We guide companies in the pharmaceutical and healthcare sector towards safer and more efficient operations, products and services with tailor-made data analysis solutions based on artificial intelligence.
We develop technological tools, supported by techniques such as machine learning, deep learning or data mining to bring them to biomedical research and clinical and administrative health care.
We offer the Data Science in Company service, which we carry out at the client's premises and which, depending on the needs, can be provided on a continuous basis or as part of a specific training period with the teams.
Boosting the R&D of new treatments
- Support for decision-making in the R&D of new treatments.
- Reduction of the failure rate in clinical trials.
- Ease of patient recruitment under specific conditions.
- Mobile monitoring of clinical trial participants.
- Faster and more accurate data analysis.
Improving the quality of patient care
- Identification of the characteristics of the disease.
- Prediction of the patient's response to treatments.
- Diagnostic support and prospective care guidance.
- Ad hoc treatment and dosage for each patient.
- Monitoring the progression of at-risk patients.
Improving the efficiency of health services
- Prevention of medical errors and reduction of readmissions.
- Refinement of operational processes.
- Optimisation of the workflow of professionals.
- Streamlining the processing and analysis of healthcare data.
- Increased efficiency in patient care.
AI to drive R&D for new treatments
Applying AI-based technologies makes it possible to reduce the failure rate of clinical trials and R&D costs in general, thereby optimising costs and driving the development of new treatments.
One of TAIREL’s goals is to develop methods of theoretical modelling, using machine learning, capable of identifying promising therapeutic targets, i.e. biological elements with which a drug could interact. This will help scientists make decisions about researching further treatments or about bringing existing treatments to other diseases.
AI also makes it possible to design clinical trials and analyse the data sources used for volunteer and patient selection more accurately and quickly. In later phases of the studies, AI can also be used to monitor participant compliance and progress through mobile health systems. This helps to shorten times for data processing and analysis for quicker results.
Improving patient care and increasing the efficiency of health services
At TAIrel Data we develop algorithms to identify diseases and their characteristics and predict the response of patients to treatments or protocols. We also research other technologies, based on AI, capable of identifying the optimal dose of medication required by a particular patient and providing recommendations to healthcare professionals.
In hospital settings, AI-based tools can help prevent medical errors and reduce hospital readmissions. These techniques also provide diagnostic support and prospective care guidance, among other clinical applications.
Health trackers and wearables (patient-integrated devices) provide real-time data to track patients at risk and monitor their evolution. In the administrative management of healthcare services, AI can be useful for optimising the workflow of professionals by eliminating redundancies and unnecessary procedures.
Discover the transformative potential of artificial intelligence to obtain precision medicine.
Our team at TAIrel Data offers a multidisciplinary perspective that combines scientific and technological background - computational techniques, mathematics, statistics and modelling - with a solid knowledge of research and business management in the health sector, with the ultimate goal of improving quality in patient care through precision medicine.
Industrial pharmacy specialist, CEO of TAIrel Data and project leader.
PhD in Mathematics and expert in Big Data and Business Intelligence.
José Antonio Moler
Mathematician and expert in clinical trial design under FDA and EMEA requirements.
TAIrel Data relies on a team of external advisors formed by Antonio Pineda, PhD in Chemistry and Deputy Director and Director of Translational Research at the Centre for Applied Medical Research (CIMA), Jesús López Fidalgo, Professor of Statistics and Director of the Institute of Data Science and Artificial Intelligence (DATAI), both from the University of Navarra, and Mariano Velasco, Software, Programming and Artificial Intelligence Engineer and IT Services Manager at the Public University of Navarra (UPNA).
Antonio Pineda-Lucena, PhD Doctor in Chemical Sciences and deputy director and director of translational research at the Center for Applied Medical Research (CIMA) of the University of Navarra.
Software, Programming and Artificial Intelligence engineer and IT services manager at the Public University of Navarra (UPNA), Mariano Velasco. He is also a Mentor (Xcout) in the Explorer program.
Artificial intelligence, applied in the field of healthcare
The use of data analysis to improve the decision-making process has become a differentiating element for the strategic positioning of companies, not least in the healthcare sector. The tools and processes used to perform this data analysis are known as Artificial Intelligence (AI).
AI refers to processes, performed by machines using machine learning algorithms and software, developed from information gathered from previous similar behaviour, that serve to “mimic” patterns of human cognition. Its application in healthcare facilitates the analysis, understanding and presentation of complex data, such as recognising differences between two patients’ responses to the same treatment.
These innovative processes accelerate the pace of research and development of new medicines, and increase precision in the prevention, diagnosis and treatment of diseases.
Applied to clinical management, they facilitate the management of patient information, helping medical professionals to make decisions and achieve greater efficiency. AI provides more accurate estimates of the evolution of diseases, allowing for more appropriate treatments while increasing users’ confidence.
Driven to offer an innovative service that contributes to the improvement of precision medicine, TAIrel Data aims to become a benchmark in the research of customised solutions, based on AI and other advanced techniques, to be implemented in the field of healthcare.
From researching new therapies, to bringing them to the patient, our goal is to boost the development of new treatments, improve the quality of patient care and increase the efficiency of healthcare services.
We manage your healthcare data with advanced techniques.
Find out more.
Machine Learning is one of the essential tools for carrying out data mining. Data scientists develop algorithms adapted to each field and need and capacitate machines with them so that they can process existing data. Based on their initial programming, machines are able to draw a pattern, which they use to reprogram themselves. And so on. In other words, through a kind of learning loop, computers search for patterns and draw conclusions from existing information, increasing the level of accuracy of the results as time progresses and the amount of data processed increases.
Data mining is a fundamental issue in the field of artificial intelligence and database research. As such, it refers to the process of extracting previously unknown and potentially valuable information from a large amount of data. It is an effective tool to support decision making and is based on the synthesis, identification and clustering of behavioural patterns, through which it is possible to learn, for example, the response of a group of patients to a particular treatment. Data mining is designed to obtain the keys to large amounts of data, while machine learning is used to programme machines so that they can refine the information they have available by means of "machine learning".
Deep Learning is a new branch of Machine Learning and is one of the most innovative. It is closer to human learning due to its functioning as neurons and is related to the discovery of neural networks, that allow to link more fields of analysis that were previously treated independently. The main difference between the two techniques is based on the type of algorithms required by each technique.
Big Data refers broadly to new technologies capable of processing large amounts of information quickly and efficiently. Unlike traditional computer hardware, Big Data is capable of aggregating and analysing diverse data and can extract information from any digital input, such as text, photographs and videos, as well as figures. All this has meant a qualitative change for companies, although it is not directly related to Artificial Intelligence, as it can be a purely statistical process. The integration of artificial intelligence into Big Data technologies is called data science. The essential difference lies in the fact that Big Data handles huge volumes of data to draw conclusions and decipher patterns of behaviour in the data, with the possibility of impacting decision making.