How Machine Learning is Transforming Disease Risk Prediction in Healthcare

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Disease risk prediction is a cornerstone of preventative healthcare. It is used to provide guidelines for clinicians to follow to identify their most at-risk patients and provide guidance to reduce risk. Effective predictions allow for early intervention, personalized treatments, and improved outcomes. However, traditional models often struggle to account for the complexities of human health.  

Machine learning has emerged as a powerful tool that can revolutionize disease risk prediction using the vast amounts of healthcare data available in electronic health records, wearables, and other sources. Here, we will explore how machine learning has transformed disease risk prediction in healthcare and where future improvements can continue to be made.  

Traditional Methods of Disease Risk Prediction in Healthcare

Historically, disease risk prediction has relied on classical statistics models including logistic regression, Cox proportional hazards models, and survival analysis. This class of models assesses the probability of disease based on known input variables. These variables typically include family history, lab values, behavioral factors, and demographics like age and race. For example, a commonly used risk calculator is the Framingham Risk Score. This tool estimates the risk of cardiovascular disease using a linear regression model.  

While these models are useful and have proven statistical significance, they do come with limitations related to the specific type of model. For example, the Framingham Risk Score assumes a linear relationship between the variables and outcome, which may not always be true if there are complex, non-linear relationships in the data. These models also rely on specific, structured data inputs, such as numeric lab values or race to be selected from a multiple choice list. Unstructured data like clinical notes, imaging, or genetics cannot be incorporated into most of these traditional models.  

Therefore, traditional models often oversimplify the complexity of human health. This leads to less accurate predictions and can mask the importance of difficult to collect variables in predicting health outcomes. This is where the power of machine learning models can come in.  

How Machine Learning Enhances Disease Risk Detection

The main benefit of machine learning is that it can analyze large scale, high dimensional data to uncover hidden patterns that traditional models cannot detect. Specifically, machine learning models can handle a wide range of data types. This includes both structured and unstructured data, like electronic health records and genomic data.  

Machine learning also excels at identifying complex interactions between multiple factors simultaneously. This allows for more accurate predictions, as well as highly personalized ones that pull in more data points from each patient.  

An added benefit is that machine learning models can dynamically adjust as more information becomes available. For example, traditional cardiovascular disease models may rely solely on static inputs such as lab values and demographics. Machine learning models can integrate constantly updating data such as that which comes from wearables or clinic notes to provide up-to-date predictions of heart risk.  

Applications of Machine Learning to Specific Diseases

There are many specific applications where using machine learning to develop medical risk prediction models can be beneficial. Cardiovascular disease is one of the leading causes of death worldwide. Machine learning models can be developed to enhance risk prediction beyond traditional risk factors. For example, these models can include real-time data from wearable devices or use neural networks to analyze medical imaging.  

Another disease where machine learning is making significant strides in risk prediction is Type 2 Diabetes. These models can combine many factors, including lifestyle, lab results, family history, and narrative style clinical notes to predict who is at risk. The earlier interventions are put into place for high risk groups, the better the long term outcomes tend to be.  

Machine learning models have also shown a lot of promise in predicting neurodegenerative diseases like Alzheimer’s. Early diagnosis of these diseases can be challenging and, often, neurodegenerative diseases progress silently for years. Machine learning can analyze a variety of data types to identify novel indicators and early warning signs for Alzheimer’s to determine risk before clinical symptoms emerge.  

Key Machine Learning Techniques Used in Disease Risk Prediction

There are many types of machine learning techniques and models that can be applied to disease risk prediction. Three of the most commonly used are:  

• Supervised Learning: This type of learning involves training models on labeled data where the outcome, such as disease status, is already known. Common models under this type of machine learning include random forests and support vector machines. These models can be the easiest to run and more closely align with traditional statistical techniques where data from known patients is used to predict the likelihood of disease in future patients.
• Unsupervised Learning: Unsupervised learning does not have labels in the dataset to indicate who does or does not have the disease. This type of machine learning identifies clusters of similar patients and is best used to group patients into distinct risk categories based on similarities in their data. One important application for this type of machine learning in health risk detection is finding previously unknown or underserved subpopulations that are at higher risk for specific diseases.
• Natural Language Processing: A growing source of in healthcare is text-based unstructured data, including physician notes in the chart, patient messaging portals, and discharge summaries. Because this data is unstructured, it has been difficult or even impossible to incorporate into traditional statistical risk models. However, natural language processing allows machine learning models to incorporate this new source of data to identify risk factors otherwise left uncaptured.

Future Directions of Machine Learning and Disease Risk Prediction

 
The future of machine learning in disease risk prediction lies in the integration of even more diverse data sources and techniques. As more data and computational abilities become available, machine learning models can be significantly improved. New data sources such as genetics and epigenetics can also be incorporated to further drive personalized medicine forward.  

Future work should also include improving how these predictive machine learning models are incorporated into clinical practice. Healthcare professionals should have quick and easy access to these models and their predictions from within the electronic health record. This will require the collaboration of a range of stakeholders, and may necessitate the development of more transparent and interpretable models.  

Summary

 
Machine learning is revolutionizing many fields, including disease risk prediction within healthcare, by offering more accurate, personalized, and real-time predictions. These models can incorporate a vast amount of data from a range of sources, overcoming the limitations of traditional statistical methods of disease risk prediction. This technology has the potential to transform preventative healthcare and drive the field towards a future of personalized medicine.  
 
 

Mehrnaz Siavoshi holds a Masters in Data Analytics and is a full time biostatistician working on complex machine learning development and statistical analysis in healthcare. She has experience with AI and has taught university courses in biostatistics and machine learning at University of the People.