Machine Learning

With the evolution of digital capacity, more and more data is produced and stored in the digital space. The amount of available digital data is growing by a mind-blowing speed, doubling every two year. In 2013, it encompassed 4.4 zettabytes, however by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes.

Usually, we make sense of the world around us with the help of rules and processes which build up a system. The world of Big Data is so huge that we will need artificial intelligence (AI) to be able to keep track of it.

Actually, AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high.

In this regard, our Aptuso team is ready for your success by providing solutions to the raised challenges:

  1. We develop an electronic medical record based on semantically clean knowledge representation techniques
  2. We deliver automated capture of clinical data, from the speech, natural language, or structured
    entry, in order to provide the data required to move forward
  3. We will give you computable representations of the literature. Both clinical and basic biology data should be structured and universally accessible for automated data analysis
  4. We plan to do automated diagnosis. Despite the passing of its era, it is still worth understanding because there are times it is useful
  5. We have automated decision support for providers who interact with patients episodically and need help in making decisions about the treatment trajectory
  6. We deliver systems for improving access to information and explanation for patients and systems to provide and document continuing education for physicians
  7. We make demonstrations of the cost-effectiveness of advanced information technology
  8. We are high-motivated to create new medical knowledge with machine-learning and/or data-mining techniques. Having established the data infrastructure for clinical data and biological data, there will be unprecedented opportunities for gaining new knowledge