Application of Machine Learning

Machine Learning Theory and Application Development

Machine learning is an application of artificial intelligence (AI) .It gives ability to systems to learn automatically and improve from experience without being explicitly programmed. The basic thesis of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range. Machine Learning can play aimportant role in large amount ofcritical applications, such asimage recognition, natural language processing, data mining and expert systems. The aim of ML is to understand the structure of data, so that it leads to make accurate predictions based on the properties of that data. Intelligent algorithms and rich data setsare two important things that are needed for the successful application of machine learning in healthcare.

While the healthcare sector is being transformed by the ability to record massive amounts of information about individual patients, the enormous volume of data being collected is impossible for human beings to analyse. Machine learning provides a way to automatically find patterns and reason about data, which enables healthcare professionals to move to personalised care known as precision medicine.

Machine Learning Healthcare Applications:

Diagnosis in Medical Imaging :

ML is a being appliedfor identifying and diagnosing diseases and other medical issues. ML also gives suggestions. While proceeding with “suggestions” given by machine learning in a diagnostic situation, a doctor’s judgement would be needed in order to factor for the specific context of the patient.

Scaled Up / Crowdsourced Medical Data Collection

In the today’s world of healthcare, Internet of Things (IoT) devices likeFitbit or there are many ways that can be used to collect vast amount of medical data for anonymous sources.ML is helping to make sense of all that data.

Drug Discovery

From next generation sequencing to applications in precision medicine, machine learning has various roles to play with drug discovery and development both now and in the future.

The initial screening of drugs in early stage and preliminary testing could utilize machine learning systems as could the methods used to predict a drugs success rate. Unsupervised learning is also being used within precision medicine to better understand disease mechanisms and, therefore, understand better treatment routes for these diseases.

Robotic Surgery

Robotic surgery is nothing new and machine learning technologies look to add to what is an already possible using robot for surgical procedures.

The benefits of robotic surgery by replacing human surgeons with robots allows to operate in tighter spaces, with finer detail, and drastically reducing the chances for human-based challenges such as trembling hands.

Main focus of MLin robotic surgery is around machine vision and is used to measure distances to a much higher degree of accuracy or to identify specific parts or organs within the body.

Radiation Treatment:

Understanding and being able to detect the differences in healthy and cancerous tissues and cells is key to then building the most appropriate treatment plans.This is especially true for radiation treatments such as radiotherapy, where the risk of damaging healthy cells is particularly high.


Treatment Personalization and Optimizing clinical Trials

Personalized medicines and treatments have been discussed and debated for many years now, however, with technological advances in healthcare devices, artificial intelligence and machine learning, such treatments could one day soon be available. machine learning technologies can be used to interpret the high volumes of patient data collected by IoT and healthcare devices and then use these interpretations to predict conditions or suggest treatments

Use of machine learning technology in clinical research as it leads to several benefits including identifying ideal candidate groups based on factors such as genetics. It would make clinical research trials that were not only smaller in size and, therefore, quicker and more efficient, but also much less expensive in both financial terms and with regards to clinical resources.