For centuries, medical drug data analytics, managed by the commercial research at pharma industries has been a greatly manual and boring task. But lately, distributive innovations such as artificial intelligence or machine learning have started infiltrating the medical field. Therefore, machine learning plays a crucial role in improving our health today. According to the survey by TechEmergence, it was disclosed that machine learning or artificial intelligence will be adopted on a broader scale by 2025.
Data is found easily in every healthcare industry in huge amount . Artificial intelligence algorithms work better when subjected to more data. Having said that, when it comes to the effectiveness of machine learning, more data almost and always yields better results. According to the famous management consulting firm, McKinsey; it was estimated that big data in machine learning in the pharma industry can generate a value of $100 Billion every year. This can be easily done through greater efficiency of clinical trials, optimized innovation, improved efficiency clinical tools and better insights into decision making.
The ability of artificial intelligence to spot patterns in massive volumes of data provides machine learning a wide range of application. Some of them are discussed below that will provide insight into areas for continued innovation.
Identification of diseases
Disease recognition and diagnosis of illness are at the foreground of machine learning research in medication. According to a research, it was found that over 800 medicines and vaccines to treat cancer were in trial. In October 2016, IBM Watson Health declared IBM Watson Genomics as the partner initiative of quest Diagnostics. Its goal was to make progress in the accuracy of medicine by integrating subjective computing and genomic tumor sequencing.
Machine learning programs take the entire knowledge that a physician has beside his experience in treating patients. Well, the idea behind artificial intelligence in the pharma industry is not to substitute doctors but to upgrade their medical expertise. Moreover, from the entire information related to diseases and its medication, the doctors have a generous amount of data available to them. This information is gained by them that they digest rapidly which becomes their vital skill. This is how machine learning enables them to learn from that data and put it into good practice.
Behavioral modification or personalized treatment is a more effective method of treatment which is a great analysis area and is closely related to better disease assessment. Usually, it is based on individual health data that is paired with predictive analytics. In coming years, the use of microdevices and biosensors will increase eventually. In order to provide the best treatment facility, mobile apps with more refined health measurements and remote displaying capabilities will be offered.
Drug efficiency detection
Today, identifying the most proficient biomarker is a quite difficult task because there is a huge amount of genomics, proteomics and metabolomics data around. The use of machine learning in early stages drug manufacturing is possible for numerous uses such as R&D discovery technologies. The uses are based on biological factors, from initial screening of drug compounds to the expected success rate. A great amount of data of the patient is being collected, but regrettably there are no tools in place to extract the required information from the data. But with the emergence of machine learning, it shall be possible to find novel targets with data mining.
Better patient care
Often it becomes difficult for the physician to keep an accurate record of test results and other metrics. They are supposed to manage a lot of data from a huge number of patients while making real-time decisions too. Therefore, it becomes very tiring for them to integrate and manage all this at the same time. But, machine learning systems can make real time predictions possible, as their main objective is to use data from medical records to predict actionable interventions and improve healthcare. There are various machine learning programs that could help the physician in an attempt to reduce stress and efforts.
Radiology & Radiotherapy
In October 2016, Dr. Ziad Obermeyer (an assistant professor at Harvard Medical School) stated in an interview that, in coming 20 years, radiologists might not be in their current status and instead they may look like robots who might be supervising algorithms reading thousands of studies per minute. Keeping this in mind, Google’s DeepMind health is working in order to develop machine learning algorithms. To help improving radiation treatments, these algorithms will be made capable of detecting differences in healthy and cancerous tissues. Therefore, in order to speed up the disunion process (ensuring that no healthy structures are damaged), DeepMind and UCLH (University College London Hospital) are working together to increase the accuracy in radiotherapy planning.
Epidemic outbreak prevention
Epidemic is caused by various factors such as the genetic change in the bacterium reservoir, the emergence of pathogens and change in a condition of the host population. This infection travels at the speed of light and infects a large number of people within a short time for two weeks or more. Sometimes, it spreads to other countries too, thus affecting a huge amount of people. Artificial Intelligence and Medical Epidemiology (AIME), has managed to give their users exact information regarding deadly diseases the three months ahead. This is done in order to aware people about these diseases in advance. This program also advises anti-dengue measures for the infected area within a 400-meter radius.
Smart electronic health records
There are two main machine learning technologies, classification of documents using vector machines and recognition of character recognition. These technologies help in collection and digitalization of electronic health information in advance. Examples of optical character recognition are Google's cloud vision API and MATLAB's ML handwriting recognition.
There are various uses of machine learning and its applications are applied in various fields to help in shaping and directing clinical trial research. Applying advanced prognostic analytics, draws on a much wider range of data than at present. Usually, the trial process of a clinical trial is the most dangerous and burdensome part of drug discovery. With the emergence of artificial intelligence in the medical industry, the opportunity to influence qualified data into a machine learning program has reduced patient risk exponentially. Machine learning builds a model using a platform with the patient’s own biology and monitors patient response on a biological level.
The challenges of using ML in pharma are by no means small either
Achieving data control
Ensuring transparent algorithms
Recruiting data science talent
Breaking down "data silos" (i.e. seeing the value in sharing and integrating data)
Streamlining electronic records
As of now, Machine learning will not be able to replace human intelligence completely, which in this case is a doctor, a researcher or a chemist. However, its power and capability to understand and collate clinical notes and unstructured data like numbers and dates in huge numbers will benefit the pharmaceutical and healthcare industries in every possible way.