Asociación para el Desarrollo de la Ingeniería del Conocimiento
Instituto de Ingeniería del Conocimiento (ADIC-IIC)
Instituto de Ingeniería del Conocimiento (IIC) is a private R&D+i institution. It is a ground-breaking innovation centre on Artificial Intelligence, expert on Data Analysis and Big Data Technologies since 1989. It was founded by Asociación para el Desarrollo de la Ingeniería del Conocimiento (ADIC), a non-profit entity under the sponsorship of the Spanish Ministerio de Industria (Ministry of Industry). IIC core and experience along the years revolved around the Data Analysis. The current members of ADIC are IBM Spain, Gas Natural Fenosa, Santander Group and Universidad Autónoma de Madrid.
After a 25-year experience on the market, we are offering a set of quality tools and also offer customized solutions and services using the most advanced analytical techniques in Big Data, Algorithm Engineering, Artificial Intelligence and Automated Learning to turn data from companies into value and thus optimize, redeem and make decision making processes more dynamic.
We work closely with our clients during the whole process: from the identification of opportunities to innovate, the continuous improvement of the developed systems and the transfer of technology through successful implementations, up to the adjustment of the technological underlying research to the problem that need to be solved.
Also, IIC projects allow our clients to obtain valuable information out of data, optimise their business processes, achieve accurate results and save costs and time, making the investment more profitable.
The team specialised in Big Data in Healthcare centres its activity in the Healthcare and Eldercare Environments to transform the data from the basic clinical research into knowledge to prevent illnesses, and improve patient care (diagnosis and treatment) and healthcare management. Analysing thoroughly all that information so as to help healthcare professionals is a real challenge.
Big Data in Healthcare
In the Healthcare Sector there are many heterogeneous data sources that contain a high volume of information related to patients, illnesses and healthcare centres. If all this information was thoroughly analysed, it would be very useful for healthcare professionals.
Data about healthcare may be obtained from electronic medical histories, telemedicine devices, clinical tests, or even from wearables. Likewise, it would be possible to get valuable information from epidemiological, nutritional and genomic data, more closely related to Real World Data and Personalized Medicine.
Applying Big Data techniques in Healthcare helps revealing an intelligent layer under the data and using predictive models in order to anticipate healthcare needs and offer a more effective medical attention.
At Instituto del Ingeniería del Conocimiento we apply our own techniques to analyse data and optimize both healthcare management and patient care.
Solutions and services
Intelligent Alerts Systems. Neurodegeneration
The Social Network Analysis reports integrated in our intelligent alerts systems DIGNA and ADMIRE provide useful information to support medical specialists in developing protocols, early diagnosis, prognosis of the evolution of diseases, and planning of treatments for their patients.
Chronic Patients Segmentation. Prediction of Needs
We develop systems to stratify population into different risk categories for early detection of chronic patients that would not need to be referred to medical specialists, emergencies or to a hospital if they were proactively controlled at Primary Care Centres. Categorising people according to risks allows healthcare centres to predict hospital readmissions, healthcare costs, etc.
Analysis of Frequent Attenders and Hyper-prescription
It is necessary to obtain behaviour patterns from patients to determine the profiles that actually correspond to frequent attenders or cases of hyper-prescription. This helps developing projects to detect frequent attenders in Primary Care Centres from a specific Healthcare area as well as projects to detect cases of hyper-prescription in Specialised Healthcare Services in order to make improvements to a process.
Optimization of Resources for Care Services
We analyse the factors related to use and demand of different care services in order to segment population in groups sharing social and healthcare characteristics and specific risk levels. This allows providing the most suitable services and resources according to the real needs from patients.
Sepsis Early Detection
Sepsis early detection within a hospital reduces the amount of false positives. We developed an intelligent alerts system applying Big Data techniques to the data gathered by the specialists in sepsis. This helped detecting real cases of sepsis and false positives early, reducing human losses and economic costs.
Prediction of Induction Success of Labour
A predictive model for induction success of labour helps obstetricians to make decisions. An intelligent alerts system was built applying Big Data techniques and the rules from a panel of experts on the field and using anonymized information about patients from their electronic medical records. The system was developed to support specialists to decide on the success of induction labour. Identifying those patients with a high probability of failure allowed offering a more personalised patient care, achieving better results and reducing costs.
Collaborations and associations
We have collaborated with key institutions of the medical technology area, such as ITEMAS
Network or FENIN and major research institutions such as Fundación CIEN (Research Centre of Neurological Diseases), Fundación Reina Sofía, Universidad Rey Juan Carlos, Fundación DIM (for the Development of Advanced Medical Images), Instituto de Salud Carlos III, or UCLA (University of California, Los Angeles).