It is now out there clean and clear that machine learning provides better result with more volume of data and with every passing minute there is more data, so in so universe it is like wine getting better with time. One sector that has great grapes - let's just stop with this analogy. One sector that has data in abundance like crazy data since forever is the healthcare industries. Professionals had to keep the records of the patients maybe not in computers (didn't have it like back then) but in ledgers or books.
According to McKinsey, has estimated that the big data and machine learning in pharma and medicine could generate a value of up to $100b annually (that's exactly the numbers of zeros that makes you feel how poor you are). How all of this is possible? Well like every big thing that works with a few add-ons, in this case better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.
As talked earlier there is plenty of data in this industry, but it is scattered across different departments like research and development, different clinics, with various physicians, even data with patients and the people the caretakers of some patients too. Due to this large volume of data being spread across various people among various departments of various spaces, there is an issue in coordinating all this information to use it for the improvement of the healthcare infrastructure and treatments. In today's time the main issue at the crossroads of machine learning and healthcare is how to collect data in a way it is effective at its best and use these various data types for better analysis, prevention and treatment of individuals.
The blooming applications of machine learning in medicine and pharma show flashes of a future which has the potential of having data, analysis and innovation all synchronized and could be an everyday reality.
This post is about such applications and help spread awareness among peoples into the areas for continued innovation.
1. Disease Identification/Diagnosis
when it comes to machine learning research in medicine the lime light sure falls on disease identification and diagnosis of disease. The organisation Pharmaceutical Research and Manufactures of America issued a report in the year 2015 that stated that "there are more than 800 medicines and vaccines in there trial process which can treat cancer." "That is where the idea of a biologist working with information scientists and computationalists is so important," said Jeff Tyner when he talked about the challenge of finding ways to work with all the resulting data. Talking about cancer identification and treatment the first ones to sail this ship were the big players of the industry or sector. In October 2016, IBM Watson Health announced IBM Watson Genomics; this was in partnership with Quest Diagnostics. The main aim of this collaboration was to take steps in precision medicine by merging cognitive computing and genomic tumour sequencing. There are many other examples of companies working on multiple areas of this issue.
2. Personalized Treatment
another area which is red in research area and is closely related to assessing disease better is personalized medicine or in laymen's term a better treatment provided on the basis of an individual's health data paired with predictive analytics. At the moment the area is dominated by supervised learning which gives the physicians a select set of diagnoses to choose from. At the moment the prominence that is driving the car of change in treatment decision is IBM Watson Oncology, which is using medical information provided and the history o the individual to make perfect selection from the treatment option. In the next coming years there is a better possibility that healthcare industry will be using micro biosensors and devices that will provide with more sophisticated health measurement and movable monitoring capabilities. This will lead to another flood of data that can be of great help to the research and development department and with effectiveness of treatments. This could also lead to a reduction in healthcare costs.
3. Discovery of new drugs
At this moment the use of machine learning in initial stages of drug development/ discovery has the promise of many uses. Right from the initial screening of drug compounds to predicted success rated based on some biological factors, which includes the R&D technologies like next generation sequencing.MIT clinical Machine Learning Group is working on precision medicine research by developing an algorithm that can understand the process of disease and provide a structure for better treatment of a disease.
4. Clinical trial research
This topic has a lot to benefit from machine learning, with various application that can help shape and direct clinical trial research. Applying advanced predictive analytics in identifying candidates for clinical trials could draw on a much wider range of data than at present, including social media and doctor visits, machine learning has other uses to like monitoring on the go and access of data at anytime with rise in safety. According to McKinsey, there are many other ML applications for helping increase clinical trial efficiency, including finding best sample sizes for increased efficiency; addressing and adapting to differences in sites for patient recruitment; and using electronic medical records to reduce data errors.
5. Radiology and Radiotherapy
Google's DeepMind Health and University College London Hospital are already working together to develop machine learning algorithms capable of detecting differences in healthy and cancerous tissues to help improve radiation treatments. This could help pace up the segmentation process and increase accuracy in radiotherapy planning.
6. Epidemic Outbreak Prediction
ML and AI technologies are also being applied to monitoring and predicting epidemic outbreaks around the world, based on data collected from satellites, historical information on the web, real-time social media updates, and other sources. Support vector machines and artificial neural networks have been used, for example, to predict malaria outbreaks, taking into account data such as temperature, average monthly rainfall, total number of positive cases, and other data points. Predicting outbreak severity is particularly pressing in third-world countries, which often lack medical infrastructure, educational avenues, and access to treatments.
7. Smart Electronic Health Records
Document classification using support vector machines, and optical character recognition are both essential ML-based technologies in helping advance the collection and digitization of electronic health information. MATLAB's ML handwriting recognition technologies and Google's Cloud Vision API for optical character recognition are just two examples of innovations in this area.