it knows exactly what it is fed and what output it’s looking for.
Unstructured data, on the other hand, is not organized. The AI algorithm has no identifiers or predefined structure to rely on, and it has to figure out how to make the data useful. Examples of unstructured data include medical notes in audio or text format, images, and so on.
One of the biggest advantages promised by AI in healthcare is the ability to harness unstructured data. Computer scientists have used its structured counterpart for a long time, but that’s only the tip of the iceberg. By applying deep learning algorithms, clinicians and medical researchers can extract valuable insights from unstructured datasets within realistic timeframes. Until recently, this segment of data hasn’t been sufficiently explored, but AI provides the necessary tools and computational algorithms to successfully tackle unstructured data.
So what are the algorithms used in AI development, and how do they work? Read on to find out.
Artificial intelligence enlists an almost endless number of soldiers in its algorithm army. We’ve hand-picked the algorithms that see heavy use and provide the most value in the medical industry.
If we were to point to the algorithm most inspired by the human brain, it would have to be an artificial neural network (ANN). ANNs consist of layers of nodes that filter input based on specified parameters. They are a type of ML algorithms that requires very little human supervision when training and can crunch huge amounts of data in a short time.
As for their application in healthcare, ANNs are used to analyze medical imaging, biochemical studies, as well as for text and speech recognition.
One of the most popular algorithms for medical diagnosis, logistic regression has been favored by the medical industry for a long time. It’s a relatively simple ML method to predict the state of a variable, usually a binary variable. Logistic regression helps assess the probability of an event (such as having or not having a disease) and helps solve classification problems.
Another ML approach frequently used by the healthcare industry, support vector machines (SVMs) are a direct rival of logistic regression. It’s a non-probabilistic binary linear classifier, and works by assigning the data it’s fed to one of two categories. The algorithm presents input data as vectors and splits them by plotting a hyperplane.
Medical scientists have been using SVM in drug discovery and development since the 1990s. Image segmentation and text categorization are also typical use-cases of SVM. Support vector machines help classify data from large, imbalanced, or incomplete datasets.
Just as the name suggests, this ML algorithm grows trees, only in this case, they’re decision trees. An operator feeds data into multiple trees, and then based on the output of all the decision trees, the algorithm determines the average, most probable outcome.
Disease prediction and analysis of magnetic resonance imaging (MRI) and electrocardiogram (ECG) data are areas of healthcare that benefit most from using random forest algorithms.
This method, widely used in heart disease classification and medical data mining, focuses on predicting the class of an unknown variable. To achieve that, it learns by analyzing known classes and approximating where new data points fall within the class. The K-nearest neighbor algorithm is often used for predicting medical conditions.
When you need to refine a data classifier or when you question its adequacy, discriminant analysis is the standard choice. LDA (linear discriminant analysis) is especially instrumental when it comes to finding specific sets of characteristics that distinguish two or more classes of events or objects. As for typical use-cases in healthcare, this ML algorithm helps identify diseases at early stages using data from EHRs.
Based on the eponymous theorem, the naive Bayes algorithm is among the most prominent AI algorithms used in healthcare. By applying naive Bayes probabilistic classifiers, you can model the quality of patient care at a facility or in a home setting. It’s also used to enhance disease prediction. The algorithm displays extremely high sensitivity and specificity and is another alternative to logistic regression. One of its strengths is the ability to efficiently work with dependent and independent variables.
AI algorithms, combined with ample computational resources, are reliable and powerful tools that can solve many challenges in healthcare. It all comes down to choosing the right method for your needs and implementing it in the best way possible.
That’s something Postindustria can help with. We are skilled in custom AI development and ready to take on your medical algorithm project, however complex it may be. We know how to build a machine learning pipeline and are expert in medical data science.
With Postindustria, you’ll always have a live person to talk to about algorithms.
