Browsing by Subject "Biobanks"

Now showing 1 - 7 of 7
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    Autonomy and consent in biobanks
    (2010-02) Schwartz, Peter H.
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    Banking the Future: Adolescent Capacity to Consent to Biobank Research
    (Wiley, 2019) McGregor, Kyle A.; Ott, Mary A.; Pediatrics, School of Medicine
    Adolescents are an important population to represent in biobanks. Inclusion of biospecimens from adolescents advances our understanding of the long-term consequences of pediatric disease and allows the discovery of methods to prevent adult diseases during childhood. Consent for biobanking is complex, especially when considering adolescent participation, as it brings up issues that are not present with general clinical research. The development and successful implementation of an adolescent capacity assessment tool applied specifically to biobanking can potentially provide researchers and clinicians with contextualized information on participants' understanding, appreciation, reasoning, and voluntary choice for biobanks. This tool would enhance current studies looking at the role of shared decision-making in biobanking, as well as provide a formal measurement when considering decisions around pediatric and adolescent biobanking participation. This study adapted the MacCAT-CR for use with a hypothetical adolescent biobank study and examines predictors of MacCAT-CR scores on healthy and chronically ill adolescents.
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    Biobanks and Electronic Health Records: Ethical and Policy Challenges in the Genomic Age
    (IU Center for Applied Cybersecurity Research, 2009-10) Meslin, Eric M.; Goodman, Kenneth
    In this paper we discuss the ethical and policy challenges presented by the construction and use of biobanks and electronic health records systems, with a particular focus on how these resources implicate certain types of security concerns for patients, families, health care providers and institutions. These two technology platforms are selected for special emphasis in this paper for two reasons. First and foremost, there is a close connection between them. Indeed, of the many accepted definitions, this one from the German National Bioethics Commission provides a sense of this close connection and the great power and reflects the great power these two separate platforms provide to probe more deeply the connection between genotype and phenotype: "...[B]iobanks are defined as collections of samples of human bodily substances (e.g., cells, tissues, blood or DNA as the physical medium of genetic information) that are or can be associated with personal data and information on their donors." Second, these two topics implicate both clinical ethics issues (those arising at the bedside for health care providers and patients), and human research ethics issues (issues arising for scientists, research subjects, ethics review bodies and regulatory authorities). Both of these sub-specialty areas confront similar and complementary ethical issues; for example, issues arising from the nature and adequacy of informed consent, the sufficiency of systems to protect personal privacy and confidentiality, or the need to balance concerns relating to data security and the need to know. A growing research base supports calls for more attention to these issues, and yet current professional ethics frameworks and policy consultation methods are poorly organized and ill-equipped to anticipate and fully address ethical issues in health information technology generally, or to provide adequate ethical assessment of the tools that elicit these issues. Our strategy is to orient readers to the history and context of these issues, to frame several key challenges for researchers and policy makers, and then to close with several recommendations for next steps.
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    Cardioinformatics Advancements in Healthcare and Biotechnology
    (American Heart Association, 2023) Khomtchouk, Bohdan B.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
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    A proposal for comprehensive biobank research laws to promote translational medicine in Indiana
    (Indiana University, 2008) Girod, Jennifer; Drabiak, Katherine
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    Report from the PredictER Expert Panel Meeting, November 2, 2007
    (2008-10-27T16:44:59Z) Barrett, Patrick R.; Meslin, Eric M.; Schwartz, Peter H.; Girod, Jennifer; Odell, Jere D.; Quaid, Kimberly; Wolf, James G.
    On November 2, 2007, the Indiana University Center for Bioethics convened an expert panel on predictive health research (PHR) as part of the Center’s Program in Predictive Health Ethics Research (http://www.bioethics.iu.edu/predicter.asp) which is supported by a grant from the Richard M. Fairbanks Foundation. The goal of this meeting was to identify the major obstacles and opportunities for engaging the community in PHR. PredictER intends to use the results of this meeting as a first step toward more fully engaging the Indianapolis community in discussions about PHR.
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    Utilizing multimodal AI to improve genetic analyses of cardiovascular traits
    (medRxiv, 2024-03-20) Zhou, Yuchen; Cosentino, Justin; Yun, Taedong; Biradar, Mahantesh I.; Shreibati, Jacqueline; Lai, Dongbing; Schwantes-An, Tae-Hwi; Luben, Robert; McCaw, Zachary; Engmann, Jorgen; Providencia, Rui; Schmidt, Amand Floriaan; Munroe, Patricia; Yang, Howard; Carroll, Andrew; Khawaja, Anthony P.; McLean, Cory Y.; Behsaz, Babak; Hormozdiari, Farhad; Medical and Molecular Genetics, School of Medicine
    Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.