Technology at the service of health
We have witnessed, at least on social media, how natural disasters have struck in Turkey and Syria, sadly leaving tens of thousands dead. Additionally, we are just finishing the process in which the pandemic plunged us.
We could say that as we move forward in time, we are also facing more and more tragedies, natural and biological disasters. This makes it more than necessary that the data collected from all these events can be processed effectively to prepare us for a new global eventuality.
It is with this in mind that a new area of medicine and statistics has emerged that attempts to use available technology to optimize the process of data collection and analysis, thus giving rise to what has been called Digital Epidemiology. If you want to know what this is all about, I invite you to read on, because what I have to tell you will surely interest you.
This morning I was reviewing a PAHO article where they talked about the emergencies that are occurring globally, which include natural disasters and viral and bacterial outbreaks, and that these are constantly increasing, and I came across an interesting topic that I wanted to share with you, and this is digital epidemiology, How can we define it?.
"Digital epidemiology is a new field that has grown rapidly in recent years, driven by the increasing availability of data and computing power, as well as advances in data analysis methods." Source
This is logical to arise, because it is valid to make use of what we have at hand, of the scientific and technological advances that arise for the welfare of people, that is how the world has always evolved in the course of history.
Digital epidemiology is an emerging field that focuses on the use of digital data and information technologies to study public health and diseases in populations. Digital epidemiology uses data that are generated digitally, such as electronic health records, online search data, social media posts, and sensor data, to identify patterns and trends in population health and disease.
This area is something that can provide many advantages, Digital epidemiology can help health professionals identify disease outbreaks and epidemics more quickly, and can also help predict the spread of diseases and plan public health responses and strategies. It can also be useful for studying the social determinants of health and health inequalities.
In this way, from the point of view of health policy, states can use these data to establish better ways of addressing social problems in health so that they have greater coverage. At least this in theory.
In this case we are making use of Machine Learninig, which can be defined as a branch of artificial intelligence that focuses on the development of algorithms and models that can learn from data, without being explicitly programmed to do so. Instead of following a set of predefined rules, machine learning models use patterns and relationships in the data to make predictions and decisions.
The use of this technology in epidemiological data analysis makes it possible to analyze large amounts of digital health data and discover patterns and relationships that can be difficult to identify using traditional methods. Some uses that are being made of this technology are as follows:
Text analysis: Machine learning can be used to analyze large amounts of text, such as social media posts or medical records, to identify patterns of behavior or disease symptoms. For example, researchers can use sentiment analysis to assess people's opinions about the * *effectiveness of public health measures.
Outbreak prediction: Machine learning can be used to predict disease outbreaks in real time. Machine learning algorithms can analyze real-time data, such as online searches for symptoms and population mobility patterns, to identify geographic areas that could be at risk of a disease outbreak.
Image analysis: Machine learning can be used to analyze medical images, such as X-rays and CT scans, to detect patterns that may indicate disease. This can help doctors diagnose diseases faster and more accurately.
Disease modeling: Machine learning can be used to build disease models that can predict the course of the disease and its impact on the population. For example, models can predict how many people might contract a disease in a given population based on factors such as climate, population mobility and vaccination rates.
Much is being said, as against, of the application of this technology, especially regarding the privacy of the data, in view of the fact that in order to be able to analyze them it is important that they are all registered online, which exposes people and privacy can be compromised.
Despite the above, I must say that this is a clear example of what the application of technology means, of new advances in other areas of human knowledge, which can be summarized in a way to positively impact society, which is, as I understand, the purpose of every advance, or at least that is what it should be.
I would like to know if you were aware of this new trend or area of study, and the application of this area of artificial intelligence, such as Machine Learning in the area of health. Which I particularly find very interesting.
If you have anything to add, I invite you to leave it in the comments so that we can all benefit from it.
