Artificial Intelligence is called to star in the central core of the Fourth Industrial Revolution (Industry 4.0), along with IoT, blockchain, robotics, analytics, nanotechnology. The massive interconnection of digital systems and devices, and the emergence of new tools will lead to a profound transformation of production and business models.
The great attraction of these technologies, however, may be blurring the fact that the data must be validated, whether it is historical and/or referenced.
Data science, artificial intelligence and machine learning have become fundamental fields of knowledge that support the work of professionals in health areas and help improve the diagnosis of diseases and the quality of life of patients.
Analyzing and understanding the data much better will allow us to understand, for example, what type of treatment is most effective and the desired impacts. This allows doctors and other healthcare practitioners to make better decisions.
Data analysis has caused medical science to reach an unprecedented level of development: from the digitization of medical records to the discovery of drugs and the exploration of genetic diseases. Did you know that the health industry and Data Science are commonly linked through finance, as the medical sector is looking for a significant reduction in expenses? We will tell you some other interesting facts about how Data Science has influenced the study of health in this article that we have prepared for all passionate people like you.
To begin to delve into the subject, it is significant to point out that the medical industry makes use of large amounts of information: for example, digital medical records, clinical studies, genetic information, bills, care management, databases, scientific articles, networks social and internet research. With these hermetic data sets, scientists engage in analysis of both identifying patterns and hypotheses and risk assessment; or prediction through Machine Learning models that calculate the possibility of an event occurring in the future based on known variables.
Below we point out some actions where Data Science has been used for better care not only of health, but also of quality of life:
In the discovery of the alterations that a medicine can cause in the human body, scientists use Big Data management to simulate the reaction of a medicine in proteins of the body, in different cells and conditions so that there is an improvement in the times approval of a new medicine, which currently can take.
One of the most desirable actions within health care is disease prevention. Habit that not only ensures that every citizen has a better quality of life, but also promotes greater savings for the entire health system. In this sense, Data Science has influenced this fight through the implementation of smart devices that help monitor and promote healthy habits.
Medical imaging is one of the most fundamental applications of Data Science in medicine because it brings great insights to the process of diagnosing a patient. For example, at Stanford University there are researchers who have developed data driven models to diagnose irregular heartbeats and recognize benign or malignant skin lesions.
The state of data science and machine learning in healthcare
As healthcare continually advances through digitalization and digital transformation, it’s become one of the best equipped industries to maximize usage of data science and machine learning. Since 2015, venture capital investments in AI companies in healthcare have seen a 22X increase in Europe alone (McKinsey).
Data science and machine learning are transforming healthcare across several verticals, from patient care to pharmaceuticals and more. But scaling the impact of data science in healthcare requires careful consideration of many challenges, including compliance, data governance and oversight, data culture, and the availability of data skills.
How is Drowzee contributing to the data science X healthcare universe?
Drowzee works on the model of neurofeedback training to help its users achieve optimal results with their current sleep schedules.
Neurofeedback training is a technology-based physical and mental training technique. Systems and software record and monitor and measure physiological activity: muscle tension, respiration, cardiovascular system, electrodermal response and brain waves.
Pendulum is plug and play, based on science, and makes improving sleep engaging by augmenting data insights into pragmatic actions. The science behind it is inspired by a heritage of research, called operant conditioning, and further developed in-house, based on real user data and analytics.
The solution consists of a EEG headset, capable of reading brain activity in real-time, and a mobile application for keeping an overview of data analytics and suggestions for action. It allows the user to conduct brain training, which rebalances the brain for better sleep, monitor and track their sleep quality, and receive concrete suggestions for improvements.
The headband itself can be worn during the night for tracking and analysing sleep. However, the Pendulum also allows for third party sleep monitor integration for more accurate data gathering. Data from the headband and integrated devices are utilised to provide the users with pragmatic suggestions for how to improve their sleep health.
The training includes optimizing mental performance, teaching the athlete to control their brain function, teaching them to work in their optimal zone of control, concentration, and relaxation, based on their brain wave pattern. To optimize physical performance, control of heart rate, breathing and muscle tension is trained. First, its base values are analyzed and then a personalized action plan is executed to optimize all the variables.
Another model used by Drowzee for sleep training in relation to Neurofeedback training is closed loop training of the brain. apses of attention can have negative consequences, including accidents and lost productivity. Drowzee uses closed-loop neurofeedback to improve sustained attention abilities and reduce the frequency of lapses.
During a sustained attention task, the focus of attention is monitored in real time with multivariate pattern analysis of whole-brain data through the EEG Headset. When indicators of an attentional lapse are detected in the brain, the Pendulum app gives Drowzee users feedback through rewards during their sessions.
As per studies and user data, we have seen sleep performance improvements after an average of 6 to 10 training session.
To learn more about how you can get the Pendulum EEG headset and mobile application visit here.