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Machine Learning in Cardiac Health Monitoring and Decision Support

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Machine Learning in Cardiac Health Monitoring and Decision Support COVER FEATURE SMART HEALTH AND WELL-BEING Machine Learning in Cardiac Health Monitoring and Decision Support Shurouq Hijazi and Alex Page, University of Rochester Burak Kantarci, University of Ottawa Tolga Soyata, SUNY Albany Portable medical devices generate volumes of data that could be useful in identifying health risks. The proposed method filters patients’ electrocardiograms (ECGs) and applies machine-learning classifiers to identify cardiac health risks and estimate severity. The authors present the results of applying their method in a case study. s personalized medicine becomes increas- decision-support systems based on machine learning ingly more sophisticated and affordable, (ML) can ease the review burden by filtering noise, errors, portable medical devices have become ubiq- and irrelevant information so that the data reviewed uitous and monitoring applications have contains only relevant clinical markers. ML algorithms begunA to blend a range of functions. AliveCor, for exam- learn patterns within the data, which serve as the basis ple, offers an inexpensive smartphone electrocardio- for predictions about patient health. A machine can look gram (ECG) attachment that can sample an individual’s through millions of reports and medical records to iden- ECG, calculate real-time statistics, and share the record- tify previously unknown drug interactions.1 Such algo- ing with a physician. rithms can significantly improve diagnostic accuracy, In aggregate, portable medical devices generate data healthcare quality, and patients’ quality of life. at a much higher rate than conventional systems, which ML algorithms have many potential applications in can overwhelm medical personnel who must review smart health. To explore one of these, we developed a reports for many patients. However, the data also pres- method that filters data from long-term ECG recordings ents scientists and engineers with the opportunity to of patients with Long QT Syndrome (LQTS), uses ML to create health-monitoring and decision-support systems identify circadian patterns that signal risk of symp- that enhance and personalize healthcare. For example, toms such as cardiac arrhythmia, and estimates the 38 COMPUTER PUBLISHED BY THE IEEE COMPUTER SOCIETY 0018-9162/16/$33.00 © 2016 IEEE Acquired data Patient Physician EHRs Delineation Q, R, S, T (n leads) QTc > 470 ms ST elevated Clinical severity of that risk. LQTS is a disor- markers Database Alerts and der that primarily affects ion chan- queries thresholding QT, RR, QTc, nels in heart muscle cells, allowing ST amplitude abnormal electrical activity to occur (n leads) Syncope 87% that can lead to sudden and dangerous Database TdP 54% Merge Seizure 22% arrhythmias. We tested our method (optional) using four classification methods AMI 4% QT, RR, QTc, against a database of 434 24-hour ECG Filtering Classication Visualization ST amplitude (optional) and ML recordings. We also explored how our (1 lead) method might scale with the volume Decision support of medical data. Scalability is rapidly becoming critical in medical studies. FIGURE 1. Conceptual workflow of a remote health-monitoring and decision-support Analyzing massive amounts of data system. Data from a patient at a remote location is acquired, preprocessed, and used to not only promotes a deeper under- provide decision support in several forms. Visualization provides simple summaries to standing of the mechanisms that the physician or other clinician without any recommendations. Alerts are triggered by cause diseases but also makes per- more urgent events, such as the violation of an established threshold. Classification of sonalized treatment possible. Such the patient’s condition is based on the results of machine learning (ML), which involves analyses can lead to breakthroughs comparing the patient to existing electronic health records (EHRs). AMI: acute myocardial in relating genes to diseases as well as infarction; Q, R, S, T: waves that indicate cardiac electrical state (on an electrocardiogram providing the basis for treatment ori- [ECG]); QT, RR, QTc, and ST: intervals in the cardiac electrical cycle, also measured on an ented to a particular patient’s lifestyle ECG; TdP: torsades de pointes, a cardiac arrhythmia. and genetic makeup. AN ML-BASED SYSTEM Work flow cases, it would be desirable to obtain We envision incorporating our method Figure 1 is a conceptual diagram of more detailed information about cer- in a remote health- monitoring system the workflow for a healthcare system tain patients from their physicians— that can provide feedback and decision that stores patient data electroni- an impossibility because it would support to