The Quantified Self is a movement to incorporate technology into data acquisition on aspects of a person’s daily life in terms of inputs (e.g. food consumed, quality of surrounding air), states (e.g. mood, arousal, blood oxygen levels), and performance (mental and physical). Such self-monitoring and self-sensing, which combines wearable sensors (EEG, ECG, video, etc.) and wearable computing, is also known as lifelogging. The primary methodology of self-quantification is data collection, followed by visualization, cross-referencing, and discovery of correlations. If we add genetic data to all the social and personal medical sensors data then the dream of data driven personalized medicine in healthcare is not too far.
Personalized medicine or PM is a medical model that proposes the customization of healthcare – with medical decisions, practices, and/or products being tailored to the individual patient. Traditional clinical diagnosis and management focuses on the individual patient’s clinical signs and symptoms, medical and family history, data from laboratory and imaging evaluation to diagnose and treat illnesses. This is often a reactive approach to treatment, i.e., treatment/medication starts after the signs and symptoms appear. A proactive approach using big data technologies and application of predictive analytics can help us take effective preventive measures.
Life and health tracking information from various applications can be difficult to integrate and find meaning. But innovative and readily available open source technologies have delivered efficient tools in the hands of data scientists. Now we can combine, visualize, and analyze the cacophony data to identify correlations between specific input factors and treatments (independent variables) and psychological or physiological outcomes (dependent variables).
Today, creative data scientists can arrive at amazing conclusions about how various factors correlate with one another and affect a person’s psychology or physiology. We can combine this insight and apply predictive algorithms to build models, tools to predict how a person would respond to a device, situation, treatment or medicine. Open source innovators behind path breaking frameworks such as Hadoop, Hive, MapReduce, Yarn, KNIME, WEKA and Apache Mahout have built a set of components that can help us achieve the goals of personalized medicine.
Learning all these technologies, building the analytical tool from scratch and then running data analysis is a set of daunting tasks. The team at Big Data Cognition is helping data scientists face this challenge. We have integrated all these technologies together to build a single platform, Saarus. Using Saarus,data scientist can focus on creative use of data without wasting precious time and effort on the technology plumbing.
Saarus, as is the case with all it’s component technologies, remains open source and is designed to make life easier for data scientists. It helps data scientists harness the capabilities of all these technologies and provide a cost-effective graphical analytic work bench. Big Data Cognition is committed to work on integration of all promising statistical algorithms and technologies to help healthcare data scientists achieve the goal of personalized medicine and preventive care.