Proposal for a predictive model for healthcare based on big data analytics and technology transfer functionalities.

Anticipating the prognosis of diseases is essential for clinical decision-making. The objective of this research is to develop a predictive model using big data analytics and technology transfer functionalities to harness the potential of technology transfer processes and predict real patient data. Based on a systematic literature review, the Predictive Model for Big Data and Technology Transfer (PMBDTT) was proposed, incorporating big data analysis and technology transfer functionalities. Data are captured in hospitals, processed, and subjected to analysis using supervised learning algorithms to identify patterns related to the prognosis of hospital readmission – when patients can return after receiving treatment. The discovery of new knowledge and, as a final step, technology transfer and decision-making, represent probabilistic measures known as clinical referral rates. After developing the proposed predictive model, it can be applied to real patient data, selected using specific criteria, and its results can be utilised to improve the quality of care and health outcomes for patients. [ABSTRACT FROM AUTHOR]