Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective

Objectives: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms “semantic integration" and “knowledge representation“. This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs). Methods: We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles. Results: We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT. Conclusions: We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.

Download Full-text

Clinical research data management in the United States: Where we've been and where we're going

Journal of the Society for Clinical Data Management ◽

10.47912/jscdm.61 ◽

2021 ◽

Author(s):

Mary Banach ◽

Kaye H Fendt ◽

Johann Proeve ◽

Dale Plummer ◽

Samina Qureshi ◽

...

Keyword(s):

Clinical Research ◽

Data Management ◽

Clinical Data ◽

Management Practices ◽

The United States ◽

Research Data ◽

Fda Guidance ◽

Research Data Management ◽

Challenges And Opportunities ◽

Clinical Research Data

With the focus of the COVID-19 pandemic, we wanted to reach all stakeholders representing communities concerned with good clinical data management practices. We wanted to represent not only data managers but bio-statisticians, clinical monitors, data scientists, informaticians, and all those who collect, organize, analyze, and report on clinical research data. In our paper we will discuss the history of clinical data management in the US and its evolution from the early days of FDA guidance. We will explore the role of biomedical research focusing on the similarities and differences in industry and academia clinical research data management and what we can learn from each other. We will talk about our goals for recruitment and training for the CDM community and what we propose for increasing the knowledge and understanding of good clinical data practice to all – particularly our front-line data collectors i.e., nurses, medical assistants, patients, other data collectors. Finally, we will explore the challenges and opportunities to see CDM as the hub for good clinical data research practices in all of our communities.We will also discuss our survey on how the COVID-19 pandemic has affected the work of CDM in clinical research.

Download Full-text

Sharing Privacy Protected and Statistically Sound Clinical Research Data Using Outsourced Data Storage

Journal of Applied Mathematics ◽

10.1155/2014/381361 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Geontae Noh ◽

Ji Young Chun ◽

Ik Rae Jeong

Keyword(s):

Clinical Research ◽

Data Storage ◽

Clinical Data ◽

Private Information ◽

Homomorphic Encryption ◽

Scientific Progress ◽

Research Data ◽

Computing Services ◽

Public Keys ◽

Clinical Research Data

It is critical to scientific progress to share clinical research data stored in outsourced generally available cloud computing services. Researchers are able to obtain valuable information that they would not otherwise be able to access; however, privacy concerns arise when sharing clinical data in these outsourced publicly available data storage services. HIPAA requires researchers to deidentify private information when disclosing clinical data for research purposes and describes two available methods for doing so. Unfortunately, both techniques degrade statistical accuracy. Therefore, the need to protect privacy presents a significant problem for data sharing between hospitals and researchers. In this paper, we propose a controlled secure aggregation protocol to secure both privacy and accuracy when researchers outsource their clinical research data for sharing. Since clinical data must remain private beyond a patient’s lifetime, we take advantage of lattice-based homomorphic encryption to guarantee long-term security against quantum computing attacks. Using lattice-based homomorphic encryption, we design an aggregation protocol that aggregates outsourced ciphertexts under distinct public keys. It enables researchers to get aggregated results from outsourced ciphertexts of distinct researchers. To the best of our knowledge, our protocol is the first aggregation protocol which can aggregate ciphertexts which are encrypted with distinct public keys.

Download Full-text