Improving patient record search: A meta-data based approach

The International Classification of Diseases (ICD) is a type of meta-data found in many Electronic Patient Records. Research to explore the utility of these codes in medical Information Retrieval (IR) applications is new, and many areas of investigation remain, including the question of how reliable the assignment of the codes has been. This paper proposes two uses of the ICD codes in two different contexts of search: Pseudo-Relevance Judgments (PRJ) and Pseudo-Relevance Feedback (PRF). We find that our approach to evaluate the TREC challenge runs using simulated relevance judgments has a positive correlation with the TREC official results, and our proposed technique for performing PRF based on the ICD codes significantly outperforms a traditional PRF approach. The results are found to be consistent over the two years of queries from the TREC medical test collection

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

Explorative search of distributed bio-data to answer complex biomedical questions

Background The huge amount of biomedical-molecular data increasingly produced is providing scientists with potentially valuable information. Yet, such data quantity makes difficult to find and extract those data that are most reliable and most related to the biomedical questions to be answered, which are increasingly complex and often involve many different biomedical-molecular aspects. Such questions can be addressed only by comprehensively searching and exploring different types of data, which frequently are ordered and provided by different data sources. Search Computing has been proposed for the management and integration of ranked results from heterogeneous search services. Here, we present its novel application to the explorative search of distributed biomedical-molecular data and the integration of the search results to answer complex biomedical questions. Results A set of available bioinformatics search services has been modelled and registered in the Search Computing framework, and a Bioinformatics Search Computing application (Bio-SeCo) using such services has been created and made publicly available at http://www.bioinformatics.deib.polimi.it/bio-seco/seco/. It offers an integrated environment which eases search, exploration and ranking-aware combination of heterogeneous data provided by the available registered services, and supplies global results that can support answering complex multi-topic biomedical questions. Conclusions By using Bio-SeCo, scientists can explore the very large and very heterogeneous biomedical-molecular data available. They can easily make different explorative search attempts, inspect obtained results, select the most appropriate, expand or refine them and move forward and backward in the construction of a global complex biomedical query on multiple distributed sources that could eventually find the most relevant results. Thus, it provides an extremely useful automated support for exploratory integrated bio search, which is fundamental for Life Science data driven knowledge discovery

Fighting Cybercrime After \u3cem\u3eUnited States v. Jones\u3c/em\u3e

In a landmark non-decision last term, five Justices of the United States Supreme Court would have held that citizens possess a Fourth Amendment right to expect that certain quantities of information about them will remain private, even if they have no such expectations with respect to any of the information or data constituting that whole. This quantitative approach to evaluating and protecting Fourth Amendment rights is certainly novel and raises serious conceptual, doctrinal, and practical challenges. In other works, we have met these challenges by engaging in a careful analysis of this “mosaic theory” and by proposing that courts focus on the technologies that make collecting and aggregating large quantities of information possible. In those efforts, we focused on reasonable expectations held by “the people” that they will not be subjected to broad and indiscriminate surveillance. These expectations are anchored in Founding-era concerns about the capacity for unfettered search powers to promote an authoritarian surveillance state. Although we also readily acknowledged that there are legitimate and competing governmental and law enforcement interests at stake in the deployment and use of surveillance technologies that implicate reasonable interests in quantitative privacy, we did little more. In this Article, we begin to address that omission by focusing on the legitimate governmental and law enforcement interests at stake in preventing, detecting, and prosecuting cyber-harassment and healthcare fraud

The metric tide: report of the independent review of the role of metrics in research assessment and management

This report presents the findings and recommendations of the Independent Review of the Role of Metrics in Research Assessment and Management. The review was chaired by Professor James Wilsdon, supported by an independent and multidisciplinary group of experts in scientometrics, research funding, research policy, publishing, university management and administration. This review has gone beyond earlier studies to take a deeper look at potential uses and limitations of research metrics and indicators. It has explored the use of metrics across different disciplines, and assessed their potential contribution to the development of research excellence and impact. It has analysed their role in processes of research assessment, including the next cycle of the Research Excellence Framework (REF). It has considered the changing ways in which universities are using quantitative indicators in their management systems, and the growing power of league tables and rankings. And it has considered the negative or unintended effects of metrics on various aspects of research culture. The report starts by tracing the history of metrics in research management and assessment, in the UK and internationally. It looks at the applicability of metrics within different research cultures, compares the peer review system with metric-based alternatives, and considers what balance might be struck between the two. It charts the development of research management systems within institutions, and examines the effects of the growing use of quantitative indicators on different aspects of research culture, including performance management, equality, diversity, interdisciplinarity, and the ‘gaming’ of assessment systems. The review looks at how different funders are using quantitative indicators, and considers their potential role in research and innovation policy. Finally, it examines the role that metrics played in REF2014, and outlines scenarios for their contribution to future exercises

