Predicting the Severity of Adverse Events on Osteoporosis Drugs Using Attribute Weighted Logistic Regression.

Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients.

Incremental Learning of Human Activities in Smart Homes.

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours.

Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes.

Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant's activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance.

Population pharmacokinetics of intramuscular droperidol in acutely agitated patients.

Background: Intramuscular droperidol is used increasingly for sedation of aggressive and violent patients. This study aimed to characterise the pharmacokinetics of intramuscular droperidol in these patients to determine how rapidly it is absorbed and the expected duration of measurable drug concentrations.

Methods: We undertook a population pharmacokinetic analysis of a subgroup of patients from a clinical trial comparing droperidol and midazolam: 17 receiving 5 mg and 24 receiving 10 mg droperidol.

Designs to balance cost and success rate for an early phase clinical study.

We present an initial exploration of a fully cost-driven design. A design criterion was proposed that represented the minimum expected cost of an early phase clinical study, where costs include resource use as well as study failure. The design was based on attainment of a target concentration in a cohort of study participants.

Comparison of robust criteria for D-optimal designs.

This study compared the performance of a local and three robust optimality criteria in terms of the standard error for a one-parameter and a two-parameter nonlinear model with uncertainty in the parameter values. The designs were also compared in conditions where there was misspecification in the prior parameter distribution. The impact of different correlation between parameters on the optimal design was examined in the two-parameter model.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies.

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation.

Adaptive optimal design for bridging studies with an application to population pharmacokinetic studies.

Purpose: To develop and evaluate methods for conducting adaptive population pharmacokinetic bridging studies.

Methods: An adaptive D-optimal design based on optimization of the population Fisher information matrix was used to determine the best sampling schedule for a target-population. Recruitment of the target-population was divided into batches and patients are assumed to enroll by batch.

Methods of robust design of nonlinear models with an application to pharmacokinetics.

Optimal design methods for nonlinear models are dependent on the true but unknown parameter values. Criteria for developing designs that are robust to the choice of parameter values such as ED optimality have been proposed. However, these criteria are computationally intensive and can perform poorly at extremes of the prior parameter distribution.