Machine Learning in Medicine

 

MUSC is developing machine learning algorithms that harness big data to transform patient care.

The avalanche of medical data now available to clinicians and researchers represents an unprecedented opportunity to transform health care and realize precision medicine. High-throughput screening of patient samples yields multi-omics data (e.g., genomics, proteomics, glycomics), the electronic health record (EHR) provides information on clinical care and history, and wearables provide real-time updates on a patient’s activity and  vital signs.

Machine and deep learning, subsets of artificial intelligence (AI) cannot only handle these burgeoning datasets, they actually need them if they are to continue to grow “smarter.” When trained on appropriate datasets, deep learning algorithms have:

• Referred patients for more than 50 sight-threatening diseases as well as a panel of ophthalmologists
• Distinguished malignant melanomas from benign moles with better sensitivity than dermatologists (95 vs 88 percent) and
• Detected metastases in sentinel lymph nodes better than pathologists.

An electronic safety net

At MUSC, a data science team in MUSC’s Information Solutions is using machine and deep learning to predict patient risk and outcomes.

Analytics predict which patients are at highest risk of:

• Dying while in the hospital
• Being readmitted or
• Visiting an ER.

They can also identify — in real-time — patients who are, for instance, likely to deteriorate or to develop sepsis.

Unlocking the power of clinical notes

Predictive analytics must tap into the richest data in the electronic health record – the clinicians’ notes. Natural language processing, another subset of AI, enables computers to begin to understand human language. This is not simple keyword searching. It involves syntax and complex semantics. In other words, the algorithm can recognize that a word’s meaning is changed by its context.

Precision medicine bioinformatics

Precision medicine informatics analyzes all of that information in order to recommend optimal treatment that is personalized for each patient. The patient’s response to a certain treatment can be “predicted” based on how well groups of patients with similar features have responded to that therapy in the past.

A good example of the predictive analytics platform is one that was used at Ralph H. Johnson VA Medical Center to cluster patients with diabetes undergoing knee or hip surgery by A1C levels, and predict outcomes for each group. It was designed as infrastructure-as-code that enables the computer infrastructure required to run a predictive analytics algorithm to be cloned in the cloud or on as many computers as are necessary at a new institution. Thus, the same platform could reused and expanded, for instance, to examine how chronic stress affects prostate cancer outcomes.

Predicting patient preference

A team in the MUSC Department of Health Sciences is working to create a collaborative filtering recommender system for patient treatment preference, much like those that Netflix or Amazon uses to recommend you new products based on the past purchasing history of people like you.

There are three stages to developing such a system:

1. eliciting patient preferences via surveys
2. clustering patients into preference phenotypes based upon their responses, and
3. using satisfaction and quality-of-life data from patients in the same preference phenotype who have already undergone treatment to recommend therapies that would be most aligned with their preferences.

Enabling citizen science

The ubiquity of smart phones and watches that can continuously track heart rates, steps taken and other health-relevant data could revolutionize clinical trials, making citizen science feasible. Compared with traditional trials that rely on study coordinators at medical centers to enroll patients from the surrounding region, citizen science trials aim to virtually enroll large numbers of participants from across the country.

MUSC has created a software platform that is being used to facilitate citizen science. The platform weds Apple’s ResearchKit and HealthKit to REDCap, a widely available, HIPAA-compliant data collection and management tool for researchers, via an API, basically creating a secure repository for data collected from health care apps. More than 40 apps for specific health care use cases using his platform technology have been created at a fraction of the cost one such app typically costs to develop.

Future state

Artificial intelligence, including machine and deep learning and natural language processing, is poised to begin transforming medicine. MUSC and Clemson University have begun jointly offering a joint Ph.D. degree in Biomedical Data Science and Informatics that provides training in mathematical and statistical modeling, “hacking” skills that enable them to implement algorithms, and an understanding of the data sciences needs of medicine.

This will help ensure that South Carolina has the workforce it needs to remain competitive in the health care of tomorrow and to make available to its citizens the many medical breakthroughs that machine and deep learning are predicted to bring.