Browsing by Author "Rocha, Luis M."
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- Challenges and opportunities for digital twins in precision medicine from a complex systems perspectivePublication . Domenico, Manlio De; Allegri, Luca; Caldarelli, Guido; d'Andrea, Valeria; Camillo, Barbara Di; Rocha, Luis M.; Rozum, Jordan; Sbarbati, Riccardo; Zambelli, FrancescoDigital twins (DTs) in precision medicine are increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. We argue that including mechanistic simulations that produce behavior based on explicitly defined biological hypotheses and multiscale mechanisms is beneficial. It enables the exploration of diverse therapeutic strategies and supports dynamic clinical decision-making through insights from network science, quantitative biology, and digital medicine.
- Focused digital cohort selection from social media using the metric backbone of biomedical knowledge graphsPublication . Guo, Ziqi; Felag, Jack; Rozum, Jordan C.; Correia, Rion Brattig; Wang, Xuan; Rocha, Luis M.Social media data allows researchers to construct large digital cohorts — groups of users who post health-related content — to study the interplay between human behavior and medical treatment. Identifying the users most relevant to a specific health problem is, however, a challenge in that social media sites vary in the generality of their discourse. While X (formerly Twitter), Instagram, and Facebook cater to wide ranging topics, Reddit subgroups and dedicated patient advocacy forums trade in much more specific, biomedically-relevant discourse. To filter relevant users on any social media, we have developed a general method and tested it on epilepsy discourse. We analyzed the text from posts by users who mention epilepsy drugs at least once in the general-purpose social media sites X and Instagram, the epilepsy-focused Reddit subgroup (r/Epilepsy), and the Epilepsy Foundation of America (EFA) forums. We used a curated medical terminology dictionary to generate a knowledge graph (KG) from each social media site, whereby nodes represent terms, and edge weights denote the strength of association between pairs of terms in the collected text. Our method is based on computing the metric backbone of each KG, which yields the (sparsified) subgraph of edges that participate in shortest paths. By comparing the subset of users who contribute to the backbone to the subset who do not, we show that epilepsy-focused social media users contribute to the KG backbone in much higher proportion than do general-purpose social media users. Furthermore, using human annotation of Instagram posts, we demonstrate that users who do not contribute to the backbone are much more likely to use dictionary terms in a manner inconsistent with their biomedical meaning and are rightly excluded from the cohort of interest. Our metric backbone approach, thus, has several benefits: it yields focused user cohorts who engage in discourse relevant to a targeted biomedical problem; unlike engagement-based approaches, it can retain low-engagement users who nonetheless contribute meaningful biomedical insights and filter out very vocal users who contribute no relevant content, it is parameter-free, algebraically principled, does not require classifiers or human-curation, and is simple to compute with the open-source code we provide.
- myAURA: a personalized health library for epilepsy management via knowledge graph sparsification and visualizationPublication . Correia, Rion Brattig; Rozum, Jordan C.; Cross, Leonard; Felag, Jack; Gallant, Michael; Guo, Ziqi; Herr II, Bruce W.; Min, Aehong; Sanchez-Valle, Jon; Rocha, Deborah Stungis; Valencia, Alfonso; Wang, Xuan; Börner, Katy; Miller, Wendy; Rocha, Luis M.Objectives: Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care. Materials and Methods: myAURA rests on an unprecedented collection of epilepsy-relevant heterogeneous data resources, such as biomedical databases, social media, and electronic health records (EHRs). We use a patient-centered biomedical dictionary to link the collected data in a multilayer knowledge graph (KG) computed with a generalizable, open-source methodology. Results: Our approach is based on a novel network sparsification method that uses the metric backbone of weighted graphs to discover important edges for inference, recommendation, and visualization. We demonstrate by studying drug-drug interaction from EHRs, extracting epilepsy-focused digital cohorts from social media, and generating a multilayer KG visualization. We also present our patient-centered design and pilot-testing of myAURA, including its user interface. Discussion: The ability to search and explore myAURA’s heterogeneous data sources in a single, sparsified, multilayer KG is highly useful for a range of epilepsy studies and stakeholder support. Conclusion: Our stakeholder-driven, scalable approach to integrating traditional and nontraditional data sources enables both clinical discovery and data-powered patient self-management in epilepsy and can be generalized to other chronic conditions.