Browsing by Subject "Data integration"
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Item Data Integration and Interoperability for Patient-Centered Remote Monitoring of Cardiovascular Implantable Electronic Devices(MDPI, 2019-03-17) Daley, Carly; Toscos, Tammy; Mirro, Michael; BioHealth Informatics, School of Informatics and ComputingThe prevalence of cardiovascular implantable electronic devices with remote monitoring capabilities continues to grow, resulting in increased volume and complexity of biomedical data. These data can provide diagnostic information for timely intervention and maintenance of implanted devices, improving quality of care. Current remote monitoring procedures do not utilize device diagnostics to their potential, due to the lack of interoperability and data integration among proprietary systems and electronic medical record platforms. However, the development of a technical framework that standardizes the data and improves interoperability shows promise for improving remote monitoring. Along with encouraging the implementation of this framework, we challenge the current paradigm and propose leveraging the framework to provide patients with their remote monitoring data. Patient-centered remote monitoring may empower patients and improve collaboration and care with health care providers. In this paper, we describe the implementation of technology to deliver remote monitoring data to patients in two recent studies. Our body of work explains the potential for developing a patent-facing information display that affords the meaningful use of implantable device data and enhances interactions with providers. This paradigm shift in remote monitoring-empowering the patient with data-is critical to using the vast amount of complex and clinically relevant biomedical data captured and transmitted by implantable devices to full potential.Item Development of a Real-Time Dashboard for Overdose Touchpoints: User-Centered Design Approach(JMIR, 2024-06-11) Salvi, Amey; Gillenwater, Logan A.; Cockrum, Brandon P.; Wiehe, Sarah E.; Christian, Kaitlyn; Cayton, John; Bailey, Timothy; Schwartz, Katherine; Dir, Allyson L.; Ray, Bradley; Aalsma, Matthew C.; Reda, Khairi; Pediatrics, School of MedicineBackground: Overdose Fatality Review (OFR) is an important public health tool for shaping overdose prevention strategies in communities. However, OFR teams review only a few cases at a time, which typically represent a small fraction of the total fatalities in their jurisdiction. Such limited review could result in a partial understanding of local overdose patterns, leading to policy recommendations that do not fully address the broader community needs. Objective: This study explored the potential to enhance conventional OFRs with a data dashboard, incorporating visualizations of touchpoints-events that precede overdoses-to highlight prevention opportunities. Methods: We conducted 2 focus groups and a survey of OFR experts to characterize their information needs and design a real-time dashboard that tracks and measures decedents' past interactions with services in Indiana. Experts (N=27) were engaged, yielding insights on essential data features to incorporate and providing feedback to guide the development of visualizations. Results: The findings highlighted the importance of showing decedents' interactions with health services (emergency medical services) and the justice system (incarcerations). Emphasis was also placed on maintaining decedent anonymity, particularly in small communities, and the need for training OFR members in data interpretation. The developed dashboard summarizes key touchpoint metrics, including prevalence, interaction frequency, and time intervals between touchpoints and overdoses, with data viewable at the county and state levels. In an initial evaluation, the dashboard was well received for its comprehensive data coverage and its potential for enhancing OFR recommendations and case selection. Conclusions: The Indiana touchpoints dashboard is the first to display real-time visualizations that link administrative and overdose mortality data across the state. This resource equips local health officials and OFRs with timely, quantitative, and spatiotemporal insights into overdose risk factors in their communities, facilitating data-driven interventions and policy changes. However, fully integrating the dashboard into OFR practices will likely require training teams in data interpretation and decision-making.Item Does a Drop-in and Case Management Model Improve Outcomes for Young Adults Experiencing Homelessness: A Case Study of YouthLink(University of Minnesota, 2022-03) Foldes, Steven