The quality of vital signs measurements and value preferences in electronic medical records varies by hospital, specialty, and patient demographics

We aimed to assess the frequency of value preferences in recording of vital signs in electronic healthcare records (EHRs) and associated patient and hospital factors. We used EHR data from Oxford University Hospitals, UK, between 01-January-2016 and 30-June-2019 and a maximum likelihood estimator to determine the prevalence of value preferences in measurements of systolic and diastolic blood pressure (SBP/DBP), heart rate (HR) (readings ending in zero), respiratory rate (multiples of 2 or 4), and temperature (readings of 36.0 °C). We used multivariable logistic regression to investigate associations between value preferences and patient age, sex, ethnicity, deprivation, comorbidities, calendar time, hour of day, days into admission, hospital, day of week and speciality. In 4,375,654 records from 135,173 patients, there was an excess of temperature readings of 36.0 °C above that expected from the underlying distribution that affected 11.3% (95% CI 10.6–12.1%) of measurements, i.e. these observations were likely inappropriately recorded as 36.0 °C instead of the true value. SBP, DBP and HR were rounded to the nearest 10 in 2.2% (1.4–2.8%) and 2.0% (1.3–5.1%) and 2.4% (1.7–3.1%) of measurements. RR was also more commonly recorded as multiples of 2. BP digit preference and an excess of temperature recordings of 36.0 °C were more common in older and male patients, as length of stay increased, following a previous normal set of vital signs and typically more common in medical vs. surgical specialities. Differences were seen between hospitals, however, digit preference reduced over calendar time. Vital signs may not always be accurately documented, and this may vary by patient groups and hospital settings. Allowances and adjustments may be needed in delivering care to patients and in observational analyses and predictive tools using these factors as outcomes or exposures

Transfer Learning for Bayesian Case Detection Systems

In this age of big biomedical data, a variety of data has been produced worldwide. If we could combine that data more effectively, we might well develop a deeper understanding of biomedical problems and their solutions. Compared to traditional machine learning techniques, transfer learning techniques explicitly model differences among origins of data to provide a smooth transfer of knowledge. Most techniques focus on the transfer of data, while more recent techniques have begun to explore the possibility of transfer of models. Model-transfer techniques are especially appealing in biomedicine because they involve fewer privacy risks. Unfortunately, most model-transfer techniques are unable to handle heterogeneous scenarios where models differ in the features they contain, which occur commonly with biomedical data. This dissertation develops an innovative transfer learning framework to share both data and models under a variety of conditions, while allowing the inclusion of features that are unique to and informative about the target context. I used both synthetic and real-world datasets to test two hypotheses: 1) a transfer learning model that is learned using source knowledge and target data performs classification in the target context better than a target model that is learned solely from target data; 2) a transfer learning model performs classification in the target context better than a source model. I conducted a comprehensive analysis to investigate conditions where these two hypotheses hold, and more generally the factors that affect the effectiveness of transfer learning, providing empirical opinions about when and what to share. My research enables knowledge sharing under heterogeneous scenarios and provides an approach for understanding transfer learning performance in terms of differences of features, distributions, and sample sizes between source and target. The model-transfer algorithm can be viewed as a new Bayesian network learning algorithm with a flexible representation of prior knowledge. In concrete terms, this work shows the potential for transfer learning to assist in the rapid development of a case detection system for an emergent unknown disease. More generally, to my knowledge, this research is the first investigation of model-based transfer learning in biomedicine under heterogeneous scenarios