The use of AI techniques and Machine Learning (ML) in biomedical and healthcare fields holds much promise, with the deployment of predictive analytics increasingly leading to precision medicine advancements. However, there are several key challenges including the inability and difficulties in using raw data from multiple sources; inconsistency in data standards and annotations; selection of tools for a particular problem; and dealing with datasets that are too big, too fast, or too complicated to handle with traditional systems. Traditional ML technologies are incompatible with biomedical raw data formats, and few, if any, gold standard protocols exist for data standardization, normalization, and harmonization.
To address these challenges, BioSymetrics has created a customized and flexible biomedical AI ML processing and modeling environment. Our scalable platform technology provides data exploration and ML algorithms, and generates real-time updates for massive datasets.
AugustaTM provides rapid analytics with unlimited inputs and real-time predictive modeling for use cases such as:
- Bioinformatics, health informatics, and computational biology using:
- MRI/fMRI and other imaging modalities
- Wearables data
- EHR/EMR data
- Real Time Integrated Analytics: For example, combining genomics with clinical and patient monitoring data from your mobile device for personalized medicine applications.
- Small molecule activity prediction
- Patient care quality and program analysis
- Drug discovery and development analysis
- Hospital telemetry and operational data
IoT in Biomedicine
Gartner reports that by 2020 the Internet of Things (IoT) will consist of more than 25 billion devices producing exabytes of data. These connected devices unleash immense troves of data, yet organizations are struggling to extract actionable insights from these expanding data pools. Predictive analytics can be leveraged in IoT to explore these data type varieties and large scale datasets.
AugustaTM is uniquely suited for Internet of Things data due to its predictive analytics capabilities. Use cases include wearables data exploration and analytics; personalized medicine and advanced pattern analysis; and smarter, connected healthcare and disease monitoring utility.