Machine learning solution to clinical decision support for early detection of respiratory conditions within the ICU

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Intensive care unit (ICU) is a highly specialised environment concerned with the provision of critical care and treatment of life-threatening conditions. It consolidates a variety of stakeholders ranging from nurses and doctors to consultants - each requiring a different scope and extent of patient data to do their job. This creates a problem, as variety of the systems need to be in place to facilitate the access to this information, making it difficult to aggregate all relevant data for inference in complex conditions.Acute respiratory distress syndrome (ARDS) is a life-threatening condition of the respiratory system that contributes to a large number of ICU admissions. With a challenging diagnosis process, ARDS is frequently diagnosed late or missed altogether, leading to a high mortality rate of over 40% and devastating outcomes for the survivors. Tools allowing for the early detection of ARDS are therefore critically needed within clinical practice.The growing ubiquity of electronic health records (EHR) and clinical information systems (CIS) within the hospitals renders the data-rich environment of the ICU an excellent opportunity for machine learning (ML) approaches to that problem. However, despite the promising results of that technique demonstrated in the early research, several barriers inherent to the characteristics of the ICU data prevent its widespread adoption in clinical setting.The first challenge that has to be overcome is the unification of data format discrepancies spanning different ICUs. These could be proprietary formats attributable to the vendor-specific software, but also differences stemming from the fact that CISs are highly configurable leading to divergence even between data logged by CISs that share the same provider. In addition to that, changes to how data is gathered can be made over time further complicating the problem.The second barrier relates to the variety of sources that store the data and the scopes of physiological parameters they measure. Data relevant to the patients' stay in the ICU is frequently very comprehensive, however it lacks the information of what happened before and after their stay, as that data is stored elsewhere. Facilitating the robust linkage between these datasets requires a first-hand knowledge of the underlying systems and has been previously researched. Furthermore, the data gathered in the critical care sector undergoes national clinical audits performed by Intensive Care National Audit & Research Centre (ICNARC) including the Case Mix Programme (CMP) which consolidates data from 99% of adult general critical care units in the UK.This research project aims to tackle the problem of early ARDS detection by employing novel data-linkage techniques to build a machine learning solution that has a clear pathway of implementation within the clinical setting. To achieve this goal, the initial efforts will focus on constructing a platform for robust extraction of data consolidated from local CISs, as well as the data compiled for the purpose of CMP audit. After that, the machine-learning approach will be taken to design and develop a clinical decision support tool for ARDS detection, incorporating the previously established data-linkage software. To that extent, both supervised and unsupervised ML techniques will be explored. The issues surrounding supervised learning such as the quality of labelling and ability to pinpoint the ground truth will also be thoroughly examined at the design stage. Finally, the effectiveness of the solution will be measured by testing it across several larger datasets that will be sourced from the ICNARC as well as other available sources including the MIMIC-III, eICU and HIC which share an outstanding track record of use for clinical research purposes.