Using Markov Models to Characterize and Predict Process Target Compliance

Processes are everywhere, covering disparate fields such as business, industry, telecommunications, and healthcare. They have previously been analyzed and modelled with the aim of improving understanding and efficiency as well as predicting future events and outcomes. In recent years, process mining has appeared with the aim of uncovering, observing, and improving processes, often based on data obtained from logs. This typically requires task identification, predicting future pathways, or identifying anomalies. We here concentrate on using Markov processes to assess compliance with completion targets or, inversely, we can determine appropriate targets for satisfactory performance. Previous work is extended to processes where there are a number of possible exit options, with potentially different target completion times. In particular, we look at distributions of the number of patients failing to meet targets, through time. The formulae are illustrated using data from a stroke patient unit, where there are multiple discharge destinations for patients, namely death, private nursing home, or the patient’s own home, where different discharge destinations may require disparate targets. Key performance indicators (KPIs) of this sort are commonplace in healthcare, business, and industrial processes. Markov models, or their extensions, have an important role to play in this work where the approach can be extended to include more expressive assumptions, with the aim of assessing compliance in complex scenarios

Gated Task Interaction Framework for Multi-task Sequence Tagging

Recent studies have shown that neural models can achieve high performance on several sequence labelling/tagging problems without the explicit use of linguistic features such as part-of-speech (POS) tags. These models are trained only using the character-level and the word embedding vectors as inputs. Others have shown that linguistic features can improve the performance of neural models on tasks such as chunking and named entity recognition (NER). However, the change in performance depends on the degree of semantic relatedness between the linguistic features and the target task; in some instances, linguistic features can have a negative impact on performance. This paper presents an approach to jointly learn these linguistic features along with the target sequence labelling tasks with a new multi-task learning (MTL) framework called Gated Tasks Interaction (GTI) network for solving multiple sequence tagging tasks. The GTI network exploits the relations between the multiple tasks via neural gate modules. These gate modules control the flow of information between the different tasks. Experiments on benchmark datasets for chunking and NER show that our framework outperforms other competitive baselines trained with and without external training resources.Comment: 8 page

Fusing Thermopile Infrared Sensor Data for Single Component Activity Recognition within a Smart Environment

To provide accurate activity recognition within a smart environment, visible spectrum cameras can be used as data capture devices in solution applications. Privacy, however, is a significant concern with regards to monitoring in a smart environment, particularly with visible spectrum cameras. Their use, therefore, may not be ideal. The need for accurate activity recognition is still required and so an unobtrusive approach is addressed in this research highlighting the use of a thermopile infrared sensor as the sole means of data collection. Image frames of the monitored scene are acquired from a thermopile infrared sensor that only highlights sources of heat, for example, a person. The recorded frames feature no discernable characteristics of people; hence privacy concerns can successfully be alleviated. To demonstrate how thermopile infrared sensors can be used for this task, an experiment was conducted to capture almost 600 thermal frames of a person performing four single component activities. The person’s position within a room, along with the action being performed, is used to appropriately predict the activity. The results demonstrated that high accuracy levels, 91.47%, for activity recognition can be obtained using only thermopile infrared sensors

Development of a technology adoption and usage prediction tool for assistive technology for people with dementia

This article is available open access through the publisher’s website at the link below. Copyright @ The Authors 2013.In the current work, data gleaned from an assistive technology (reminding technology), which has been evaluated with people with Dementia over a period of several years was retrospectively studied to extract the factors that contributed to successful adoption. The aim was to develop a prediction model with the capability of prospectively assessing whether the assistive technology would be suitable for persons with Dementia (and their carer), based on user characteristics, needs and perceptions. Such a prediction tool has the ability to empower a formal carer to assess, through a very limited amount of questions, whether the technology will be adopted and used.EPSR