S.; Long, Kirsten Hall; Piescher, Kristine; Warburton, Katelyn; Hong, Saahoon; Alesci, Nina L.This study used two approaches to examine YouthLink as an example of a drop-in and case management model for working with youth experiencing homelessness. These approaches investigated the same group of 1,229 unaccompanied youth, ages 16 to 24 and overwhelmingly Black, who voluntarily visited or received services from YouthLink in 2011. Both approaches looked at the same metrics of success over the same time period, 2011 to 2016. One approach—Study Aim 1—examined the drop-in and case management model overall, asking whether YouthLink’s service model resulted in better outcomes. It compared a YouthLink cohort with a group of highly similar youth who did not visit YouthLink but may have received similar services elsewhere. A second approach—Study Aim 2—investigated within the YouthLink cohort the ways in which YouthLink’s drop-in and case-management approach worked toward achieving the desired outcomes. The results and their implications were discussed.Item A graph-based integration of multimodal brain imaging data for the detection of early mild cognitive impairment (E-MCI)(Springer, 2013) Kim, Dokyoon; Kim, Sungeun; Risacher, Shannon L.; Shen, Li; Ritchie, Marylyn D.; Weiner, Michael W.; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of MedicineAlzheimer's disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation.Item Integration of evidence across human and model organism studies: A meeting report(Wiley, 2021-04-23) Palmer, Rohan H.C.; Johnson, Emma C.; Won, Hyejung; Polimanti, Renato; Kapoor, Manav; Chitre, Apurva; Bogue, Molly A.; Benca-Bachman, Chelsie E.; Parker, Clarissa C.; Verm, Anurag; Reynolds, Timothy; Ernst, Jason; Bray, Michael; Kwon, Soo Bin; Lai, Dongbing; Quach, Bryan C.; Gaddis, Nathan C.; Saba, Laura; Chen, Hao; Hawrylycz, Michael; Zhang, Shan; Zhou, Yuan; Mahaffey, Spencer; Fischer, Christian; Sanchez-Roige, Sandra; Bandrowski, Anita; Lu, Qing; Shen, Li; Philip, Vivek; Gelernter, Joel; Bierut, Laura J.; Hancock, Dana B.; Edenberg, Howard J.; Johnson, Eric O.; Nestler, Eric J.; Barr, Peter B.; Prins, Pjotr; Smith, Desmond J.; Akbarian, Schahram; Thorgeirsson, Thorgeir; Walton, Dave; Baker, Erich; Jacobson, Daniel; Palmer, Abraham A.; Miles, Michael; Chesler, Elissa J.; Emerson, Jake; Agrawal, Arpana; Martone, Maryann; Williams, Robert W.; Medical and Molecular Genetics, School of MedicineThe National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.Item MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework(Frontiers, 2019-06-28) Lee, Garam; Kang, Byungkon; Nho, Kwangsik; Sohn, Kyung-Ah; Kim, Dokyoon; Radiology & Imaging Sciences, IU School of MedicineAs large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. However, applying the deep learning approach requires expertise for constructing a deep architecture that can take multimodal longitudinal data. Thus, in this paper, a deep learning-based python package for data integration is developed. The python package deep learning-based multimodal longitudinal data integration framework (MildInt) provides the preconstructed deep learning architecture for a classification task. MildInt contains two learning phases: learning feature representation from each modality of data and training a classifier for the final decision. Adopting deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, linear regression classifier is used for detecting and investigating biomarkers from multimodal data. Thus, by combining the linear model and the deep learning model, higher accuracy and better interpretability can be achieved. We validated the performance of our package using simulation data and real data. For the real data, as a pilot study, we used clinical and multimodal neuroimaging datasets in Alzheimer's disease to predict the disease progression. MildInt is capable of integrating multiple forms of numerical data including time series and non-time series data for extracting complementary features from the multimodal dataset.Item Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles(Springer Nature, 2023) Morton, James T.; Jin, Dong-Min; Mills, Robert H.; Shao, Yan; Rahman, Gibraan; McDonald, Daniel; Zhu, Qiyun; Balaban, Metin; Jiang, Yueyu; Cantrell, Kalen; Gonzalez, Antonio; Carmel, Julie; Frankiensztajn, Linoy Mia; Martin-Brevet, Sandra; Berding, Kirsten; Needham, Brittany D.; Zurita, María Fernanda; David, Maude; Averina, Olga V.; Kovtun, Alexey S.; Noto, Antonio; Mussap, Michele; Wang, Mingbang; Frank, Daniel N.; Li, Ellen; Zhou, Wenhao; Fanos, Vassilios; Danilenko, Valery N.; Wall, Dennis P.; Cárdenas, Paúl; Baldeón, Manuel