Using narrative evidence synthesis in HRM research: an overview of the method, its application and the lessons learned

The use of systematic approaches to evidence review and synthesis has recently become more common in the field of organizational research, yet their value remains unclear and largely untested. First used in medical research, evidence review is a technique for identifying, evaluating and synthesizing existing empirical evidence. With greater demand for the best evidence about ‘what works’ in organizational settings, nuanced approaches to evidence synthesis have evolved to address more complex research questions. Narrative synthesis is perceived to be particularly suited to evaluating diverse evidence types spanning multiple disciplinary fields, characteristic of the HRM domain. This article evaluates the narrative evidence synthesis approach, explains how it differs from other techniques and describes a worked example in relation to employee engagement. We consider its strengths, the challenges of using it and its value in HRM research

Exploring Design Characteristics of Data Trustees in Healthcare - Taxonomy and Archetypes

The use of health data can provide valuable insights for both research and industry comprising the potential to improve healthcare services and facilitate the development of innovative solutions for the healthcare sector. However, due to data protection requirements and technical challenges, access to health data is still severely inhibited. To enhance access to and utilization of health data, science and politics increasingly consider data trustee models as a conceivable solution. Yet, such concepts are still in their infancies and hardly known. At the same time, they exhibit strong differences in their design. Thus, to foster awareness about and the development of data trustee models, this study investigates their design characteristics and integrates them into a holistic taxonomy. Additionally, design patterns are explored and archetypes derived. The findings reveal that data trustee models in healthcare follow some overarching design patterns and can be assigned to four dominant archetypes

ORGANIZATIONAL BOUNDARIES, INDUSTRY FRAGMENTATION, AND ELECTRONIC PERSONAL HEALTH RECORDS

This research-in-progress paper reports on an in-depth case study that integrates literatures from health care policy, information systems, and strategic management to explore how an entrepreneurial firm shapes its organizational boundaries in the context of the complicated healthcare ecosystem in the United States. In doing so, we investigate the implications of boundary shaping decisions on the development and deployment of an electronic personal health record (ePHR) system. The growing call for the use of ePHR systems is based on the logic that providing personalized, timely healthcare information that supports an incentive-based compliance program will not only lower healthcare costs but lead to healthier individuals and improved organizational performance. However, as mentioned in this paper, populating ePHR systems is a huge data integration challenge that requires the successful coordination of many players with potentially competing objectives. By adopting the perspective of a startup firm within this context, we illuminate the impact of industry fragmentation and competing goals and objectives within the health care context, and show the importance of boundary decisions that promote cooperation and tight integration to facilitate the information flows needed to populate employer sponsored ePHR systems

Innovation dynamics and the role of infrastructure

This report shows how the role of the infrastructure – standards, measurement, accreditation, design and intellectual property – can be integrated into a quantitative model of the innovation system and used to help explain levels and changes in labour productivity and growth in turnover and employment. The summary focuses on the new results from the project, set out in more detail in Sections 5 and 6. The first two sections of the report provide contextual material on the UK innovation system, the nature and content of the infrastructure knowledge and the institutions that provide it. Mixed modes of innovation, the typology of innovation practices developed and applied here, is constituted of six mixed modes, derived from many variables taken from the UK Innovation Survey. These are: Investing in intangibles Technology with IP innovating Using codified knowledge Wider (managerial) innovating Market-led innovating External process modernising. The composition of the innovation modes, and the approach used to compute them, is set out in more detail in Section 4. Modes can be thought of as the underlying process of innovation, a bundle of activities undertaken jointly by firms, and whose working out generates well known indicators such as new product innovations, R&D spending and accessing external information, that are the partial indicators gathered from the innovation survey itself