Health Analytics
Health Analytics is a platform that combines predefined machine learning models with data analysis for researchers to develop their own models. By using the platform worldwide researchers can contribute to the progress of the system.
- Effective & secure healthcare solutions
- Better outcomes with predictive analytics
- Reaching analysis directly from a platform without data sharing
- Difficulties in handling large numbers of live health data
- Obtaining clean and validated data
Tiga Health Analytics solution includes Tiga patented technologies which provide tools for effective epidemiological researchers. The platform both includes predefined Machine Learning models, as well as FaaS (Function as a Service) for researchers to develop their own models. By this way, as long as the data is collected continuously in the system, worldwide researchers will be able to contribute to the progress of the system.
After implementation of Exchange-Hub, it will be possible to collect and record large amounts of data that will occur within the framework of health activities accurately and reliably. As the volume of data increases, meaningful information can be generated by analyzing these data and scientific studies that will pave the way for breakthroughs in the health field become possible.
Data scientists face several basic problems in conducting their research. Most important problem is the difficulty in accessing data. Particularly, there are great difficulties in obtaining clean and validated data. The other problem is the complexity of the tools and infrastructures that should be used in data analysis. On the one hand, while learning the programming languages to be used in research, on the other hand, it is a serious problem to install and configure the necessary software and hardware tools for the processing of data on a large scale.
Nowadays, billions of records in operational systems have begun to be formed in the operational systems with the problem of data collection. The processing of this data requires the use of scalable systems both in data storage and processing, rather than in standard systems. The creation of these systems exceeds the expertise of those involved in data science and creates serious barriers to working with big data. Health Analytics is a very special data analysis and machine learning platform that is developed in order to minimize all these problems.
With Health Analytics, data scientists can use the time they spend in accessing and cleaning data for modeling, and more inferences can be obtained from the data. In addition, it is now sufficient to learn only the programming language and the necessary libraries without having to learn all the tools. Data scientists will be able to sustain the models they create and their gains will not be lost.
Health Analytics' infrastructure systems will be able to scale automatically as the workforce increases, thus serving more and more users. In addition, data security is one of the most important components of health and this infrastructure provides solutions to the data security concerns by providing authorization to access data according to the research topic and needs.
Innovations
- Scalable platform
- Ease of use of tools and infrastructures that used in data analysis
- Worldwide researchers can contribute to the progress of the system
- ML on health data
Benefits
Benefits for Researchers
- Data scientists can sustain the models they create on the platform and their gains will not be lost after the publication of the articles
- Data scientists can use the time they spend in accessing and cleaning data for modeling
- Providing solutions to the data security concerns by providing authorization to access data according to the research topic and needs
- Allowing working / analysis directly from the platform without data sharing
- Reliable and collaborative platform
Benefits for Healthcare Professionals & Managers, Health System and Regulatory Organizations
- Helping family doctors diagnose and treat rural and small cities
- Ensuring continuous monitoring of quality and efficiency and control with recommendations for health service providers and managers
- Possibility to use the results by many stakeholders such as healthcare providers, pharmaceuticals and insurance companies
- Possibility to collect and record large amounts of data