Interpretation of an international terminology standard in the development of a logic-based compositional terminology

Purpose: Version 1.0 of the International Classification for Nursing Practice (ICNP®) is a logic-based compositional terminology. International Organization for Standardization (ISO) 18104:2003 Health Informatics¿Integration of a reference terminology model for nursing is an international standard to support the development, testing and implementation of nursing terminologies. Methods: This study examines how ISO 18104:2003 has been interpreted in the development of ICNP® Version 1.0 by identifying mappings between ICNP® and the ISO standard. Representations of diagnostic and interventional statements within ICNP® are also analyzed according to the requirements mandated by the ISO standard. Results: All structural components of ISO 18104:2003 i.e. semantic categories, semantic domains, qualifiers and semantic links are represented either directly or in interpreted form within ICNP®. The formal representations within ICNP® of diagnostic and interventional statements meet the requirement of the ISO standard. Conclusions: The findings of this study demonstrate that ICNP® Version 1.0 conforms to ISO 18104:2003. More importantly perhaps, this study provides practical examples of how components of a terminology standard might be interpreted and it examines how such a standard might be used to support the definition of high-level schemata in developing logic-based compositional terminologies

The management of context-sensitive features: A review of strategies

In this paper, we review five heuristic strategies for handling context- sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on context-sensitive learning

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care