3 Ways Healthcare Apps Make Use of Machine Learning
3 Ways Healthcare Apps Make Use of Machine Learning
See how healthcare apps are leveraging machine learning.
Join the DZone community and get the full member experience.Join For Free
The healthcare industry has generated plenty of data. The new method of data collection, such as sensor-generated data, has helped this industry to find a spot in the top.
What if this data can be used to provide better healthcare services at lower costs and increase patient satisfaction? Yes, you heard it right. It’s actually possible by applying machine learning (ML) techniques in the healthcare industry.
Machine learning is already being used in diverse situations in healthcare. With the help of effective machine learning implementations, it enables healthcare professionals in better decision-making, identifying trends and innovations, and improving the efficiency of research and clinical trials as healthcare offers a wide range of data. This data is used for analysis, prediction, diagnosis, and treatment. Let us see how machine learning can address this challenge.
Machine Learning in Healthcare
In this digital era, the healthcare industry is being transformed by the advancements in machine learning and artificial intelligence (AI). Previously, it was a challenging and tough task for healthcare professionals to collect and analyze the huge volume of data for effective prediction and treatment. But, by leveraging real-time data, machine learning allows you to analyze data and deliver results. It is being used in healthcare to provide superior patient care and has given accurate and better outcomes.
Now, utilizing machine learning in healthcare, it’s been relatively easy, as big data technologies such as Hadoop are mature enough for wide-scale adoption.
In fact, as per the Ventana Research Survey, 54% of organizations are using or considering Hadoop as a big data processing tool to get important insights on healthcare. 94% of Hadoop users out of existing users perform analytics on voluminous data, which they believe was not possible before.
This new ML-based technology will help in providing vital statistics, real-time data, and advanced analytics in terms of the patient’s disease, lab test results, blood pressure, family history, clinical trial data, etc. to doctors.
See some glimpses into healthcare areas where machine learning can be applied to change the future of healthcare.
Recognizing Diseases and Diagnoses
One of the major machine learning applications in healthcare is to identify and diagnose diseases that are considered as hard-to-diagnose. This includes anything from cancers that are tough to diagnose during the initial states.
IBM Watson Genomics is a prime example of how integrating cognitive computing with genome-based tumor sequencing can help in making a fast diagnosis.
The modern technology works towards a better health environment and to prevent the disease with early intervention rather than go for treatment after diagnosis. The doctors or physicians use fundamental information such as demographics, medical conditions, life routines, and more to calculate the probability of developing a certain disease.
Machine learning being processed on computing devices can consider a large number of variables, which results in better accuracy of healthcare data.
As per the recent study, the researcher obtained better diagnostic accuracy, using entire medical records by considering around 200 variables.
One of the primary clinical applications of machine learning lies in the early-stage drug discovery process. It also includes R&D technologies such as next-generation sequencing and precision medicine. Drug discovery and development is very costly and time-consuming work. As per the report, new drug development takes more than 10 years to get into a market and costs roughly around 2.6 billion dollars.
Microsoft has developed Project Hanover by using machine learning-based technologies for multiple initiatives including developing AI-based technology for cancer treatment and personalizing drug combinations for AML (Acute Myeloid Leukemia).
Electronic Health Records
Maintaining up-to-date records is a very tedious process, and while the technology has played its part in the healthcare sector, the data availability and accessibility makes its way to maintain electronic health records. The main role of machine learning is to ease the process to save time, effort, and money. ML-based EHR model transfer approach helps apply predictive model across different EHR systems. Such models can be trained using datasets from one EHR and can be utilized to predict an outcome for another system.
The data may come in many forms —structured and unstructured, such as images, text, medical imaging, and more. Datastore is not a major concerned, but since the data comes in an inconsistent format, it's really hard to deploy this data for analysis and predictions.
Machine learning technologies such as image processing, optical character recognition, natural language processing, and others can help to convert these data into the structural and appropriate format from various sources and multiple systems.
The above-mentioned are the few areas where machine learning can step in to help the healthcare industry. Machine learning in healthcare and medicine segments can advance into a new realm and completely transform the healthcare operations.
In fact, there are many mobile app development companies today are leveraging the power of machine learning when developing mobile app solutions. Whether the company is developing healthcare and wellness apps, photo and video apps, any other kind of app, machine learning can revolutionize today’s apps, making them more powerful.
Opinions expressed by DZone contributors are their own.