The feasibility of a Bayesian network model to assess the probability of simultaneous symptoms in patients with advanced cancer

Stap, Lotte van der; Haaften, Myrthe F van; Marrewijk, Esther F van; Heij, Albert H de; Jansen, Paula L; Burgers, Janine M N; Sieswerda, Melle; Los, Renske K.; Reyners, Anna K.L.; Linden, Y.M. van der

doi:10.1038/s41598-022-26342-4

Cited by 3 publications

(1 citation statement)

References 47 publications

(56 reference statements)

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“…To evaluate model performance, we used the area under the receiver operating characteristic curve (AUC), mean absolute error (MAE), and mean squared error (MSE), as suggested in the literature [49,50]. The high AUC value indicates a robust overall classification accuracy between the predicted probabilities of event occurrence and the observed presences and absences.…”

Section: Bayesian Network Modeling With Only Poi Types (Bn Poi )mentioning

confidence: 99%

What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA

Yang,

Wu,

Yuan

2024

IJGI

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In-person social events bring people to places, while people and places influence where and what social events occur. Knowing what people do and where they build social relationships gives insights into the distribution and availability of places for social functions. We developed a Bayesian Network model, integrating points of interest (POIs) and sociodemographic characteristics, to estimate the probabilistic effects of places and people on the presence of social events. A case study in Dallas demonstrated the utility and performance of the model. The Bayesian Network model predicted the presence likelihoods for seven types of social events with an R2 value around 0.83 (95% confidence interval). For both the presence and absence of social events at locations, the model predictions were within a 20% error for most event types. Furthermore, the model suggested POI, age, education, and population density configurations as important contextual variables for place–event associations across locations. A spatial cluster analysis identified likely multifunctional hotspots for social events (i.e., socially vibrant places). While psychological and cultural factors likely contribute further to local likelihoods of social event occurrences, the proposed conceptually informed geospatial data-science approach elucidated intricate place–people–event relationships and implicates inclusive, participatory places for urban development.

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Section: Bayesian Network Modeling With Only Poi Types (Bn Poi )mentioning

confidence: 99%

What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA

Yang,

Wu,

Yuan

2024

IJGI

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Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review

Doppenberg-Smit,

Lamers,

van Linde

et al. 2024

J Cancer Surviv

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Purpose Patients with cancer often experience multiple somatic and psychological symptoms. Somatic and psychological symptoms are thought to be connected and may reinforce each other. Network analysis allows examination of the interconnectedness of individual symptoms. The aim of this scoping review was to examine the current state of knowledge about the associations between somatic and psychological symptoms in patients with cancer and cancer survivors, based on network analysis. Methods This scoping review followed the five-stage framework of Arksey and O’Malley. The literature search was conducted in May, 2023 in PubMed, APA PsycINFO, Embase Cochrane central, and CINAHL databases. Results Thirty-two studies were included, with eleven using longitudinal data. Seventeen studies reported on the strength of the associations: somatic and psychological symptoms were associated, although associations among somatic as well as among psychological symptoms were stronger. Other findings were the association between somatic and psychological symptoms was stronger in patients experiencing more severe symptoms; associations between symptoms over time remained rather stable; and different symptoms were central in the networks, with fatigue being among the most central in half of the studies. Implications for Cancer Survivors Although the associations among somatic symptoms and among psychological symptoms were stronger, somatic and psychological symptoms were associated, especially in patients experiencing more severe symptoms. Fatigue was among the most central symptoms, bridging the somatic and psychological domain. These findings as well as future research based on network analysis may help to untangle the complex interplay of somatic and psychological symptoms in patients with cancer.

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Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi

Filigheddu,

Leonelli,

Varando

et al. 2024

Computational and Structural Biotechnology Journal

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The feasibility of a Bayesian network model to assess the probability of simultaneous symptoms in patients with advanced cancer

Cited by 3 publications

References 47 publications

What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA

What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA

Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review

Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi

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