Automated machine learning (AutoML) plays an essential role in the future of machine learning within many industry segments. It enables the widespread adoption of machine learning by industry specialists without ML expertise. Further, it dramatically simplifies the implementation of compelling and leading-edge solutions across multiple domains. In the healthcare arena specifically, AutoML powers predictive and actionable analytics that inform and improve decision-making, benefiting stakeholders like doctors, administrators, clinicians, researchers, and ultimately patients.
AutoML- ML made easy
AutoML provides methods and processes to make machine learning available to non-machine learning experts by automating the time-consuming, iterative machine learning model development tasks. Although traditional machine learning automatically improves algorithms as it processes data and gains experience, with the intervention of a data scientist, more extensive gains are often made through iterative algorithmic tweaks. However, these adjustments are often time-consuming, tweaking parameters, and generating predictive models with the best performing results. AutoML makes the process much more streamlined, efficient, and effective, essentially replacing that iterative process altogether.
AutoML in healthcare
AutoPrognosis is a framework created to automate the design of actionable predictive models informing clinicians about the future course of a patient’s clinical conditions. It applies the principles of AutoML to the medical area of prognosis that calculates and predicts the risk of future health outcomes in patients. AutoPrognosis can be more accurate than human diagnosis and inherently adaptable to numerous clinical settings like early diagnosis for cardiovascular disease, breast cancer, and cystic fibrosis. In addition, AutoML has proven helpful in predicting ICU admission and for hospital capacity planning.
With AutoML, healthcare researchers can perform data analysis faster by isolating and identifying new biomarkers for many diseases in a more efficient manner while enabling diagnostics and prognoses that are less intrusive and more cost-effective. AutoML platforms allow users to upload their data – ranging from data scientists with deep machine learning expertise or clinicians with no experience in coding, thus making data analysis a lot easier for non-experts and more efficient for experts.
AutoML is emerging as the go-to technology within many industries as it automatically selects, composes, and parametrizes machine learning models to achieve optimal performance. AutoML delivers a broader range of informative and actionable analytics to support clinical decision-making, personalization in screening and monitoring, time-to-event analysis (survival analysis), interpretations, uncertainty estimates, healthcare discoveries, and much more. AutoML transforms the mundane and tedious data entry tasks in EHRs into a tool that empowers healthcare providers and patients.
The application of machine learning in healthcare has demonstrated material benefits for providers in delivering services, cutting healthcare costs, and advancing clinical research. While many providers lag in ML adoption, often due to lacking the expertise required for model development and deployment, this challenge is easily mitigated through an AutoML implementation strategy. AutoML takes care of much of the data science heavy lifting necessary for traditional ML, simplifying and streamlining the process.
If you would like to leverage AutoML within your healthcare environment, send us your query to firstname.lastname@example.org. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.