Machine learning for the detection of clinical deterioration on hospital wards

Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score (EWS) systems. Such systems often rely on routinely collected vital-sign data, such as the National Early Warning Score system that is currently deployed across the United Kingdom. In this thesis, we propose improved and generalisable risk assessment algorithms, that can continuously alert for deterioration preceding the composite outcome of unplanned intensive care unit admission, cardiac arrest, and mortality. We develop and validate three statistical and machine learning models using large-scale datasets from two independent centres: Oxford University Hospitals and Portsmouth NHS Hospitals within the HAVEN database. The retrieved datasets include patient demographics, vital signs, laboratory tests, and data of the occurrence of any adverse events. The first model is the Age- and Sex- specific Early Warning Score (ASEWS), which was derived from statistical distributions of vital signs. The work suggests that accounting for age-related vital-sign changes can more accurately detect deterioration in younger patients (16-45 years old). We also propose the Deep Early Warning Score (DEWS), which consists of an end-to-end attention-based deep learning architecture that processes vital-sign time-series data. The vital-sign data is initially modelled using Gaussian Process Regression. The results suggest that deep learning improves the detection of clinical outcomes by recognising complex patterns in the data. The final model is the information Fusion in a multi-modal Early Warning System (iFEWS), which incorporates additional information about the patient, such as results of laboratory tests or the first diagnosis assigned at admission. In this framework, representation learning and continued learning within a multi-modal system improved the performance of detecting deterioration. All of our proposed models achieved a better performance than the state-of-the-art clinical EWS systems across two independent testing sets. Given their high performance, clinical utility, and illustrated interpretability, our models can be easily deployed in clinical settings to supplement existing EWS systems since they use the same data streams. </p

Machine learning for clinical outcome prediction

Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the state-of-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research

Data pre-processing using neural processes for modelling personalised vital-sign time-series data

Clinical time-series data retrieved from electronic medical records are widely used to build predictive models of adverse events to support resource management. Such data is often sparse and irregularly-sampled, which makes it challenging to use many common machine learning methods. Missing values may be interpolated by carrying the last value forward, based on pre-specified physiological normality ranges, or through linear regression. Increasingly popular is the use of Gaussian process (GP) regression for performing imputation, and often re-sampling of time-series at regular intervals. However, the use of GPs can require extensive, and likely adhoc, investigation to determine model structure, such as an appropriate covariance function. This can be challenging for multivariate real-world clinical data, in which time-series variables exhibit different dynamics to one another. In this work, we construct generative models to estimate missing values in clinical time-series data using a neural latent variable model, known as a Neural Process (NP). The NP model employs a conditional prior distribution in the latent space to learn global uncertainty in the data by modelling variations at a local level. In contrast to conventional generative modeling, such as via a GP, this prior is not fixed and is itself learned during the training process. Thus, an NP model provides the flexibility to adapt to the dynamics of the available clinical data. We propose a variant of the NP framework for efficient modelling of the mutual information between the latent and input spaces, ensuring meaningful learned priors. Experiments using the MIMIC III dataset are used to demonstrate the effectiveness of the proposed approach as compared to conventional data interpolation methods

Deep interpretable early warning system for the detection of clinical deterioration

Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score systems. Early warning scores are simple to use yet they consider data as independent and identically distributed random variables. Deep learning applications are able to learn from sequential data, however they lack interpretability and are thus difficult to deploy in clinical settings. We propose the ‘Deep Early Warning System’ (DEWS), an interpretable end-to-end deep learning model that interpolates temporal data and predicts the probability of an adverse event, defined as the composite outcome of cardiac arrest, mortality or unplanned ICU admission. The model was developed and validated using routinely collected vital signs of patients admitted to the the Oxford University Hospitals between 21st March 2014 and 31st March 2018. We extracted 45 314 vital-sign measurements as a balanced training set and 359 481 vital-sign measurements as an imbalanced testing set to mimic a real-life setting of emergency admissions. DEWS achieved superior accuracy than the state-of-the-art that is currently implemented in clinical settings, the National Early Warning Score, in terms of the overall area under the receiver operating characteristic curve (AUROC) (0.880 vs. 0.866) and when evaluated independently for each of the three outcomes. Our attention-based architecture was able to recognize ‘historical’ trends in the data that are most correlated with the predicted probability. With high sensitivity, improved clinical utility and increased interpretability, our model can be easily deployed in clinical settings to supplement existing EWS systems

Sustainability integration in supply chain management through systematic literature review

