Interpretable Machine Learning Model for Clinical Decision Making

Despite machine learning models being increasingly used in medical decision-making and meeting classification predictive accuracy standards, they remain untrusted black-boxes due to decision-makers\u27 lack of insight into their complex logic. Therefore, it is necessary to develop interpretable machine learning models that will engender trust in the knowledge they generate and contribute to clinical decision-makers intention to adopt them in the field. The goal of this dissertation was to systematically investigate the applicability of interpretable model-agnostic methods to explain predictions of black-box machine learning models for medical decision-making. As proof of concept, this study addressed the problem of predicting the risk of emergency readmissions within 30 days of being discharged for heart failure patients. Using a benchmark data set, supervised classification models of differing complexity were trained to perform the prediction task. More specifically, Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Gradient Boosting Machines (GBM) models were constructed using the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD). The precision, recall, area under the ROC curve for each model were used to measure predictive accuracy. Local Interpretable Model-Agnostic Explanations (LIME) was used to generate explanations from the underlying trained models. LIME explanations were empirically evaluated using explanation stability and local fit (R2). The results demonstrated that local explanations generated by LIME created better estimates for Decision Trees (DT) classifiers

FORMULATION OF A BUSINESS PLAN FOR A NURSE PRACTITIONER-DRIVEN OUTPATIENT PALLIATIVE CARE CLINIC

Problem: Although empirical evidence demonstrates significant benefits to quality of life and reduction of 30-day hospital readmissions with the intervention of palliative care, outpatient palliative care is not sufficiently available in the targeted metropolitan area. The National Priorities Partnership report identified palliative care as one of six priority areas that would significantly improve the quality of American health care (Meier, 2011). Project Purpose: The purpose of this practice change project was to develop a business plan that determined the feasibility of an outpatient palliative care nurse practitioner-driven clinic. Project Method: Formation of a business plan according to the United States Small Business Administration model, which has six components. Current scholarly evidence related to quality of life and 30-day hospital readmissions was synthesized in preparation for development of the business plan. Project Result: The business plan supports the feasibility of a nurse practitioner outpatient palliative care clinic in addition to traditional care in the Kansas City metropolitan area as a means to increase accessibility to palliative care and to: 1) improve quality of life for patients and families, 2) reduce 30-day hospital readmissions for patients with life-limiting illness

Survey on highly imbalanced multi-class data

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multi-class data

Promoting Clinical Engagement and Cross-sector Collaboration Through Changes in Workforce, Use of Technology, and Improved Business Systems

Published version made available here with permission from publisher.Background: Cross-sectoral collaboration across health care settings has the potential to deliver efficiencies as well as improve health care outcomes. There is a need for better understanding and awareness of models, mechanisms and strategies that enhance crosssectoral collaboration in Australia. Improved cross-sectoral collaboration is supported by a number of changes in workforce, use of technology and improved business systems. This review seeks to summarise these programs for those who may be seeking to engage in this area as a means of determining the range of options and possible proven benefits. Methodology: This study employs a mixed methods approach. A pragmatic literature review was undertaken to determine the relevant collaborative care models and review current programs Australia-wide that implement these models. Programs were selected from searching the grey and indexed medical literature as well as suggestions obtained from relevant stakeholders. Criteria for inclusion included having description in the peer reviewed and grey literature, ability to represent a unique model, extent of current use and description of outcomes of the intervention. Additional qualitative semi-structured interviews were conducted to elucidate more detailed information about technology, workforce and business systems. This information is summarised in the report and details about the individual programs are included as an appendix to this report. Results: Fifteen models were reviewed for this report. Qualitative semi-structured interview data were employed to supplement findings from the literature review. Key mechanisms of these models are described specifically focusing on the use of technology, workforce and business systems. Facilitators and barriers were identified and explored

2016 Research Week

RESEARCH WEEK 2016 TABLE OF CONTENTS ADMINISTRATION RESEARCH COMMITTEES ACTIVITY SCHEDULE LETTERS OF ENDORSEMENT GENERAL SESSION FACULTY, STAFF, AND STUDENT POSTER PRESENTATIONS FACULTY AND STUDENT ORAL PRESENTATION

Using Critical and Transformative Theory to Describe Basic Palliative Care in the Acute Care Setting

The population of chronically ill, older adults is expected to grow in the coming years as the baby boomer generation ages. The Institute of Medicine recommends that all healthcare providers have a basic competency in palliative care, also referred to as basic palliative care. The definitions and descriptions to date are vague and do not provide an in-depth description of how basic palliative care differs from the care provided by specialists. The purpose of this study was to describe nurses’ understanding and perceptions of basic palliative care in the acute care setting. Focus group and individual interviews were utilized for data collection. The results of this study deepen our understanding of how acute care nurses practice basic palliative care and the challenges that exist in caring for homeless and culturally diverse patients who could benefit from palliative care. This study offers guidance for future research, policy changes, and integrating palliative care into practice earlier in the illness trajectory

Validation of design artefacts for blockchain-enabled precision healthcare as a service.

Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption. Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR), Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management, and delivery. These disruptive innovations have made it feasible for the healthcare industry to provide personalised digital health solutions and services to the people and ensure sustainability in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in the system. Anecdotal evidence shows that people are refraining from adopting PHC due to distrust. This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in. The designed ecosystem also incorporates emerging information technologies that are potential to address the need for user-centricity, data privacy and security, accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem. The research adopts Soft System Methodology (SSM) to construct and validate the design artefact and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem. Following a comprehensive view of the scholarly literature, which resulted in a draft set of design principles and rules, eighteen design refinement interviews were conducted to develop the artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated through a design validation workshop, where the designed ecosystem was presented to a Delphi panel of twenty-two health industry actors. The key research finding was that there is a need for data-driven, secure, transparent, scalable, individualised healthcare services to achieve sustainability in healthcare. It includes explainable AI, data standards for biosensor devices, affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity, which prompts further research and industry application. The proposed ecosystem is potentially effective in growing trust, influencing patients in active engagement with real-world implementation, and contributing to sustainability in healthcare