Automatisierte Klassifizierung von radiologischen Freitext-Befunden: Analyse verschiedener Feature-Extraction-Methoden zur Identifizierung distaler Fibulafrakturen

Purpose: Radiology reports mostly contain free-text, which makes it challenging to obtain structured data. Natural language processing (NLP) techniques transform free-text reports into machine-readable document vectors that are important for creating reliable, scalable methods for data analysis. The aim of this study is to classify unstructured radiograph reports according to fractures of the distal fibula and to find the best text mining method. Materials & Methods: We established a novel German language report dataset: a designated search engine was used to identify radiographs of the ankle and the reports were manually labeled according to fractures of the distal fibula. This data was used to establish a machine learning pipeline, which implemented the text representation methods bag-of-words (BOW), term frequency-inverse document frequency (TF-IDF), principal component analysis (PCA), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and document embedding (doc2vec). The extracted document vectors were used to train neural networks (NN), support vector machines (SVM), and logistic regression (LR) to recognize distal fibula fractures. The results were compared via cross-tabulations of the accuracy (acc) and area under the curve (AUC). Results: In total, 3268 radiograph reports were included, of which 1076 described a fracture of the distal fibula. Comparison of the text representation methods showed that BOW achieved the best results (AUC = 0.98; acc = 0.97), followed by TF-IDF (AUC = 0.97; acc = 0.96), NMF (AUC = 0.93; acc = 0.92), PCA (AUC = 0.92; acc = 0.9), LDA (AUC = 0.91; acc = 0.89) and doc2vec (AUC = 0.9; acc = 0.88). When comparing the different classifiers, NN (AUC = 0,91) proved to be superior to SVM (AUC = 0,87) and LR (AUC = 0,85). Conclusion: An automated classification of unstructured reports of radiographs of the ankle can reliably detect findings of fractures of the distal fibula. A particularly suitable feature extraction method is the BOW model. Key Points:  - The aim was to classify unstructured radiograph reports according to distal fibula fractures. - Our automated classification system can reliably detect fractures of the distal fibula. - A particularly suitable feature extraction method is the BOW model.Ziel: Radiologische Befundtexte enthalten häufig Freitext, was eine strukturierte Datenauswertung erschwert. Natural language processing (NLP)-Techniken wandeln Freitext in maschinenlesbare Dokumentenvektoren um, die für die Entwicklung zuverlässiger, skalierbarer Methoden zur Datenanalyse wichtig sind. Ziel dieser Studie war es, unstrukturierte Röntgenbefunde nach Frakturen der distalen Fibula zu klassifizieren und die beste Text-Mining-Methode zu finden. Material & Methoden: Zur Erstellung eines eigenen deutschsprachigen Befunddatensatzes wurden mittels einer dedizierten Suchmaschine Sprunggelenks-Röntgenbilder identifiziert und die entsprechenden Befunde manuell nach Frakturen der distalen Fibula sortiert. Anhand der Daten wurde eine Machine-Learning-Pipeline erstellt, die die Textrepräsentationsmethoden Bag-of-Words (BOW), Term Frequency-Inverse Document Frequency (TF-IDF), Principal Component Analysis (PCA), Non-Negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA) und Document Embedding (doc2vec) implementierte. Die extrahierten Dokumentvektoren wurden zum Trainieren von neuronalen Netzen (NN), Support Vector Machines (SVM) und logistischer Regression (LR) verwendet, um distale Fibulafrakturen zu erkennen. Die Ergebnisse wurden mittels Kreuztabellen bzgl. der Accuracy (acc) und der area under the curve (AUC) verglichen. Ergebnisse: Insgesamt wurden 3268 Röntgenbefunde inkludiert, von denen 1076 eine distale Fibulafraktur beschrieben. Der Vergleich der Textdarstellungsmethoden zeigte, dass BOW die besten Ergebnisse erzielte (AUC = 0,98; acc = 0,97), gefolgt von TF-IDF (AUC = 0,97; acc = 0,96), NMF (AUC = 0,93; acc = 0,92), PCA (AUC = 0,92; acc = 0,9), LDA (AUC = 0,91; acc = 0,89) und doc2vec (AUC = 0,9; acc = 0,88). Im Vergleich der Klassifikatoren erwiesen sich die NN (AUC = 0,91) gegenüber SVM (AUC = 0,87) und LR (AUC = 0,85) als überlegen. Schlussfolgerung: Durch die automatisierte Klassifikation von unstrukturierten Befunden von Sprunggelenksaufnahmen können Frakturen der distalen Fibula zuverlässig erkannt werden. Eine besonders geeignete Methode zur Feature Extraction ist das BOW-Modell. Kernaussagen:  - Ziel war die automatisierte Klassifizierung unstrukturierter Röntgenbefunde entsprechend distaler Fibulafrakturen. - Eine zuverlässige Detektion von distalen Fibulafrakturen ist durch das automatisierte Klassifizierungssystem gewährleistet. - Eine besonders geeignete Methode zur Feature Extraction ist das BOW-Modell

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran