Applications of Machine Learning in Pharma and Medicine
They call the Healthcare sector a data goldmine that contains data from research & development (R&D), physicians and clinics, patients, caregivers, etc. It is evaluated that huge data and machine learning (AI- Artificial Intelligence) in pharma and medicine can generate the value up to $100B annually. Machine learning can help in better decision making or better results, optimize innovation, make research and clinical trials efficient, create new tools for physicians, consumers, insurers, and regulators.
Machine Learning Applications in Pharma & Medicine:
It is the first line where AI is used in the form of machine learning to identify and diagnose disease. There was a report issued by PRMA (Pharmaceutical Research and Manufacturers of America) in 2015, telling that more than 800 medicines and vaccines for treating diseases like cancer are stuck in the trial. Some big companies using AI or machine learning in their specific fields are:
- IBM Watson Genomics- Integrating cognitive computing and genomic tumor sequencing
- Berg (Biopharma Company)- Oncology and multiple areas
- (Google’s) DeepMind Health- Macular degeneration in aging eyes
- Oxford’s P1vital- Predicting Response to Depression Treatment (PReDicT)
The treatment is based on personalized medicine for an individual, based on that individual’s health data and it is more effective than normal medication. Some start-ups, as well as a leading institution like IBM Watson (Oncology), are using this application. The new start-ups or entrepreneurs are:
- Somatix- ML-based app usually helps in smoking cessation
- SkinVision- Skin cancer risk app (First and the only CE certified online assessment)
Drug Discovery / Manufacturing
The ML in prelim drug discovery stages can help in the initial screening of drug compounds resulting in a better success rate. It also helps in R&D like next-generation sequencing.
This ML technology is used by leading projects like Microsoft’s Project Hanover to develop AI technology for precise cancer treatment and MIT Clinical Machine learning Group, for effective treatment of diseases like Type-2 Diabetes.
Clinical Trial Research
Machine Learning can act as a superior application in the case of clinical trials by predicting the category and identifying the suitable candidates for the clinical trials based on a wide range of data as compared to the data in the present scenario. ML can make sure of the safety and health of the participants without disturbing their routine life can make the results better in the trials.
Radiology and Radiotherapy
This application can increase accuracy and speed up the segmentation process without disturbing or damaging healthy structures in radiotherapy. Google DeepMind and UCLH (University College London Hospital) are working together for cancer treatment through radiotherapy by developing machine learning algorithms.
Smart Electronic Health Records
The ML-based technology to classify the document with the help of support vector machines and optical character recognition can advance the collection and digitization of electronic health information.
The MIT Clinical Machine Learning Group is leading in the development of creating next-gen intelligent electronic health records.
Machine learning and AI technologies are used today to monitor and predict epidemic outbreaks, some of them are Opioid epidemic, Malaria outbreak by the data depending on temperature, average rainfall, number of cases, etc.
Obstacles in Machine Learning:
- Data governance– It is an obstacle as the medical data is personal and cannot be accessed easily. Therefore, it is essential to keep data privacy concerns in mind.
- Transparent algorithm– To cope up with the stringent rules and regulations on drug development, it is required to have more transparent algorithms to make the application of ML technologies in the Healthcare System, seamless.
- Recruiting data Science talent– The need for people who are skilled and proficient in data science is increasing in the pharma industry and should be addressed on a priority basis.
- Breaking Down “Data Silos”– The encouragement of data-centric view across various pharmaceutical sectors is of prime importance as opposed to the conventional behavior which lacked initiatives and support in the research area.
- Streamlining electronic records– Properly organizing the healthcare database at a single platform is very important to speed up the treatment procedures.