scholarly journals Comparative Study of Adaptive Consensus Control of Euler-Lagrange Systems on Directed Network Graph

2017 ◽  
Vol 2 (3) ◽  
pp. 1165-1171
Author(s):  
Yoshihiko Miyasato
Keyword(s):  
Comparative Study ◽  
Directed Network ◽  
Network Graph ◽  
Consensus Control

Abstract P214: Social Network Analysis of a Community Aphasia Group Pre- And Post-Covid: Implications for Future Planning

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Tom Sather ◽  
Anna Livera
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Time Frame ◽  
Directed Network ◽  
Negative Consequences ◽  
Network Graph ◽  
Future Planning ◽  
Network Characteristics ◽  
The Impact

Introduction: Among the many negative consequences of aphasia is an altered social network. Social network analysis supports an objective, quantitative evaluation of social networks among individuals with aphasia along with potential impacts of social programming and interventions on an individual’s social network. Social network analysis may also support better understanding of the impact of Covid on individuals with aphasia. Aims: This pilot evaluation utilized social network analysis via R to evaluate the social network characteristics of a community-based aphasia network across a 12-month pre-Covid period. Social network aphasia group data for a standard duration of time pre- and post-Covid were also compared to identify potential social implications of Covid in a population already at higher risk for reduced social interactions. This presentation will also provide fundamental concepts relevant to social network analysis for those interested in pursuing such analysis in further depth. Methods: Twelve months of pre-Covid aphasia group program attendance data were examined using the visNetwork R package. An additional six months of Covid-era time frame data were also analyzed.The primary relationship function of “ a attended b” (where a = individual participant and b = event/setting) was used in the analysis. Multiple social network characteristics were analyzed and displayed including node, edgeness, directionality, weight, and centrality indices across individuals with aphasia, care partners and community members and settings. Results and Conclusions: Network analysis reveals a directed network graph with primarily unidirectional relationships. There is an emergence of several aphasia group participant behavior types, both pre- and post-Covid, relevant for future planning including: communities of individuals who have similar behaviors in terms of type of event attendance; key individuals who are "heavy users" of various services in terms of frequency and breadth of event attendance; and peripheral users who use only one service. Post-Covid social network implications are discussed including supports to mitigate negative impacts of Covid on social network composition.

scholarly journals Premagic and Ideal Flow Matrices

2019 ◽  
Vol 3 (1) ◽  
pp. 35-45
Author(s):  
Kardi Teknomo
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Directed Graph ◽  
Directed Network ◽  
Network Graph ◽  
Ideal Flow ◽  
Strongly Connected

Several interesting properties of a special type of matrix that has a row sum equal to the column sum are shown with the proofs. Premagic matrix can be applied to strongly connected directed network graph due to its nodes conservation flow. Relationships between Markov Chain, ideal flow and random walk on directed graph are also discussed.

Characterization of temporal relationships of comorbidities developed following cancer diagnoses in veterans.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18049-e18049
Author(s):  
Julian C. Hong ◽  
Elizabeth R. Hauser ◽  
Thomas S. Redding ◽  
Kellie J. Sims ◽  
Ziad F Gellad ◽  
...  
Keyword(s):  
Urethral Stricture ◽  
Carcinoma In Situ ◽  
Screening Colonoscopy ◽  
Directed Network ◽  
Computational Techniques ◽  
Network Graph ◽  
Future Health ◽  
Electronic Health Record Data ◽  
Temporal Relationships

e18049 Background: Understanding patient trajectories and common sequences of comorbidity accrual among those newly diagnosed with cancer is critical for precision approaches to care and prevention. The Veterans Affairs (VA) Cooperative Studies Program (CSP) #380 cohort includes 3,121 healthy asymptomatic veterans who underwent screening colonoscopy and were followed for at least ten years. The current analysis leverages computational approaches to characterize the temporal relationships of diagnoses in CSP #380 participants following diagnosis of colorectal or other cancers. Methods: Patients enrolled in CSP #380 with at least 5 years of linked electronic health record data from the VA Corporate Data Warehouse (October 1999-December 2015) were included. Cancer diagnoses and their most common subsequent new diagnoses were identified per patient by the first instance of each three-digit ICD-9 diagnosis affecting at least 50 patients. Pairwise chronological relative risks (RR) between subsequent diagnoses were represented as a directed network graph, which maps the probability of developing a diagnosis following a prior diagnosis. Results: A total of 2,210 patients were included. The most common cancer diagnoses were prostate (436), thoracic (169), bladder (120), colon (72), and kidney cancers (65). Most first diagnoses following a cancer diagnosis were related to progressive cancer or acute/subacute treatment toxicity. For prostate cancer, comorbidities with greatest RR were carcinoma in situ (RR 6.85), unspecified (NOS) metastases (2.75), and urethral stricture (2.53). For lung cancer, they were metastases of respiratory and digestive sites (12.24), lymph nodes (6.47), and NOS (5.68), pneumothorax and air leak (4.16), and convalescence and palliative care (3.07). In bladder cancer, they were carcinoma in situ (9.00), cystitis (6.78), kidney or other urinary cancer (6.19), attention to artificial openings (3.40), and urethral stricture (2.78). These and other results were visualized with network graphs. Conclusions: Computational techniques can identify and visualize future health concerns following cancer diagnoses. In this cohort of initially healthy and asymptomatic veterans on a prospective screening colonoscopy study, most subsequent diagnoses were related to cancer or toxicities of therapy, as might be expected in an aging cohort. Future work may focus on streamlining in-clinic identification of potential high likelihood comorbidities.

scholarly journals Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Julian C. Hong ◽  
Elizabeth R. Hauser ◽  
Thomas S. Redding ◽  
Kellie J. Sims ◽  
Ziad F. Gellad ◽  
...  
Keyword(s):  
Kidney Failure ◽  
Directed Graphs ◽  
Screening Colonoscopy ◽  
Directed Network ◽  
Preventative Care ◽  
Network Graph ◽  
Prospective Longitudinal Study ◽  
Personalized Care ◽  
Screening And Surveillance ◽  
Prospective Longitudinal

AbstractUnderstanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care.

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