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

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Deepr: A Convolutional Net for Medical Records

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space

Multisensory legal machines and legal act production

This paper expands on the concept of legal machine which was presented first at IRIS 2011 in Salzburg. The research subjects are (1) the creation of institutional facts by machines, and (2) multimodal communication of legal content to humans. Simple examples are traffic lights and vending machines. Complicated examples are computer-based information systems in organisations, form proceedings workflows, and machines which replace officials in organisations. The actions performed by machines have legal importance and draw legal consequences. Machines similarly as humans can be imposed status-functions of legal actors. The analogy of machines with humans is in the focus of this paper. Legal content can be communicated by machines and can be perceived by all of our senses. The content can be expressed in multimodal languages: textual, visual, acoustic, gestures, aircraft manoeuvres, etc. The concept of encapsulatation of human into machine is proposed. Herein humanintended actions are communicated through the machine’s output channel. Encapsulations can be compared with deities and mythical creatures that can send gods’ messages to people through the human mouth. This paper also aims to identify law production patterns by machines

Identifying Research Fields within Business and Management: A Journal Cross-Citation Analysis

A discipline such as business and management (B&M) is very broad and has many fields within it, ranging from fairly scientific ones such as management science or economics to softer ones such as information systems. There are at least three reasons why it is important to identify these sub-fields accurately. Firstly, to give insight into the structure of the subject area and identify perhaps unrecognised commonalities; second for the purpose of normalizing citation data as it is well known that citation rates vary significantly between different disciplines. And thirdly, because journal rankings and lists tend to split their classifications into different subjects – for example, the Association of Business Schools (ABS) list, which is a standard in the UK, has 22 different fields. Unfortunately, at the moment these are created in an ad hoc manner with no underlying rigour. The purpose of this paper is to identify possible sub-fields in B&M rigorously based on actual citation patterns. We have examined 450 journals in B&M which are included in the ISI Web of Science (WoS) and analysed the cross-citation rates between them enabling us to generate sets of coherent and consistent sub-fields that minimise the extent to which journals appear in several categories. Implications and limitations of the analysis are discussed