Deep Learning has been the most frequently researched and talked topic in data science recently. And it deserves the attention it gets, some of the recent breakthroughs in data science emanates deep learning. It’s predicted that many deep learning applications will have a significant impact on your life shortly. However, if you look at deep learning from outside the context, it might sound complicated, confusing, scary and intimidating. Sometimes terms like Machine Learning (ML), Artificial Intelligence (AI), neural networks might scare you. However, it’s not that difficult! While looking in depth with deep learning It might take effort and time to understand and follow, but applying them on a regular basis on day to day life will make you face your problems at ease.
Machine learning, AI, and deep learning have gained a lot of popularity and attention in the recent times. These emerged technologies are revolutionizing various industries like retail, offline market, finance, travel, manufacturing, healthcare, and so on.
Healthcare is one such industry which implements these revolutionizing technologies the most. As health is the priority for all, medical experts and doctors are continually trying to innovate ways to implement new technologies and provide instant results. Especially Deep learning in healthcare can uncover the hidden opportunities and unrecognized patterns in clinical data, and also offers pathbreaking innovative applications. Deep learning gathers an enormous volume of data, which includes patients’ records their medical reports, and insurance records, and applies its neural networks to provide the practical outcomes, which results in helping doctors to treat their patients effectively and instantly.
Deep learning in healthcare assists medical researchers and professionals in discovering the hidden ways to serve the healthcare industry at its best with deep learning the doctors can analyze any disease accurately and help them to treat their patients in a better way. Thus it results in better medical decisions.
Whereas the predictive measures in children healthcare with the help of deep learning is quite essential at present because these days a kid with a smartphone is very common surprisingly these kids not only carry a smartphone with them instead they are mastering them. Whether it be texting, games or engaging in other activities there is no doubt that this digital enhancement in children needs a monitor of their actions and preventive measures of diseases that cause due to excessive use of this wireless devices.
There are certain diseases predicted and treated using deep learning some of the references are
children who are affected with autism have trouble recognizing the emotional state of other people around them, therapists have developed a robot which is armed with a personalized deep learning network that demonstrates those unrecognized emotions and engages children by imitating those emotions and respond to them in most appropriate ways.
"The long-term goal is not to create robots that will replace human therapists, but to augment them with key information that the therapists can use to personalize the therapy content and also make more engaging and naturalistic interactions between the robots and children with autism," - Oggi Rudovic, (postdoc at the Media Lab and first author of the stud).
Deep learning would be useful to perceive the children’s behavior more naturally, though this ideology of deep learning has been around since late 1980’s its recently we have got this enormous computing power to implement this kind of technology into our lives and believes it grows.
Early Childhood obesity has increased tremendously in the past few decades due to the change in the unhealthy lifestyle and more explosion to the wireless devices. Many research has been undergoing to identify interventions to prevent and remediate obesity in children since obesity in adulthood has several adverse health effects.
Deep learning technique is techs create an attractive modeling method for analyzing early clinical data to predict later obesity in children. Such techniques can reduce the complexity of this problem than the more straightforward modeling techniques. Deep learning technique could be a practical approach to predicting future obesity among children.
The paramount to health is the quality of sleep you undergo; insufficient sleep affects the physical, mental and emotional well-being which results to a multitude of complications in health, in recent times children are more open to poor sleep quality which affects their health, vision and brain health.
This poor sleep efficiency can lead to a significant health risk for children like cardiovascular disease, mental disorders, decreased cognitive function for memory and judgment and also contribute in multiple lifestyle diseases like type 2 diabetes mellitus and obesity.
In the recent times, researches are going on to explore the use of deep learning methods to predict the quality of sleep based on actigraphy data thereby can increase the health care rate of children.
The deep neural network is used for the prediction of sleep time and quality. It uses your sleep data exported from the Sleep Cycle app to train to make predictions. It gives you an estimation of how long you'll sleep when you will wake up and what your sleep quality is going to be.
This will help to take predictive and safety measures in the healthcare of the children.
Schizophrenia is a chronic which can leave imprints lifelong that frequently disable mental disorder and affects children and during adolescence.
However, the deep learning approach that uses machine learning and AI to examine millions of cortical links in the brain can identify schizophrenia with a high rate of accuracy and also predict schizophrenia symptom severity.
Many researches and medical professionals believe that they believe these technologies could help to pin down neuroimaging biomarkers for psychiatric conditions and help to overcome problems that are generalized across a large population. These adaptations are one of the essential uses of deep learning and will not be a necessary factor in diagnosing patients, but in predicting symptom severity, however, this technical approach could take up to at 5 to 10 years to be in practice
Currently, top research centers across the world and healthcare systems are developing deep learning techniques that can predict and prevent disease, hospitalizations and discover which genes are associated with future diseases and disorders.
By 2012 deep learning had beaten human-coded software and achieved
“superhuman” levels of perception.
Deep learning is a revolutionizing wide scientific field which helps to analyze medical data to treat diseases and analyze medical images. These technologies have been around for decades, new advancements have ignited to boom in deep learning especially in the children healthcare sector thereby it’s an advanced future of personalized and revolutionized medical innovations.
These examples are just a handful, there are thousands and thousands of unimaginable implementations yet to be discovered.