Big data is growing at an exponential rate and in the next few years genomic data is believed to become the biggest data type produced – likely exceeding data from other big data producers such as twitter, youtube and astronomical data by the year 2025. In the last decade, the amount of genomic data has increased in unparalleled amounts, with the total volume of sequence data doubling every seven months!
The opportunities that big data holds are extensive. With AI and machine learning technologies developing continuously, this data offers huge opportunities in the healthcare field, finding new innovations and discoveries in research, drug development, disease prevention, diagnostics and patient care.
Here are just a few examples of how AI and machine learning can contribute to the enhancement of human health:
- AI empowered consumer goods such as wearable technologies (for instance smart watches), coupled with intelligent applications, can help individuals improve their health on a daily basis, by guiding them to better lifestyle choices
- Advancing clinical and diagnostics tools such as clinical decision support systems, helping professionals to diagnose and treat their patients faster and more efficiently
- AI and ML research has led and keeps on leading to the development of impressive algorithms capable of detecting diseases in early stages, such as types of cancer, accelerating research for better treatments
- Helping the drug discovery process, by either improving the automation of otherwise slower operations, or making it more efficient and less prone to human errors
- Lowering the cost of new drug development by providing more accurate data to support decision making
But with all these advances and opportunities in mind, we have to be aware that the results highly depend on the quality of the data used. In order to get high quality results, the data you work with needs to be high quality to begin with.
At the moment, big data is being gathered from multiple different sources, usually lacking mutual standards and rules (for example, one doctor writes in her notes M and another doctor uses Male) and this is a significant barrier for the efficient usage of this data.
To enable the use of this heterogeneous data, data scientists spend vast amounts of their time (almost 80 % according to a recent study) in cleaning, harmonizing and curating these datasets. In addition to this task being really time consuming, this manual work is prone to human error, which is always a liability, potentially jeopardizing the results.
No AI or machine learning software can fully reach its potential if the data is inconsistent to begin with. In order to minimize erroneous results, all data used in research needs to be of the highest quality possible – regardless of whether it will later be manually processed or used by an AI or ML system.
MediSapiens has created a solution that tackles these problems with data that is too dirty and inconsistent for the use of AI – Accurate™. Accurate™ is the only software solution in the world that allows you to curate and harmonize your heterogeneous datasets in a user-friendly, easy to use, web-based application. The solution cuts away 80 % of the time used on curation and ontology mapping processes and enables you to use the highest quality data possible for your further analysis, both now and in the future – be that manual analysis or powered by AI.
Do you want to reach the full potential of your data so that your tools take the biggest advantage of it?
Book a demo of our solutions now or contact us to talk more!