More data almost always yields better results when it comes to the effectiveness of machine learning, and the healthcare sector is sitting on a data goldmine. As per the estimates by McKinsey, big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators. Let’s look at some of the applications of machine learning in pharma and medicine.
Personalized Treatment/Behavioral Modification
Personalized medicine, which is nothing but an effective treatment based on individual health data paired with predictive analytics is a currently hot research area. Supervised learning is presently ruling the domain, allowing physicians to select from more limited sets of diagnoses or estimate patient risk based on symptoms and genetic information.
Leading the change from the forefront is IBM Watson Oncology institution, using patient medical information and history to optimize the selection of treatment options. Increased use of micro biosensors and devices, as well as mobile apps with more sophisticated health-measurement and remote monitoring capabilities, will provide another deluge of data over the next decade that can be used to help facilitate R&D and treatment efficacy. This type of personalized treatment has important implications for the individual in terms of health optimization, but also for reducing overall healthcare costs. If more patients adhere to following prescribed medicine or treatment plans, for example, the decrease in health-care costs will trickle up and (hopefully) back down.
Behavioural modification is also an imperative cog in the prevention machine, a notion that Catalia Health’s Cory Kidd talked about in a December interview with TechEmergence. Plenty of start-ups popping up in the cancer identification, prevention, and treatment space too like Somatix which is a data-analytics B2B2C software platform company. Its ML-based app uses “recognition of hand-to-mouth gestures in order to help people better understand their behaviour and make life-affirming changes”, specifically in smoking cessation. Another example is SkinVision, the self-described “skin cancer risk app”. It claims to be the first and only CE certified online assessment.
That’s not where the vast possibilities of machine learning in pharma and medicines end. Keep watching this space to know more.