Neil McIntosh

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Condition monitoring often involves the analysis of systems with hidden factors that switch between different modes of operation in some way. Given a sequence of observations, the task is to infer the filtering distribution of the switch setting at each time step. In this paper, we present factorial switching linear dynamical systems as a general framework(More)
The observed physiological dynamics of an infant receiving intensive care are affected by many possible factors, including interventions to the baby, the operation of the monitoring equipment and the state of health. The Factorial Switching Kalman Filter can be used to infer the presence of such factors from a sequence of observations, and to estimate the(More)
The high incidence of false alarms in the intensive care unit (ICU) necessitates the development of improved alarming techniques. This study aimed to detect artifact patterns across multiple physiologic data signals from a neonatal ICU using decision tree induction. Approximately 200 h of bedside data were analyzed. Artifacts in the data streams were(More)
The aim of the NEONATE project is to investigate sub-optimal decision making in the neonatal intensive care unit and to implement decision support tools which will draw the attention of nursing and clinical staff to situations where specific actions should be taken or avoided. We have collected over 400 patient-hours of data on 31 separate babies, including(More)
This paper presents outcomes from a cognitive engineering project addressing the design problems of computerised monitoring in neonatal intensive care. Cognitive engineering is viewed, in this project, as a symbiosis between cognitive science and design practice. A range of methodologies has been used: interviews with neonatal staff, ward observations, and(More)
BACKGROUND Common concepts and definitions are important for the effective practice of medicine. In an intensive care unit clear understanding of terminology and communication between different staff groups may be critical for optimal care. If computerised decision support tools are to be successfully deployed in these high intensity environments, all staff(More)
The aim of this paper is to describe a novel approach to the analysis of data obtained from card-sorting experiments. These experiments were performed as a part of the initial phase of a project, called NEONATE. One of the aims of the project is to develop decision support tools for the neonatal intensive care environment. Physical card-sorts were performed(More)
As many as 86% of intensive care unit (ICU) alarms are false. Multiple signal integration of temporal monitor data by decision tree induction may improve artifact detection. We explore the effect of data granularity on model-building by comparing models made from 1-second versus 1-minute data. Models developed from 1-minute data remained effective when(More)
Artifacts in clinical intensive care monitoring lead to false alarms and complicate data analysis. They must be identified and processed to obtain true information. In this paper, we present a method for detecting artifacts in heart-rate (HR) and mean blood-pressure (BP) data from a physiological monitoring system used in preterm infants. The method uses(More)