A Model for Hospital Discharge Preparation: From Case Management to Care Transition

There has been a proliferation of initiatives to improve discharge processes and outcomes for the transition from hospital to home and community-based care. Operationalization of these processes has varied widely as hospitals have customized discharge care into innovative roles and functions. This article presents a model for conceptualizing the components of hospital discharge preparation to ensure attention to the full range of processes needed for a comprehensive strategy for hospital discharge

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset

Identifying Outcomes of Care from Medical Records to Improve Doctor-Patient Communication

Between appointments, healthcare providers have limited interaction with their patients, but patients have similar patterns of care. Medications have common side effects; injuries have an expected healing time; and so on. By modeling patient interventions with outcomes, healthcare systems can equip providers with better feedback. In this work, we present a pipeline for analyzing medical records according to an ontology directed at allowing closed-loop feedback between medical encounters. Working with medical data from multiple domains, we use a combination of data processing, machine learning, and clinical expertise to extract knowledge from patient records. While our current focus is on technique, the ultimate goal of this research is to inform development of a system using these models to provide knowledge-driven clinical decision-making

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Improving Business Performance Through The Integration Of Human Factors Engineering Into Organizations Using A Systems Engineeri

Most organizations today understand the valuable contribution employees as people (rather than simply bodies) provide to their overall performance. Although efforts are made to make the most of the human in organizations, there is still much room for improvement. Focus in the reduction of employee injuries such as cumulative trauma disorders rose in the 80 s. Attempts at increasing performance by addressing employee satisfaction through various methods have also been ongoing for several years now. Knowledge Management is one of the most recent attempts at controlling and making the best use of employees knowledge. All of these efforts and more towards that same goal of making the most of people s performance at work are encompassed within the domain of the Human Factors Engineering/Ergonomics field. HFE/E provides still untapped potential for organizational performance as the human and its optimal performance are the reason for this discipline s being. Although Human Factors programs have been generated and implemented, there is still the need for a method to help organizations fully integrate this discipline into the enterprise as a whole. The purpose of this research is to develop a method to help organizations integrate HFE/E into it business processes. This research begun with a review of the ways in which the HFE/E discipline is currently used by organizations. The need and desire to integrate HFE/E into organizations was identified, and a method to accomplish this integration was conceptualized. This method consisted on the generation of two domain-specific ontologies (a Human Factors Engineering/Ergonomics ontology, and a Business ontology), and mapping the two creating a concept map that can be used to integrate HFE/E into businesses. The HFE/E ontology was built by generating two concept maps that were merged and then joined with a HFE/E discipline taxonomy. A total of four concept maps, two ontologies and a taxonomy were created, all of which are contributions to the HFE/E, and the business- and management-related fields

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

On the integration of trust with negotiation, argumentation and semantics

Agreement Technologies are needed for autonomous agents to come to mutually acceptable agreements, typically on behalf of humans. These technologies include trust computing, negotiation, argumentation and semantic alignment. In this paper, we identify a number of open questions regarding the integration of computational models and tools for trust computing with negotiation, argumentation and semantic alignment. We consider these questions in general and in the context of applications in open, distributed settings such as the grid and cloud computing. © 2013 Cambridge University Press.This work was partially supported by the Agreement Technology COST action (IC0801). The authors would like to thank for helpful discussions and comments all participants in the panel on >Trust, Argumentation and Semantics> on 16 December 2009, Agia Napa, CyprusPeer Reviewe