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Background: Diagnostic reasoning requires clinicians to think through complex uncertainties. We tested the possibility of a bias toward an available single diagnosis in uncertain cases. Design: We developed 5 different surveys providing a succinct description of a hypothetical individual patient scenaric. Each scenario was formulated in 2 versions randomized to participants, with the versions differing only in whether an alternative diagnosis was present or absent. The 5 scenarios were designed as separate tests of robustness using diverse cases, including a cautious scenario, a risky scenario, a sophisticated scenario, a validation scenario, and a comparative scenario (each survey containing only 1 version of 1 scenario). Participants included community members (n = 1104) and health care professionals (n = 200) who judged the chances of COVID infection in an individual patient. Results: The first scenario described a cautious patient and found a 47% reduction in the estimated odds of COVID when a flu diagnosis was present compared with absent (odds ratio = 0.53, 95% confidence interval 0.30 to 0.94, P = 0.003). The second scenario described a less cautious patient and found a 70% reduction in the estimated odds of COVID in the presence of a flu diagnosis (odds ratio = 0.30, 95% confidence interval 0.13 to 0.70, P < 0.001). The third was a more sophisticated scenario presented to medical professionals and found a 73% reduction in the estimated odds of COVID in the presence of a mononucleosis diagnosis (odds ratio = 0.27, 95% confidence interval 0.10 to 0.75, P < 0.001). Two further scenarios—avoiding mention of population norms—replicated the results. Limitations: Brief hypothetical scenarios may overestimate the extent of bias in more complicated medical situations. Conclusions: These results demonstrate that an available simple diagnosis can lead individuals toward premature closure and a failure to fully consider additional severe diseases. Highlights: Occum's razor has been debated for centuries yet rarely subjected to experimental testing for evidence-based medicine. This article offers direct evidence that people favor an available simple diagnosis, thereby neglecting to consider additional serious diseases. The bias can lead individuals to mistakenly lower their judged likelihood of COVID or another disease when an alternate diagnosis is present. This misconception over the laws of probability appears in judgments by community members and by health care workers. The pitfall in reasoning extends to high-risk cases and is not easily attributed to information, incentives, or random chance. [ABSTRACT FROM AUTHOR]

Abstract Background China is one of the most rapidly ageing countries and has the largest ageing population in the world. The demand for long-term care is increasing. Nursing home placement is one of the most stressful events in a person’s life. Although research on relocation adjustment has been conducted in many countries, few studies have been related to the predictors of nursing home adjustment in mainland China. This study aimed to identify the predictors of nursing home adjustment in the context of filial piety in mainland China. Methods This was a descriptive study that employed a cross-sectional survey. A total of 303 residents from 22 nursing homes in Nanjing, China, were recruited. A structured questionnaire about residents’ characteristics, activities of daily living, social support, resilience, and nursing home adjustment was administered. Multiple linear regression was used to identify the predictors of adaptation to nursing homes. Results The predictors of nursing home adjustment were the satisfaction with services(β = .158, P

High throughput analysis and large scale integration of biological data led to leading researches in the field of bioinformatics. Recent years witnessed the development of various methods for disease associated gene prediction and disease comorbidity predictions. Most of the existing techniques use network-based approaches and similarity-based approaches for these predictions. Even though network-based approaches have better performance, these methods rely on text data from OMIM records and PubMed abstracts. In this method, a novel algorithm (HDCDGP) is proposed for disease comorbidity prediction and disease associated gene prediction. Disease comorbidity network and disease gene network were constructed using data from gene ontology (GO), human phenotype ontology (HPO), protein-protein interaction (PPI) and pathway dataset. Modified random walk restart algorithm was applied on these networks for extracting novel disease- gene associations. Experimental results showed that the hybrid approach has better performance compared to existing systems with an overall accuracy around 85%. [ABSTRACT FROM AUTHOR]

Abstract Background Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set. Methods We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients. Results We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease). Conclusions Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.

