An infrastructure of stream data mining, fusion and management for monitored patients

Paper presented at the 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006, Salt Lake City, UT.This paper proposes an infrastructure for data mining, fusion and patient care management using continuous stream data monitored from critically ill patients. Stream data mining, fusion, and management provide efficient ways to increase data utilization and to support knowledge discovery, which can be utilized in many clinical areas to improve the quality of patient care services. The primary goal of our work is to establish a customized infrastructure model designed for critical care services at hospitals. However this structure can be easily expanded to other areas of clinical specialties

If it may have happened before, it happened, but not necessarily before

Temporal uncertainty in raw data can impede the inference of temporal and causal relationships between events and compromise the output of data-to-text NLG systems. In this paper, we introduce a framework to reason with and represent temporal uncertainty from the raw data to the generated text, in order to provide a faithful picture to the user of a particular situation. The model is grounded in experimental data from multiple languages, shedding light on the generality of the approach.peer-reviewe

Towards a possibility-theoretic approach to uncertainty in medical data interpretation for text generation

Many real-world applications that reason about events obtained from raw data must deal with the problem of temporal uncertainty, which arises due to error or inaccuracy in data. Uncertainty also compromises reasoning where relationships between events need to be inferred. This paper discusses an approach to dealing with uncertainty in temporal and causal relations using Possibility Theory, focusing on a family of medical decision support systems that aim to generate textual summaries from raw patient data in a Neonatal Intensive Care Unit. We describe a framework to capture temporal uncertainty and to express it in generated texts by mean of linguistic modifiers. These modifiers have been chosen based on a human experiment testing the association between subjective certainty about a proposition and the participants’ way of verbalising it.peer-reviewe

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

Extending Temporal Databases to Deal with Telic/Atelic Medical Data

Objective. In this paper, we aim at defining a general-purpose data model and query language coping with both “telic ” and “atelic ” medical data. Background. In the area of Medical Informatics, there is an increasing realization that temporal information plays a crucial role, so that suitable database models and query languages are needed to store and support it. However, despite the wide range of approaches in the area, in this paper we show that a relevant class of medical data cannot be properly dealt with. Methodology. We first show that data models based on the “point-based ” semantics, which is (implicitly or explicitly) assumed by the totality of temporal DataBase approaches, have several limitations when dealing with “telic ” data. We then propose a new model (based on the “interval-based” semantics) to cope with such data, and extend the query language accordingly. Results. We propose a new three-sorted model and a query language to properly deal with both “telic ” and “atelic ” medical data (as well as nontemporal data). Our query language is flexible, since it allows one to switch from “atelic ” to “telic ” data, and vice versa