Hospital-wide natural language processing summarising the health data of 1 million patients

Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task

esyN: network building, sharing and publishing.

The construction and analysis of networks is increasingly widespread in biological research. We have developed esyN ("easy networks") as a free and open source tool to facilitate the exchange of biological network models between researchers. esyN acts as a searchable database of user-created networks from any field. We have developed a simple companion web tool that enables users to view and edit networks using data from publicly available databases. Both normal interaction networks (graphs) and Petri nets can be created. In addition to its basic tools, esyN contains a number of logical templates that can be used to create models more easily. The ability to use previously published models as building blocks makes esyN a powerful tool for the construction of models and network graphs. Users are able to save their own projects online and share them either publicly or with a list of collaborators. The latter can be given the ability to edit the network themselves, allowing online collaboration on network construction. esyN is designed to facilitate unrestricted exchange of this increasingly important type of biological information. Ultimately, the aim of esyN is to bring the advantages of Open Source software development to the construction of biological networks.This is the final published version of the paper. It's also available from PLOS at http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0106035

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Abstract Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

Precision radial velocities with CSHELL

Radial velocity identification of extrasolar planets has historically been dominated by optical surveys. Interest in expanding exoplanet searches to M dwarfs and young stars, however, has motivated a push to improve the precision of near infrared radial velocity techniques. We present our methodology for achieving 58 m/s precision in the K band on the M0 dwarf GJ 281 using the CSHELL spectrograph at the 3-meter NASA IRTF. We also demonstrate our ability to recover the known 4 Mjup exoplanet Gl 86 b and discuss the implications for success in detecting planets around 1-3 Myr old T Tauri stars.Comment: 31 pages, 3 figures, 2 tables, accepted for publication in Ap

Starspot-induced optical and infrared radial velocity variability in T Tauri star Hubble 4

We report optical (6150 Ang) and K-band (2.3 micron) radial velocities obtained over two years for the pre-main sequence weak-lined T Tauri star Hubble I 4. We detect periodic and near-sinusoidal radial velocity variations at both wavelengths, with a semi-amplitude of 1395\pm94 m/s in the optical and 365\pm80 m/s in the infrared. The lower velocity amplitude at the longer wavelength, combined with bisector analysis and spot modeling, indicates that there are large, cool spots on the stellar surface that are causing the radial velocity modulation. The radial velocities maintain phase coherence over hundreds of days suggesting that the starspots are long-lived. This is one of the first active stars where the spot-induced velocity modulation has been resolved in the infrared.Comment: Accepted for publication in The Astrophysical Journa