Applications Of Machine Learning In Healthcare
Technology is an important part of the world today. It requires fuel for it to run. One such fuel i.e. the branch of technology is Artificial intelligence.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In simple words it is a field of study where we teach computers how to learn. It's the same as how we teach kids the alphabet and the basics. AI has many branches and Machine Learning is one of them. Machine Learning provides the systems the ability to automatically learn and improve from experience without being explicitly programmed. You can even say that it is a way of designing codes or programs that teach computers stuff over time by interacting with the environment or inspecting it. Machine learning has many applications such as: self-driving cars, traffic prediction (used in google maps), healthcare, speech recognition (used in google assistant), etc.
The power of automatic learning and programmed designing combined with the massive amount of data has immensely contributed to the successful application of machine learning in the healthcare sector. The two most impactful areas are drugs and vaccine discovery. In this the machine learning offers: compound property prediction, activity prediction, reaction prediction and ligand-protein interaction. The current example for this is the role of AI and ML for the discovery of COVID-19 vaccine. Back in 2018 i.e. 3 years ago it was difficult for doctors to predict or detect the symptoms of sepsis (A life threatening complication of an infection) but people like SUCHIA SARIA (CS and health policy professor) discovered the TREWS (Targeted Real-time Early Warning Score) with the help of AI and ML to help detect the sepsis caused in patient easily. ML has an impactful and positive effect in the healthcare sector, it is considered as a hot topic there. ML has been described as technology to replace doctors, a digital way to read images or process patient’s data, predicting the likelihood of diseases and even suggesting the treatment and medicines. But even you may arise with a question now that: Will ML really streamline the evolution of healthcare? How does it work? And my answer to your questions is yes, ML will streamline the evolution of healthcare in the coming decade. Any researcher or a computer scientist having knowledge about programming can club with a biologist for the further discovery in the healthcare sector. If I talk about today then ML isn’t used to detect cancer or to treat prevalent diseases, but it is used instead to make valuable predictions, like length of stay of a particular disease, restudying risk of infection, etc. Hence by proper setting expectations, it’s easier to understand how to implement the technology.
Machine learning is used in heart disease diagnosis. An automated heart disease diagnosis system is one of the most remarkable benefits of machine learning in healthcare. Machine learning algorithms like SUPPORT VECTOR MACHINE (SVM) or Naïve Bayes to use as a learning algorithm for heart disease detection. ML is also used in detection of diabetes at an early stage to save the patient. The Naïve Bayes outperforms other algorithms here as it takes less computation time and performs excellently. Along with the above mentioned applications of ML in healthcare few others are: prediction of liver diseases using Indian Liver Patient Dataset (ILPD), robotic surgery (it is one of the impactful applications of ML in healthcare.), smart electronic health record is another convenient way to keep track on an individual’s health, radiology and many more.
Nowadays, ML is part and parcel of our everyday life. But right now though the AI and ML is advancing rapidly in other sectors it has a long way to go in the healthcare sector as there is a lot of medical complexity and scarcity about the data. Be it about an individual or the disease and the infections around. Machine learning in healthcare is composed of two domains: computer science and medical science, the blooming of ML in the healthcare sector is surely possible in the coming decade if the computer scientists and the medical scientists work hand in hand as a single thread. Hence we have to keep on giving constant efforts for the further development of ML in the healthcare sector as it would be beneficial for the world as a whole.