Background. Patients' encounters with healthcare services must undergo clinical coding. These codes are typically derived from free-text notes. Manual clinical coding is expensive, time-consuming and prone to error. Automated clinical coding systems have great potential to save resources, and realtime availability of codes would improve oversight of patient care and accelerate research. Automated coding is made challenging by the idiosyncrasies of clinical text, the large number of disease codes and their unbalanced distribution.Methods. We explore methods for representing clinical text and the labels in hierarchical clinical coding ontologies. Text is represented as term frequencyinverse document frequency counts and then as word embeddings, which we use as input to recurrent neural networks. Labels are represented atomically, and then by learning representations of each node in a coding ontology and composing a representation for each label from its respective node path. We consider different strategies for initialisation of the node representations. We evaluate our methods using the publicly-available Medical Information Mart for Intensive Care III dataset: we extract the history of presenting illness section from each discharge summary in the dataset, then predicting the International Classification of Diseases, ninth revision, Clinical Modification codes associated with these.

Objective Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients’ longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action. Materials and Methods We use a temporal convolutional network to encode longitudinal data and a feedforward neural network to encode demographic data from 4713 ICU admissions in 2014–2018. For each hour of each admission, we predict events in the subsequent 1–6 hours. We compare performance with other models including a recurrent neural network. Results Our model performed similarly to the recurrent neural network for some events and outperformed it for others. This performance increase was more evident in a sensitivity analysis where the prediction timeframe was varied. Average positive predictive value (95% CI) was 0.786 (0.781–0.790) and 0.738 (0.732–0.743) for up- and down-titrating FiO2, 0.574 (0.519–0.625) for extubation, 0.139 (0.117–0.162) for intubation, 0.533 (0.492–0.572) for starting noradrenaline, 0.441 (0.433–0.448) for fluid challenge, and 0.315 (0.282–0.352) for death. Discussion Events were better predicted where their important determinants were captured in structured electronic health data, and where they occurred in homogeneous circumstances. We produce partial dependence plots that show our model learns clinically-plausible associations between its inputs and predictions. Conclusion Temporal convolutional networks improve prediction of clinical events when used to represent longitudinal ICU data.

Clinical prediction models typically make point estimates of risk. However, values of key variables are often missing during model development or at prediction time, meaning that the point estimates mask significant uncertainty and can lead to over-confident decision making. We present a model of mortality risk in emergency laparotomy which instead presents a distribution of predicted risks, highlighting the uncertainty over the risk of death with an intuitive visualisation. We developed and validated our model using data from 127134 emergency laparotomies from patients in England and Wales during 2013–2019. We captured the uncertainty arising from missing data using multiple imputation, allowing prospective, patient-specific imputation for variables that were frequently missing. Prospective imputation allows early prognostication in patients where these variables are not yet measured, accounting for the additional uncertainty this induces. Our model showed good discrimination and calibration (95% confidence intervals: Brier score 0.071–0.078, C statistic 0.859–0.873, calibration error 0.031–0.059) on unseen data from 37 hospitals, consistently improving upon the current gold-standard model. The dispersion of the predicted risks varied significantly between patients and increased where prospective imputation occurred. We present a case study that illustrates the potential impact of uncertainty quantification on clinical decision making. Our model improves mortality risk prediction in emergency laparotomy and has the potential to inform decision-makers and assist discussions with patients and their families. Our analysis code was robustly developed and is publicly available for easy replication of our study and adaptation to predicting other outcomes.

Our e-tutorial is effective and meets the needs of medical students. Once the e-tutorial is disseminated more widely and is expanded by user-contributed questions, we hope that it will become established as a national platform for collaborative prescribing education.