a clinician. The system would cally. After preprocessing and filter- violate HIPAA. Consequently, regula- use devices that acquire data through ing patient data, the system stores it tion, not just technology, can limit the the Internet of Things and are connected as an electronic health record (EHR). acquisition of needed data. to a cloud-based decision-support sys- Each EHR gradually enriches the data- Even after removing identifying tem.2 The technological components base, which will improve the accuracy information, what remains could of such a system are within reach, and of future ML results. A large database be combined to statistically reveal a advanced devices for acquiring medical with many patients’ records might not patient’s identity. On one hand, PHI data are becoming commercially avail- be as useful as a database with fewer information such as age, gender, able.3 Sophisticated and powerful ML patients but more information on race, and genetic disorders, is critical algorithms are already well understood each patient. to developing an effective decision- and accessible.4 When many patients’ records are support system. On the other, includ- However, the human brain has aggregated and analyzed, steps must ing too much information on the unmatched reasoning abilities, so the be taken to protect the individuals’ wrong computer system risks violat- physician is still the most important privacy. Most protected health infor- ing HIPAA. Applications can also cre- part of any medical decision- support mation (PHI), such as names and birth- ate privacy violations because some system. Thus, the goal of our envisioned days, can be removed from records in require explicitly protected informa- system is to provide physicians or other compliance with the Health Insurance tion such as a patient’s voice print6 or clinicians with concise, relevant infor- Portability and Accountability Act city of residence. Researchers must mation that can increase their diagnos- (HIPAA) without detriment to the data keep these restrictions in mind during tic efficiency and accuracy. mining process.5 However, in some all stages of a study. NOVEMBER 2016 39 SMART HEALTH AND WELL-BEING Decision support typical ECG report that a cardiologist pointes (TdP), which are likely to cause An effective ML-based healthcare reviews. When real-time monitor- serious symptoms such as seizures, system capitalizes on the computer’s ing reveals an urgent issue, an alert fainting, or sudden death. It is there- vast computational capability and the would immediately be sent to both the fore critical to monitor the QT inter- physician’s reasoning ability. Both patient and physician through SMS, val in patients prone to this disorder machine and physician are looking for pager, or an application. using long-term ECG recordings. Data patterns, but the physician cannot ana- recordings of ambulatory patients over lyze every heartbeat of every patient Evolving symbiosis. The physician several hours or days are called Holter or be familiar with every disease’s is still at the head of this process— recordings or simply Holters. nuances. The machine can do all these ordering tests, analyzing records, Our study focused on congenital tasks and then present its conclusions adjusting prescriptions, and so on. LQTS rather than the drug-induced to the physician for confirmation. The machine’s visualizations and form. In the database used for the recommendations are simply addi- study, we knew which recordings were Support types. As Figure 1 shows (see tional decision- making tools. Over from patients with symptoms, but we box at lower right), we envision three time, the database will expand, and did not know if the symptoms came types of decision support: visualiza- the machine’s classifications will be before or after the ECG recording. Con- tion, alerts, and classification. more accurate. But improvements will sequently, we had no way to use ML to Visualization puts long-term mon- be symbiotic: as the machine’s accu- predict when symptoms would occur itoring data in a concise and intuitive racy grows, the physician will develop or to detect symptoms in real time. format,7 which could significantly an intuition for how and when the Instead, we attempted to identify when reduce the physician’s data burden machine makes those accurate classifi- a recording came from a patient whom and enable timely and accurate deci- cations and recognize when it might be we knew had symptoms in the past sion making. fallible. For example, a patient might or would have them in the future. In Alerts are alarms that activate when have an abnormal T-wave morphology other words, we attempted to identify a value crosses an established thresh- that the algorithms did not process the patient’s risk—an important con- old. The value can be simple to check, correctly, or a patient’s heart
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