Creating data-driven insights about sleep medicine will become an indispensable tool for physicians seeking to truly understand their patients. Machine learning approaches problems by learning rules from data, starting with patient-level observations, algorithms sift through vast numbers of variables, looking for combinations that reliably predict outcomes. The creation of algorithms requires millions of observations to reach acceptable performance levels and to handle enormous amounts of predictors that are combined in nonlinear and highly interactive ways.
As patients’ conditions and medical technologies become more complex, the role of machine learning will grow, and clinical medicine will be challenged to grow with it. Expanding the use of electronic health records (EHR) and generating well-defined sleep data from the growth and development of national research repositories, along with establishing patient networks and registries will aid in developing these observations. Data collected by integrating patient data from wearable sleep testing and monitoring devices will also be used by this technology.
Machine learning applications will provide physicians with access to new kinds of sleep data, whose sheer volume or complexity would previously have made analyzing them unimaginable. Analysis of large-volume data holds the promise for improving the application of precision medicine to sleep, including improving the identification of patient subgroups who may specifically benefit from alternative therapies.
Using data-driven insights for the field of sleep medicine will provide these key benefits:
- Boost the ability of physicians to establish a prognosis. Current prognostic models are restricted to only a handful of variables, because humans must enter and tally the scores. However, data could instead be drawn directly from EHRs or claims databases, allowing models to use thousands of rich predictor variables. Using machine learning with massive data sets will drive rapid improvements in performance, and machine accuracy, as well as monitor and interpret streaming physiological data.
- Increase diagnostic accuracy by preventing human errors and the lack of interventions to reduce them. Algorithms will soon generate differential diagnoses, suggest high-value tests, and reduce the overuse of testing. The standard for diagnosis is still unclear in many conditions making it harder to train algorithms. High-value EHR data are often stored in unstructured formats that are inaccessible to algorithms without layers of preprocessing. Models need to be built and validated individually for each diagnosis.
- Drive utilization of enormous amounts of data from macro-level physiology and behavior to laboratory and imaging studies. Machine learning will become an indispensable tool for physicians seeking to truly understand their patients. As patients’ conditions and medical technologies become more complex, the role of machine learning will grow, and sleep medicine will be challenged to grow with it. Patients, whose lives and medical histories shape the algorithms, will greatly benefit from machine learning.
By using data-driven insights, physicians will be able to quickly facilitate patient screening, diagnosis, and management of sleep disorders. They will be able to improve the recognition of differences in the presentation and susceptibility patients have to sleep apnea and other sleep disorders – leading to improved care management and outcomes. To meet the vision of personalized, precision therapeutics and diagnostics and improving the efficiency and quality of sleep medicine will require ongoing efforts, investments, and change in our current medical and research cultures.
Somnoware is transforming sleep health management by accelerating diagnostics and increasing therapy adherence. To learn how you can automate sleep testing processes, obtain actionable data and predictive insights, and drive greater patient engagement, schedule a demo by clicking here.