Today everyone heard the words Artificial Intelligence, Data Science, Machine Learning, Neural Networks, Predictive modeling. Where can this be used? How? What does this mean in all these worlds? So let’s try to answer these questions.
Data Science is one of the newest sciences which formed in the last 20-30 years. Data Science based on Applied mathematics and information technologies. This is a mix of tools, methods, algorithms, and disciplines focused on understanding data for managing real-life objects or information systems for different purposes. Data Science includes Mathematical analysis, Differential Equations, Theory of Probability, Functional analysis, Computer vision, Machine Learning, Neural Networks, Predictive modeling, Optimization methods, and other algorithms, approaches, and theories.
Computer systems that use AI-based algorithms capable to solve a wide list of tasks like texts translation, Natural Language Processing — speech understanding and conversion voice to text (or vise versa), production process management, equipment monitoring and maintenance prediction, analysis of patient’s data and predicting the need for surgeries, medicines, hospitalizations, care pathway optimization, and many other applications.
Engineers at Softarex Technologies have vast knowledge and expertise in the implementation of AI, Machine Learning, Neural Networks, Predictive modeling algorithms into new or existing systems for different industries.
The development of any system with AI capabilities starts from the domain research where it will be applied.
After we define the exact path of how AI will be implemented, what data will be used for training data sets, the expected outcome, and what algorithms for data analysis will be used.
When this analysis and design stage is finished, we move to the implementation of the system with technologies and algorithms.
The most difficult part during the creation of new medication is the evaluation and review of a clinical trial by authorities for medication approval for going to market.
During this step of review, the pharma companies lose on average €1M/day.
Reducing these costs, streaming and formalizing data submission, clinical trials review and approval are big tasks for national certifications organizations. This was followed by enhanced benefit-risk guidance for clinical assessors and by the EMA’s Benefit-Risk Methodology Project, which had as its main objective to develop and test tools and processes for balancing multiple benefits and risks.
Four tools have been proposed within the scope of this project: a generic decision-making approach entitled the PrOACT-URL Framework, an Effects Table summary of benefit and risk outcomes, MultiCriteria Decision Analysis (MCDA) modeling, and graphical displays.
The European Union Clinical Trials Register allows searching for protocol and results in information on:
The EU Clinical Trials Register currently displays 35002 clinical trials with a EudraCT protocol, of which 5706 are clinical trials conducted with subjects less than 18 years old.
The register also displays information on 18700 older pediatric trials (in the scope of Article 45 of the Paediatric Regulation (EC) No 1901/2006).
Clinical Trials Review and Approval Process. BfArM (German competent authority) submission is, in general, a two-step approach.
The scheme below shows a general process for trial review and approval.
Based on the domain’s analysis and task definition we decided that this project must be designed in the application of appropriate AI-based clinical trials analysis functions. During development of this application it should be developed particularly following steps (only few steps provided):
At this stage, it needs to create a workflow for the AI system. Typically this workflow starts from uploading data that needs to be tested/checked and then using trained neural networks these data evaluated and results provided. This is typically done on the server-side of cloud solutions hosted in AWS or Azure cloud. Here we’ve started implementing a designed system using Python or Java in AWS or Azure clouds. Firstly we developed a system prototype for proving that designed models and algorithms are selected properly. After this we grew the system step by step for adding necessary features and improving models, training new models, expanding data sets.