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

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.

Impact on Health and Care
The CDT primarily addresses the most pressing needs of nations such as the UK - namely the growth of expenditure on long term health conditions. These conditions (e.g. diabetes, depression, arthritis) cost the NHS over £70Bn a year (~70% of its budget). As our populations continue to age these illnesses threaten the nation's health and its finances.

Digital technologies transforming our world - from transport to relationships, from entertainment to finance - and there is consensus that digital solutions will have a huge role to play in health and care. Through the CDT's emphasis on multidisciplinarity, teamwork, design and responsible innovation, it will produce future leaders positioned to seize that opportunity.

Impact on the Economy
The UK has Europe's 2nd largest medical technology industry and a hugely strong track record in health, technology and societal research. It is very well-placed to develop digital health and care solutions that meet the needs of society through the creation of new businesses.

Achieving economic impact is more than a matter of technology. The CDT has therefore been designed to ensure that its graduates are team players with deep understanding of health and social care systems, good design and the social context within which a new technology is introduced.

Many multinationals have been keen to engage the CDT (e.g. Microsoft, AstraZeneca, Lilly, Biogen, Arm, Huawei ) and part of the Director's role will be to position the UK as a destination for inwards investment in Digital Health. CDT partners collectively employ nearly 1,000,000 people worldwide and are easily in a position to create thousands of jobs in the UK.

The connection to CDT research will strongly benefit UK enterprises such as System C and Babylon, along with smaller companies such as Ayuda Heuristics and Evolyst.

Impact on the Public
When new technologies are proposed to collect and analyse highly personal health data, and are potentially involved in life or death decisions, it is vital that the public are given a voice. The team's experience is that listening to the public makes research better, however involving a full spectrum of the community in research also has benefits to those communities; it can be empowering, it can support the personal development of individuals within communities who may have little awareness of higher education and it can catalyse community groups to come together around key health and care issues.

Policy Makers
From the team's conversations with the senior leadership of the NHS, local leaders of health and social care transformation (see letters from NHS and Bristol City Council) and national reports, it is very apparent that digital solutions are seen as vital to the delivery of health and care. The research of the CDT can inform policy makers about the likely impact of new technology on future services.

Partner organisation Care & Repair will disseminate research findings around independent living and have a track record of translating academic research into changes in practice and policy.

Carers UK represent the role of informal carers, such as family members, in health and social care. They have a strong voice in policy development in the UK and are well-placed to disseminate the CDTs research to policy makers.

STEM Education
It has been shown that outreach for school age children around STEM topics can improve engagement in STEM topics at school. However female entry into STEM at University level remains dramatically lower than males; the reverse being true for health and life sciences. The CDT outreach leverages this fact to focus STEM outreach activities on digital health and care, which can encourage young women into computer science and impact on the next generation of women in higher education.

For academic impact see "Academic Beneficiaries" section.