E.; Jacquemont, Sébastien; Koren, Omry; Elliott, Evan; Xavier, Ramnik J.; Mazmanian, Sarkis K.; Knight, Rob; Gilbert, Jack A.; Donovan, Sharon M.; Lawley, Trevor D.; Carpenter, Bob; Bonneau, Richard; Taroncher-Oldenburg, Gaspar; Anatomy, Cell Biology and Physiology, School of MedicineAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut–brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.Item Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases(Springer Nature, 2021-08-20) Kapoor, Manav; Chao, Michael J.; Johnson, Emma C.; Novikova, Gloriia; Lai, Dongbing; Meyers, Jacquelyn L.; Schulman, Jessica; Nurnberger, John I., Jr.; Porjesz, Bernice; Liu, Yunlong; Foroud, Tatiana; Edenberg, Howard J.; Marcora, Edoardo; Agrawal, Arpana; Goate, Alison; Medical and Molecular Genetics, School of MedicineIdentification of causal variants and genes underlying genome-wide association study (GWAS) loci is essential to understand the biology of alcohol use disorder (AUD) and drinks per week (DPW). Multi-omics integration approaches have shown potential for fine mapping complex loci to obtain biological insights to disease mechanisms. In this study, we use multi-omics approaches, to fine-map AUD and DPW associations at single SNP resolution to demonstrate that rs56030824 on chromosome 11 significantly reduces SPI1 mRNA expression in myeloid cells and lowers risk for AUD and DPW. Our analysis also identifies MAPT as a candidate causal gene specifically associated with DPW. Genes prioritized in this study show overlap with causal genes associated with neurodegenerative disorders. Multi-omics integration analyses highlight, genetic similarities and differences between alcohol intake and disordered drinking, suggesting molecular heterogeneity that might inform future targeted functional and cross-species studies.Item Network Models for Capturing Molecular Feature and Predicting Drug Target for Various Cancers(2020-12) Liu, Enze; Liu, Xiaowen; Wu, Huanmei; Zhang, Chi; Wan, Jun; Cao, Sha; Liu, LangNetwork-based modeling and analysis have been widely used for capturing molecular trajectories of cellular processes. For complex diseases like cancers, if we can utilize network models to capture adequate features, we can gain a better insight of the mechanism of cancers, which will further facilitate the identification of molecular vulnerabilities and the development targeted therapy. Based on this rationale, we conducted the following four studies: A novel algorithm ‘FFBN’ is developed for reconstructing directional regulatory networks (DEGs) from tissue expression data to identify molecular features. ‘FFBN’ shows unique capability of fast and accurately reconstructing genome-wide DEGs compared to existing methods. FFBN is further used to capture molecular features among liver metastasis, primary liver cancers and primary colon cancers. Comparisons among these features lead to new understandings of how liver metastasis is similar to its primary and distant cancers. ‘SCN’ is a novel algorithm that incorporates multiple types of omics data to reconstruct functional networks for not only revealing molecular vulnerabilities but also predicting drug targets on top of that. The molecular vulnerabilities are discovered via tissue-specific networks and drug targets are predicted via cell-line specific networks. SCN is tested on primary pancreatic cancers and the predictions coincide with current treatment plans. ‘SCN website’ is a web application of ‘SCN’ algorithm. It allows users to easily submit their own data and get predictions online. Meanwhile the predictions are displayed along with network graphs and survival curves. ‘DSCN’ is a novel algorithm derived from ‘SCN’. Instead of predicting single targets like ‘SCN’, ‘DSCN’ applies a novel approach for predicting target combinations using multiple omics data and network models. In conclusion, our studies revealed how genes regulate each other in the form of networks and how these networks can be used for unveiling cancer-related biological processes. Our algorithms and website facilitate capturing molecular features for cancers and predicting novel drug targets.Item Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation(Springer Nature, 2024) Carey, Caitlin E.; Shafee, Rebecca; Wedow, Robbee; Elliott, Amanda; Palmer, Duncan S.; Compitello, John; Kanai, Masahiro; Abbott, Liam; Schultz, Patrick; Karczewski, Konrad J.; Bryant, Samuel C.; Cusick, Caroline M.; Churchhouse, Claire; Howrigan, Daniel P.; King, Daniel; Smith, George Davey; Neale, Benjamin M.; Walters, Raymond K.; Robinson, Elise B.; Medical and Molecular Genetics, School of MedicineData within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.