Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period

Efficient and effective OCR engine training

We present an efficient and effective approach to train OCR engines using the Aletheia document analysis system. All components required for training are seamlessly integrated into Aletheia: training data preparation, the OCR engine’s training processes themselves, text recognition, and quantitative evaluation of the trained engine. Such a comprehensive training and evaluation system, guided through a GUI, allows for iterative incremental training to achieve best results. The widely used Tesseract OCR engine is used as a case study to demonstrate the efficiency and effectiveness of the proposed approach. Experimental results are presented validating the training approach with two different historical datasets, representative of recent significant digitisation projects. The impact of different training strategies and training data requirements is presented in detail

Perks and Pitfalls of City Directories as a Micro-Geographic Data Source

Historical city directories are rich sources of micro-geographic data. They provide information on the location of households and firms and their occupations and industries, respectively. We develop a generic algorithmic work flow that converts scans of them into geo- and status-referenced household-level data sets. Applying the work flow to our case study, the Berlin 1880 directory, adds idiosyncratic challenges that should make automation less attractive. Yet, employing an administrative benchmark data set on household counts, incomes, and income distributions across more than 200 census tracts, we show that semi-automatic referencing yields results very similar to those from labour-intensive manual referencing. Finally, we discuss potential applications in economic history and beyond

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach

The linguistic and social impact of multiculturalism can no longer be neglected in any sector, creating the urgent need of creating systems and procedures for managing and sharing cultural heritages in both supranational and multi-literate contexts. In order to achieve this goal, text sensing appears to be one of the most crucial research areas. The long-term objective of the DigitalMaktaba project, born from interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. In this paper, we discuss the currently ongoing design of an innovative workflow and tool in the area of text sensing, for the automatic extraction of knowledge and cataloguing of documents written in non-Latin languages (Arabic, Persian and Azerbaijani). The current prototype leverages different OCR, text processing and information extraction techniques in order to provide both a highly accurate extracted text and rich metadata content (including automatically identified cataloguing metadata), overcoming typical limitations of current state of the art approaches. The initial tests provide promising results. The paper includes a discussion of future steps (e.g., AI-based techniques further leveraging the extracted data/metadata and making the system learn from user feedback) and of the many foreseen advantages of this research, both from a technical and a broader cultural-preservation and sharing point of view

Aspects of Application of Neural Recognition to Digital Editions

Artificial neuronal networks (ANN) are widely used in software systems which require solutions to problems without a traditional algorithmic approach, like in character recognition: ANN learn by example, so that they require a consistent and well-chosen set of samples to be trained to recognize their patterns. The network is taught to react with high activity in some of its output neurons whenever an input sample belonging to a specified class (e.g. a letter shape) is presented, and has the ability to assess the similarity of samples never encountered before by any of these models. Typical OCR applications thus require a significant amount of preprocessing for such samples, like resizing images and removing all the "noise" data, letting the letter contours emerge clearly from the background. Furthermore, usually a huge number of samples is required to effectively train a network to recognize a character against all the others. This may represent an issue for palaeographical applications because of the relatively low quantity and high complexity of digital samples available, and poses even more problems when our aim is detecting subtle differences (e.g. the special shape of a specific letter from a well-defined period and scriptorium). It would be probably wiser for scholars to define some guidelines for extracting from samples the features defined as most relevant according to their purposes, and let the network deal with just a subset of the overwhelming amount of detailed nuances available. ANN are no magic, and it is always the careful judgement of scholars to provide a theoretical foundation for any computer-based tool they might want to use to help them solve their problems: we can easily illustrate this point with samples drawn from any other application of IT to humanities. Just as we can expect no magic in detecting alliterations in a text if we simply feed a system with a collection of letters, we can no more claim that a neural recognition system might be able to perform well with a relatively small sample where each shape is fed as it is, without instructing the system about the features scholars define as relevant. Even before ANN implementations, it is exactly this theoretical background which must be put to the test when planning such systems