Towards new information resources for public health: From WordNet to MedicalWordNet

In the last two decades, WORDNET has evolved as the most comprehensive computational lexicon of general English. In this article, we discuss its potential for supporting the creation of an entirely new kind of information resource for public health, viz. MEDICAL WORDNET. This resource is not to be conceived merely as a lexical extension of the original WORDNET to medical terminology; indeed, there is already a considerable degree of overlap between WORDNET and the vocabulary of medicine. Instead, we propose a new type of repository, consisting of three large collections of (1) medically relevant word forms, structured along the lines of the existing Princeton WORDNET; (2) medically validated propositions, referred to here as medical facts, which will constitute what we shall call MEDICAL FACTNET; and (3) propositions reflecting laypersons’ medical beliefs, which will constitute what we shall call the MEDICAL BELIEFNET. We introduce a methodology for setting up the MEDICAL WORDNET. We then turn to the discussion of research challenges that have to be met in order to build this new type of information resource

Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

The journals of importance to UK clinicians: A questionnaire survey of surgeons

Background: Peer-reviewed journals are seen as a major vehicle in the transmission of research findings to clinicians. Perspectives on the importance of individual journals vary and the use of impact factors to assess research is criticised. Other surveys of clinicians suggest a few key journals within a specialty, and sub-specialties, are widely read. Journals with high impact factors are not always widely read or perceived as important. In order to determine whether UK surgeons consider peer-reviewed journals to be important information sources and which journals they read and consider important to inform their clinical practice, we conducted a postal questionnaire survey and then compared the findings with those from a survey of US surgeons. Methods: A questionnaire survey sent to 2,660 UK surgeons asked which information sources they considered to be important and which peer-reviewed journals they read, and perceived as important, to inform their clinical practice. Comparisons were made with numbers of UK NHSfunded surgery publications, journal impact factors and other similar surveys. Results: Peer-reviewed journals were considered to be the second most important information source for UK surgeons. A mode of four journals read was found with academics reading more than non-academics. Two journals, the BMJ and the Annals of the Royal College of Surgeons of England, are prominent across all sub-specialties and others within sub-specialties. The British Journal of Surgery plays a key role within three sub-specialties. UK journals are generally preferred and readership patterns are influenced by membership journals. Some of the journals viewed by surgeons as being most important, for example the Annals of the Royal College of Surgeons of England, do not have high impact factors. Conclusion: Combining the findings from this study with comparable studies highlights the importance of national journals and of membership journals. Our study also illustrates the complexity of the link between the impact factors of journals and the importance of the journals to clinicians. This analysis potentially provides an additional basis on which to assess the role of different journals, and the published output from research

A Short Review of Ethical Challenges in Clinical Natural Language Processing

Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.Comment: First Workshop on Ethics in Natural Language Processing (EACL'17