Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports

Background The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical features from free-text medical notes. However, processing this rich resource of data for clinical and research purposes, depends on labor-intensive and potentially error-prone manual review. The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases. Material and methods Bone scintigraphy reports of patients undergoing surgery for bone metastases were labeled each by three independent reviewers using a binary classification (single metastasis versus two or more metastases) to establish a ground truth. A stratified 80:20 split was used to develop and test an extreme-gradient boosting supervised machine learning NLP algorithm. Results A total of 704 free-text bone scintigraphy reports from 704 patients were included in this study and 617 (88%) had multiple bone metastases. In the independent test set (n = 141) not used for model development, the NLP algorithm achieved an 0.97 AUC-ROC (95% confidence interval [CI], 0.92-0.99) for classification of multiple bone metastases and an 0.99 AUC-PRC (95% CI, 0.99-0.99). At a threshold of 0.90, NLP algorithm correctly identified multiple bone metastases in 117 of the 124 who had multiple bone metastases in the testing cohort (sensitivity 0.94) and yielded 3 false positives (specificity 0.82). At the same threshold, the NLP algorithm had a positive predictive value of 0.97 and F1-score of 0.96. Conclusions NLP has the potential to automate clinical data extraction from free text radiology notes in orthopedics, thereby optimizing the speed, accuracy, and consistency of clinical chart review. Pending external validation, the NLP algorithm developed in this study may be implemented as a means to aid researchers in tackling large amounts of data

Laag pathogene aviaire influenza virus infecties op pluimveebedrijven in Nederland

Laag Pathogene Aviaire Influenza (LPAI) is een aandoening bij pluimvee die wordt veroorzaakt door LPAI virussen. In Nederland worden elk jaar meer infecties met LPAI virussen op pluimveebedrijven gedetecteerd. In dit rapport is gekeken naar een aantal mogelijke oorzaken voor deze toename. Het vermoeden bestaat dat serologisch en/of virologisch positieve bedrijven vaker dan gemiddeld bedrijven zijn met vrije uitloop. De vraag is of dit werkelijk zo is, en zo ja welke maatregelen dan genomen kunnen worden om de kans op een introductie op een bedrijf met vrije uitloop te verminderen

The randomised uterine septum transsection trial (TRUST): Design and protocol

Background: A septate uterus is a uterine anomaly that may affect reproductive outcome, and is associated with an increased risk for miscarriage, subfertility and preterm birth. Resection of the septum is subject of debate. There is no convincing evidence concerning its effectiveness and safety. This study aims to assess whether hysteroscopic septum resection improves reproductive outcome in women with a septate uterus. Methods/design: A multi-centre randomised contr

Interactive infrastructures—distributed interfaces for the built environment

An interactive infrastructure allows users to have access to the parameters of a building in real time, potentially leading to an increased productivity, lowering of energy and material consumption, and generally greater user satisfaction and experience. The aims of this research are to investigate and design new ways for interacting with the infrastructure of buildings, and to research and develop the basis for an interactive infrastructure communication language which enables the distributed elements of the infrastructure to communicate with each other and with the users. The effectiveness of infrastructure in buildings can be significantly improved with this approac