RESUMEN
BACKGROUND: University Hospitals Dorset (UHD) has over 1,000 thyroid patient contacts annually. These are primarily patients with autoimmune hyperthyroidism treated with Carbimazole titration. Dose adjustments are made by a healthcare professional (HCP) based on the results of thyroid function tests, who then prescribes a dose and communicates this to the patient via letter. This is time-consuming and introduces treatment delays. This study aimed to replace some time-intensive manual dose adjustments with a machine learning model to determine Carbimazole dosing. This can in the future serve patients with rapid and safe dose determination and ease the pressures on HCPs. METHODS: Data from 421 hyperthyroidism patients at UHD were extracted and anonymised. A total of 353 patients (83.85%) were included in the study. Different machine-learning classification algorithms were tested under several data processing regimes. Using an iterative approach, consisting of an initial model selection followed by a feature selection method the performance was improved. Models were evaluated using weighted F1 scores and Brier scores to select the best model with the highest confidence. RESULTS: The best performance is achieved using a random forest (RF) approach, resulting in good average F1 scores of 0.731. A model was selected based on a balanced assessment considering the accuracy of the prediction (F1 = 0.751) and the confidence of the model (Brier score = 0.38). CONCLUSION: To simulate a use-case, the accumulation of the prediction error over time was assessed. It was determined that an improvement in accuracy is expected if this model was to be deployed in practice.
RESUMEN
INTRODUCTION: The introduction of electronic patient records in the ambulance service provides new opportunities to monitor the population. Approximately 36% of patients presenting to English ambulance services are discharged at scene. Ambulance records are therefore an ideal data source for syndromic early event detection systems to monitor infectious disease in the pre-hospital population. It has been previously found that tympanic temperature records can be used to detect influenza outbreaks in emergency departments. This study aimed to determine whether routine tympanic temperature readings collected by ambulance crews can be used to detect seasonal influenza. METHODS: Here we show that temperature readings do allow the detection of seasonal influenza before methods applied to conventional data sources. The counts of pyretic patients were used to calculate a sliding case ratio as a measurement to detect seasonal influenza outbreaks. This method does not rely on conventional thresholds and can be adapted to the data. RESULTS: The data collected correlated with seasonal influenza. The 2016/2017 outbreak was detected up to nine weeks before other surveillance programmes. The results show that ambulance records can be a useful data source for biosurveillance systems. CONCLUSION: Temperature readings from routinely collected ambulance patient records can be used as a surveillance tool for febrile diseases.