There is an urgent need in advanced dementia for evidence-based clinical prognostic predictors that could positively influence ethical decisions allowing health provider and family preparation for early mortality. Accordingly, the authors review and discuss the prognostic utility of clinical assessments and objective measures of pathological brain states in advanced dementia patients associated with accelerated mortality. Overall, due to the paucity of brain-activity and clinical-comorbidity predictors of survival in advanced dementia, authors outline the potential prognostic value of brain-state electroencephalography (EEG) measures and reliable clinical indicators for forecasting early mortality in advanced dementia patients. In conclusion, two consistent risk-factors for predicting accelerated mortality in terminal-stage patients with advanced dementia were identified: pressure ulcers and paroxysmal slow-wave EEG parameters associated with cognitive impairment severity and organic disease progression. In parallel, immobility, malnutrition, and co-morbid systemic diseases are highly associated with the risk for early mortality in advanced dementia patients. Importantly, the authors' conclusions suggest utilizing reliable quantitative-parameters of disease progression for estimating accelerated mortality in dementia patients entering the terminal disease-stages characterized by severe intellectual deficits and functional disability. [ABSTRACT FROM AUTHOR]

Understanding disease comorbidity contributes to improved quality of life in patients who are suffering from multiple diseases. Therefore, to better explore comorbid diseases, the clarification of associations between diseases based on biological functions is essential. In our study, we propose a method for identifying disease comorbidity in a module-based network, named the module–module interaction (MMI) network, which represents how biological functions influence each other. To construct the MMI network, we detected gene modules — sets of genes that have a higher probability of taking part in specific functions — and established a link between these modules. Subsequently, we constructed disease-related networks in the MMI network to understand inherent disease mechanisms and calculated comorbidity scores of disease pairs using Gene Ontology (GO) terms. Our results show that we can obtain further information on disease mechanisms by considering interactions between functional modules instead of between genes. In addition, we verified that predicted comorbid relationships of disease pairs based on the MMI network are more significant than those based on the protein–protein interaction (PPI) network. This study can be useful to elucidate the mechanisms underlying comorbidities for further study, which will provide a broader insight into the pathogenesis of diseases. [ABSTRACT FROM AUTHOR]

Advancements in bio-techniques have accelerated the generation of genomic and proteomic data. High throughput experiments generate huge volume of biological data that lead to cutting edge researches. These data may be stored in hierarchical structures known as ontologies. Ontologies serve as rich knowledge sources for biological information mining. Annotations from Gene Ontology (GO), Human Phenotype Ontology (HPO) and Drug Ontology are extensively used for biological studies including disease causing gene prediction, disease comorbidity analysis and drug designing purposes. Several mechanisms exist for extracting semantic similarity among ontological terms. Graph based approaches are mostly used for analyzing the semantic similarity between ontological terms. In this paper, a novel method has been proposed for predicting disease causing genes and disease comorbidities using GO and HPO data. Weighted association rules obtained from Gene Ontology and Human Phenotype Ontology are used for constructing weighted gene network and weighted phenotype network. These networks are then connected using known gene-phenotypic relationships. Modified random walk restart algorithm is performed on this network for extracting novel disease gene correlations and disease comorbidities. This method outperformed the existing methods that depend on similarity measurements. [ABSTRACT FROM AUTHOR]

Background: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set. Methods: We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients. Results: We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease). Conclusions: Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

The genetic basis of complex diseases involves alterations on multiple genes. Unraveling the interplay between these genetic factors is key to the discovery of new biomarkers and treatments. In 2014, we introduced GUILDify, a web server that searches for genes associated to diseases, finds novel disease genes applying various network-based prioritization algorithms and proposes candidate drugs. Here, we present GUILDify v2.0, a major update and improvement of the original method, where we have included protein interaction data for seven species and 22 human tissues and incorporated the disease–gene associations from DisGeNET. To infer potential disease relationships associated with multi-morbidities, we introduced a novel feature for estimating the genetic and functional overlap of two diseases using the top-ranking genes and the associated enrichment of biological functions and pathways (as defined by GO and Reactome). The analysis of this overlap helps to identify the mechanistic role of genes and protein–protein interactions in comorbidities. Finally, we provided an R package, guildifyR, to facilitate programmatic access to GUILDify v2.0 (http://sbi.upf.edu/guildify2) Unlabelled Image • GUILDify v2.0 is a web server to prioritize genes using protein interaction networks. • Contains up-to-date data on protein interactions, disease genes and drug targets • Introduces tissue-specific interaction networks and functional enrichment analyses • Identifies potential genetic and functional relationships between drugs and diseases • Provides an easy-to-use interface for comorbidity and drug repurposing research [ABSTRACT FROM AUTHOR]

Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.

