Identification, indexing, and retrieval of cardio-pulmonary resuscitation (CPR) video scenes of simulated medical crisis.

Medical simulations, where uncommon clinical situations can be replicated, have proved to provide a more comprehensive training. Simulations involve the use of patient simulators, which are lifelike mannequins. After each session, the physician must manually review and annotate the recordings and then debrief the trainees. This process can be tedious and retrieval of specific video segments should be automated. In this dissertation, we propose a machine learning based approach to detect and classify scenes that involve rhythmic activities such as Cardio-Pulmonary Resuscitation (CPR) from training video sessions simulating medical crises. This applications requires different preprocessing techniques from other video applications. In particular, most processing steps require the integration of multiple features such as motion, color and spatial and temporal constrains. The first step of our approach consists of segmenting the video into shots. This is achieved by extracting color and motion information from each frame and identifying locations where consecutive frames have different features. We propose two different methods to identify shot boundaries. The first one is based on simple thresholding while the second one uses unsupervised learning techniques. The second step of our approach consists of selecting one key frame from each shot and segmenting it into homogeneous regions. Then few regions of interest are identified for further processing. These regions are selected based on the type of motion of their pixels and their likelihood to be skin-like regions. The regions of interest are tracked and a sequence of observations that encode their motion throughout the shot is extracted. The next step of our approach uses an HMM classiffier to discriminate between regions that involve CPR actions and other regions. We experiment with both continuous and discrete HMM. Finally, to improve the accuracy of our system, we also detect faces in each key frame, track them throughout the shot, and fuse their HMM confidence with the region\u27s confidence. To allow the user to view and analyze the video training session much more efficiently, we have also developed a graphical user interface (GUI) for CPR video scene retrieval and analysis with several desirable features. To validate our proposed approach to detect CPR scenes, we use one video simulation session recorded by the SPARC group to train the HMM classifiers and learn the system\u27s parameters. Then, we analyze the proposed system on other video recordings. We show that our approach can identify most CPR scenes with few false alarms

Ensemble learning method for hidden markov models.

For complex classification systems, data are gathered from various sources and potentially have different representations. Thus, data may have large intra-class variations. In fact, modeling each data class with a single model might lead to poor generalization. The classification error can be more severe for temporal data where each sample is represented by a sequence of observations. Thus, there is a need for building a classification system that takes into account the variations within each class in the data. This dissertation introduces an ensemble learning method for temporal data that uses a mixture of Hidden Markov Model (HMM) classifiers. We hypothesize that the data are generated by K models, each of which reacts a particular trend in the data. Model identification could be achieved through clustering in the feature space or in the parameters space. However, this approach is inappropriate in the context of sequential data. The proposed approach is based on clustering in the log-likelihood space, and has two main steps. First, one HMM is fit to each of the N individual sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE) based discriminative, and the Variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the multiple models outputs using a decision level fusion method such as an artificial neural network or a hierarchical mixture of experts. Our approach was evaluated on two real-world applications: (1) identification of Cardio-Pulmonary Resuscitation (CPR) scenes in video simulating medical crises; and (2) landmine detection using Ground Penetrating Radar (GPR). Results on both applications show that the proposed method can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data

Exploring the experiences of nurses who care for children who have Acute Life Threatening Events (ALTE) in hospital

This thesis presents a program of work designed to explore and describe what the experience of caring for a child who has an Acute Life Threatening Event (ALTE) is like for the nurses. An ALTE may include a cardiac arrest, respiratory arrest or unplanned admission for a ward to the Paediatric Intensive Care unit. Using the MRC framework for the development of complex interventions, this information was then coupled with theory to develop the PREPARE and SUPPORT interventions. Given the wide-ranging and exploratory nature of this research, a pragmatic, mixed design approach was used to address the aims and objectives of the thesis. The mixed design approach included: a systematic literature review; international survey of practice; interviews with nurses and doctors using Interpretative Phenomenological Analysis; development, refinement and evaluation of interventions during a feasibility study. Two studies were identified through the systematic review which aimed to evaluate the effectiveness of debriefing. The studies did not provide evidence to support the use of these interventions within healthcare. The international survey of practice demonstrated hospitals were using interventions to both prepare and support nurses for these events. The preparatory interventions were clinically focused and the majority of the supportive interventions included a debrief. The interventions were not being evaluated for effectiveness. The interviews conducted with nurses and doctors provided insight into what that experience was like for the participants. Using the MRC framework, this evidence was coupled with theory to develop the PREPARE and SUPPORT interventions. A multidisciplinary working party used an iterative process to refine and evaluate the interventions and study procedures were explored through a feasibility study. The pragmatic, mixed design approach demonstrated how the empirical evidence was coupled with theory and clinical expertise to develop interventions for use within the healthcare environment

How to Improve Assessment of Tetanus Immunity in the Emergency Room: A Prospective Cost-Effectiveness, Double Blind Study

peer reviewe

Empowering medical personnel to challenge through simulation-based training

The rigid structure of medical hierarchies within UK hospitals can become the source of dissatisfaction and conflict for medical personnel, the repercussions of which can be disastrous for patients and staff. The research reported herein presents the results of an investigation into the use of Virtual Reality (VR) simulation and conventional story-boarded techniques to empower medical personnel to challenge decisions they feel are inappropriate. Prototype applications were crafted from a selection of transcribed ‘challenge events’ acquired from an opportunistic sample of clinical staff. Data obtained from an initial investigation were used to establish attitudes toward challenging and evaluate the findings of the literature to generate research questions and objectives. Medical personnel who engaged with both media as part of an experimental phase assessed their viability as potential training resources to help foster the ability to challenge. Analysis of this experiment suggested that both techniques are viable tools in the delivery of decision-making training and could potentially deliver impact into other applications within healthcare. To increase the realism of the training material, the technologies should be presented in a format appropriate for those with limited ‘gaming’ experience and allow a credible level of interaction with the environment and characters

Instilling reflective practice – The use of an online portfolio in innovative optometric education Accepted as: e‐poster Paper no. 098

At UCLAN we are breaking the mould and have developed a blended learning MSci optometry programme which is the first blended learning course in optometric education in the UK and the first to use a practice-based online portfolio. Optometry has traditionally been taught as a 3‐year undergraduate programme. Upon successful graduation, students are required to complete a year in practice and meet the General Optical Council's (GOC) “ability to” core competencies. However, a recent study by the GOC found that 76% of students felt unprepared for professional practice with insufficient clinical experience and in response, the GOC is currently undertaking an educational strategic review. To ensure the students receive high-quality clinical experience in the workplace, we have developed an online logbook and portfolio. Students log their experiences, learning points and reflections. The portfolio is closely monitored both by the student's mentor in practice and by academic staff. The content and reflections logged by the students then helps to drive the face to face teaching, small group discussions and clinical experiences provided by the university