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1. UDNet: A Unified Deep Learning-Based AutoML Framework to Execute Multiple ML Strategies for Multi-Modal Unstructured Data Processing

Author

Shubham Jain and Enda Fallon

Subjects
Unstructured data, deep reinforcement learning, AutoML, multi-modal data, model selection, unstructured data evaluation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The rise of multi-modal unstructured data has intensified the need for advanced processing solutions capable of deriving meaningful insights efficiently. UDNet emerges as a concurrent ML processing framework that leverages its novel methodologies, specifically Adaptive Confidence Bound (ACB) and Multi-Fidelity Meta-Learning (MFM), to execute multiple ML strategies for multi-modal unstructured data processing. These innovations are crucial for dynamically adjusting algorithm selection based on performance trends and evaluating algorithm efficacy across various data fidelity levels, optimizing computational resources while ensuring accuracy in model selection. Crucially, UDNet has been rigorously evaluated against a suite of benchmark datasets, including ICDAR 2013 Table Competition Dataset, Marmot Dataset, MIMIC-III Clinical Database, and TabLeX, each chosen for their complexity and representativeness of real-world scenarios. This comprehensive evaluation showcases UDNet’s robustness and versatility across different data types and formats. To accurately assess its performance, we introduced specialized evaluation metrics: Extraction Accuracy (EA), for measuring the precision of unstructured data extraction; Table Integrity (TI), for evaluating the accuracy of structured data conversion; and File-Type Versatility (FV), for assessing adaptability across diverse data formats. These metrics, alongside traditional ones, have substantiated UDNet’s enhanced efficiency and accuracy compared to existing AutoML solutions in the sphere of unstructured data processing. The introduction of these metrics not only underscores the technical sophistication of UDNet but also addresses the nuances of evaluating performance in unstructured data processing, marking a significant contribution to the field.

Published
2024
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2. Paediatric Homecare Risk Management: An Application of Functional Resonance Analysis Method (FRAM)

Author

Kevin M. Hoy, Enda Fallon, and Martina Kelly

Subjects
homecare, risk management, incident reporting, risk assessment, nursing, Industrial safety. Industrial accident prevention, T55-55.3, Medicine (General), R5-920
Abstract

Paediatric homecare is an advancing field of healthcare, bringing care direct to patients in their own homes. Risk management is an integral component of homecare services, including incident and risk assessment management. The objective of the study was to investigate risk management in homecare focusing on two aspects: incident reporting and risk assessments. A Grounded Theory approach was used to gather key functions of these aspects; these were then mapped using the Functional Resonance Analysis method (FRAM). Nineteen nurses working in paediatric homecare services were interviewed for the study. The interviews were semi-structured and focused on risk, quality, complaints, audit, care, and management. The interview data were transcribed and coded using Nvivo; the data were then converted into functions for utilization in the FRAM tool. The FRAM detailed the process of incident reporting and risk assessment management of the actual work carried out as viewed by the participants of the study. The information was then analysed and contrasted with the organizational policy to gain an understanding of the systems of incident reporting and risk assessments, which then led to the development of a refined process that could have less variability in function. Consequently, changes to policy and training in risk management were recommended to enhance the systems.

Published
2023
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46. The development and preliminary evaluation of a clinician e-learning training platform for a neonatal sepsis risk monitor for use in ICU settings

Author

Evismar Andrade, Leo R. Quinlan, Richard Harte, Bronwyn Reid-McDermott, Frank Kirrane, Enda Fallon, Martina Kelly, Tony Hall, Michael Scully, John Laffey, Patrick Pladys, Ethel Ryan, Dara Byrne, Gearóid ÓLaighin, University Hospital Galway, CHU Pontchaillou [Rennes], Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), and European Union (EU) [689260]

Subjects
Sepsis, [SDV]Life Sciences [q-bio], Usability, Patient monitoring system, Training, Physical Therapy, Sports Therapy and Rehabilitation, Human Factors and Ergonomics, Medical equipment, Safety, Risk, Reliability and Quality, E-learning, Engineering (miscellaneous), Human factors
Abstract

International audience; Background: Training clinicians on the use of hospital-based patient monitoring systems (PMS) is vital to mitigate the risk of use errors and of frustration using these devices, especially when used in ICU settings. PMS training is typically delivered through face-to-face training sessions in the hospital. However, it is not always feasible to deliver training in this format to all clinical staff given some constraints (e.g., availability of staff and trainers to attend in-person training sessions and the costs associated with face-to-face training). Objective: The literature indicates that E-learning has the potential to mitigate barriers associated with time restrictions for trainers and trainees and evidence shows it to be more flexible, and convenient for learners in healthcare settings. This study aimed to develop and carry out a preliminary evaluation via a case study of an e -learning training platform designed for a novel neonatal sepsis risk monitor system (Digi-NewB).Methods: A multi-modal qualitative research case study approach was used, including the analysis of three qualitative data sources: (i) audio/video recordings of simulation sessions in which participants were asked to operate the system as intended (e.g., update the clinical observations and monitor the sepsis risk), (ii) interviews with the simulation participants and an attending key opinion leader (KOL), who observed all simulation ses-sions, and (iii) post-simulation survey.Results: After receiving ethical approval for the study, nine neonatal intensive care unit (NICU) nurses completed the online training and participated in the simulation and follow-up interview sessions. The KOL was also interviewed, and seven out of the nine NICU nurses answered the post-simulation survey. The video/audio analysis of the simulations revealed that participants were able to use and interpret the Digi-NewB interface. Interviews with simulation participants and the KOL, and feedback extracted from the survey, revealed that participants were overall satisfied with the training platform and perceived it as an efficient and effective method to deliver medical device training.Conclusions: This study developed an online training platform to train clinicians in the use of a critical care medical device and carried out a preliminary evaluation of the platform via a case study. The e-learning platform was designed to supplement and enhance other training approaches. Further research is required to evaluate the effectiveness of this approach.

Published
2023
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