Pulmonary hypertension (PH) is caused by many disorders that affect the pulmonary vasculature. A recent study has provided evidence that pulmonary vascular remodeling and PH can be observed in lung cancer, and this may be associated with tumor cell–immune cell inflammatory crosstalk. These findings highlight the pressing need to understand better and manage pulmonary vascular comorbidities in lung cancer. [ABSTRACT FROM AUTHOR]

• Develop models to predict comorbidity and multimorbidity of major chronic diseases. • Apply network analytics to extract features from patient networks. • XGBoost showed the best accuracy for chronic disease comorbidity and multimorbidity. • Demonstrate an important use of administrative claim data. The prevalence of chronic disease comorbidity and multimorbidity is a significant health issue worldwide. In many cases, for individuals, the occurrence of one chronic disease leads to the development of one or more other chronic conditions. This exerts a significant burden on healthcare systems globally. Disease comorbidity is defined as the simultaneous occurrence of more than one disease. And a person having more than two comorbidities is referred to as multimorbid. This study followed a machine learning and network analytics-based approach to predict major chronic disease comorbidity and multimorbidity. In doing so, this study first extracted patient networks from the research dataset. In such networks, nodes represent patients and edges between two nodes indicate that the underlying two patients had at least one common disease. This study also considered other relevant features from patients' health trajectories. Out of the five machine learning models considered in this study (Logistic regression, k -nearest neighbours, Naïve Bayes, Random Forest and Extreme Gradient Boosting) and two deep learning models (Multilayer perceptrons and Convolutional neural networks), Extreme Gradient Boosting showed the highest accuracy (95.05%), followed by the Convolutional neural networks (91.67%). The attribute of the number of episodes from the patient trajectory had been found as the most important feature, followed by the patient network attribute of transitivity. Other relevant results (feature correlation, variable clustering, confusion matrix and kernel density estimation) were also reported and discussed. The findings and insights of this study can help healthcare stakeholders and policymakers mitigate the negative impact of disease comorbidity and multimorbidity. [ABSTRACT FROM AUTHOR]

Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent, and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction (PPI) network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWAS) have provided a large set of genetic loci influencing the risk for many common diseases. Association studies typically analyze one specific trait in single populations in an isolated fashion without taking into account the potential phenotypic and genetic correlation between traits. However, GWA data can be efficiently used to identify overlapping loci with analogous or contrasting effects on different diseases. Results: Here, we describe a new approach to systematically prioritize and interpret available GWA data. We focus on the analysis of joint and disjoint genetic determinants across diseases. Using network analysis, we show that variant-based approaches are superior to locus-based analyses. In addition, we provide a prioritization of disease loci based on network properties and discuss the roles of hub loci across several diseases. We demonstrate that, in general, agonistic associations appear to reflect current disease classifications, and present the potential use of effect sizes in refining and revising these agonistic signals. We further identify potential branching points in disease etiologies based on antagonistic variants and describe plausible small-scale models of the underlying molecular switches. Conclusions: The observation that a surprisingly high fraction (>15%) of the SNPs considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. Genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further. [ABSTRACT FROM AUTHOR]

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: China is one of the most rapidly ageing countries and has the largest ageing population in the world. The demand for long-term care is increasing. Nursing home placement is one of the most stressful events in a person's life. Although research on relocation adjustment has been conducted in many countries, few studies have been related to the predictors of nursing home adjustment in mainland China. This study aimed to identify the predictors of nursing home adjustment in the context of filial piety in mainland China.Methods: This was a descriptive study that employed a cross-sectional survey. A total of 303 residents from 22 nursing homes in Nanjing, China, were recruited. A structured questionnaire about residents' characteristics, activities of daily living, social support, resilience, and nursing home adjustment was administered. Multiple linear regression was used to identify the predictors of adaptation to nursing homes.Results: The predictors of nursing home adjustment were the satisfaction with services(β = .158, P < .01), number of diseases(β = -.091, P < .05), length of stay(β = .088, P < .05), knowledge of the purpose of admission (β = .092, P < .05), resilience(β = .483, P < .001) and social support(β = .186, P < .001). The total explained variance for this model was 61.6%.Conclusion: Nursing staff members should assess the characteristics of residents to promote their better adjustment. Resilience had the most significant influence on the level of adaptation, which has been the primary focus of interventions to improve adjustment. The management of disease comorbidities in nursing homes should be standardized and supervised by the government. More volunteers from universities and communities should be encouraged to provide social support to residents. Moreover, a caring culture needs to be emphasized, and the value of filial piety should be advocated in nursing homes of East Asian countries. [ABSTRACT FROM AUTHOR]

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic., Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity., Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores., Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)