Drafting a systematic literature review on sustainable integration of supply chain and logistics is the main aim of this research paper, given the current needs expressed by academics, managers, and investors. Business sustainability, among all management tasks, heavily depends on successful integration between supply chain and logistics. Scholarly and academic double-blinded peer-reviewed journals, indexed in Scopus and EBSCO, are considered, in a time range between the years 2010 and 2019. Thus, summaries of journals are comprehensively assessed to appraise the integration between Sustainable Supply Chain Management and logistics in business markets. Through this work paper, the procedures behind an effective application of supply chain models are investigated in order to improve knowledge, in terms of recent advancements. The authors develop and carry out an effective business-case analysis, in which the application of Supply Chain Management and logistics procedures led to advancements in the field, therefore their systematic review will be beneficial in terms of a comprehensive current framework provision. According to the analyze explored by the authors, Decision Support Systems and computer frameworks really support business leaders in using Sustainable Supply Chain Management and logistics information and models, further providing specific training. The research paper observed that, indeed, a systematic review is an effective tool that encourages a thorough understanding of the key features related to the specific field. Despite limitations due to a small number of studies carried out on the specific topic, we strongly believe that this research will provide a great contribution to business management, towards an exhaustive, useful, and insightful analysis of the current studies on the integration between Sustainable Supply Chain Management and logistics applications

Cross-sectional centiles of blood pressure by age and sex: a four-hospital database retrospective observational analysis

Objectives National guidelines for identifying physiological deterioration and sepsis in hospitals depend on thresholds for blood pressure that do not account for age or sex. In populations outside hospital, differences in blood pressure are known to occur with both variables. Whether these differences remain in the hospitalised population is unknown. This database analysis study aims to generate representative centiles to quantify variations in blood pressure by age and sex in hospitalised patients. Design Retrospective cross-sectional observational database analysis. Setting Four near-sea-level hospitals between April 2015 and April 2017. Participants 75 342 adult patients who were admitted to the hospitals and had at least one set of documented vital sign observations within 24 hours before discharge were eligible for inclusion. Patients were excluded if they died in hospital, had no vital signs 24 hours prior to discharge, were readmitted within 7 days of discharge, had missing age or sex or had no blood pressure recorded. Results Systolic blood pressure (SBP) for hospitalised patients increases with age for both sexes. Median SBP increases from 122 (CI: 121.1 to 122.1) mm Hg to 132 (CI: 130.9 to 132.2) mm Hg in men, and 114 (CI: 113.1 to 114.4) mm Hg to 135 (CI: 134.5 to 136.2) mm Hg in women, between the ages of 20 and 90 years. Diastolic blood pressure peaked around 50 years for men 76 (CI: 75.5 to 75.9) mm Hg and women 69 (CI: 69.0 to 69.4) mm Hg. The blood pressure criterion for sepsis, systolic Conclusion We have quantified variations in blood pressure by age and sex in hospitalised patients that have implications for recognition of deterioration. Nearly 10% of younger women met the blood pressure criterion for sepsis at hospital discharge.</p

Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign data: a multicentre development and validation

Objectives Early warning scores (EWS) alerting for in-hospital deterioration are commonly developed using routinely collected vital-sign data from the whole in-hospital population. As these in-hospital populations are dominated by those over the age of 45 years, resultant scores may perform less well in younger age groups. We developed and validated an age-specific early warning score (ASEWS) derived from statistical distributions of vital signs. Design Observational cohort study. Setting Oxford University Hospitals (OUH) July 2013 to March 2018 and Portsmouth Hospitals (PH) NHS Trust January 2010 to March 2017 within the Hospital Alerting Via Electronic Noticeboard database. Participants Hospitalised patients with electronically documented vital-sign observations Outcome Composite outcome of unplanned intensive care unit admission, mortality and cardiac arrest. Methods and results Statistical distributions of vital signs were used to develop an ASEWS to predict the composite outcome within 24 hours. The OUH development set consisted of 2 538 099 vital-sign observation sets from 142 806 admissions (mean age (SD): 59.8 (20.3)). We compared the performance of ASEWS to the National Early Warning Score (NEWS) and our previous EWS (MCEWS) on an OUH validation set consisting of 581 571 observation sets from 25 407 emergency admissions (mean age (SD): 63.0 (21.4)) and a PH validation set consisting of 5 865 997 observation sets from 233 632 emergency admissions (mean age (SD): 64.3 (21.1)). ASEWS performed better in the 16–45 years age group in the OUH validation set (AUROC 0.820 (95% CI 0.815 to 0.824)) and PH validation set (AUROC 0.840 (95% CI 0.839 to 0.841)) than NEWS (AUROC 0.763 (95% CI 0.758 to 0.768) and AUROC 0.836 (95% CI 0.835 to 0.838) respectively) and MCEWS (AUROC 0.808 (95% CI 0.803 to 0.812) and AUROC 0.833 (95% CI 0.831 to 0.834) respectively). Differences in performance were not consistent in the elder age group. Conclusions Accounting for age-related vital sign changes can more accurately detect deterioration in younger patients.</p

DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis

Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php.</p

DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis

Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php.</p

Development and validation of early warning score systems for COVID-19 patients

COVID-19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high-flow nasal oxygen, continuous positive airways pressure, non-invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub-optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests