Developing Novel Criteria to Classify ARDS Severity using a Machine Learning Approach

Identificador:  TDX:4124

Handle: https://hdl.handle.net/20.500.11797/TDX4124

Autores:  Abdelall, Mohammed Gamal Sayed

Resumen:
In front of the medical difficulties to properly address ARDS issues, as they are reported in multiple specialized publications, in this thesis we hypothesized that the use of modern machine learning (ML) technologies could improve our knowledge and our capacity to predict and address these ARDS issues. In order to achieve these objectives (i) we proposed a novel formula [PaO2/(FiO2xPEEP) or P/FPE] for PEEP≥5 and corresponding cut-off values to address Berlin’s definition gap for ARDS severity by using ML approaches. We examined P/FPE values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO2/FiO2 criteria with P/FPE under three different scenarios, (ii) we aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using ML approaches, and (iii) we validated P/FPE as a predictor of ICU mortality beyond the current state of the art using intuitive classification thresholds based on ML.