Bayesian Network Analysis Research Articles

Evaluation of pitolisant, sodium oxybate, solriamfetol, and modafinil for the management of narcolepsy: a retrospective analysis of the FAERS database

ObjectiveNarcolepsy, a rare neurological disorder believed to have an autoimmune etiology, necessitates lifelong management. This study aimed to provide evidence supporting the safety of pharmacological treatment for narcolepsy.MethodsFive-year data on pitolisant, sodium oxybate, solriamfetol, and modafinil were extracted from the FDA Adverse Event Reporting System (FAERS) self-reporting database for the period spanning from 2019 to 2023. Various statistical methods, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network analysis (BCPNN), and multi-item gamma Poisson shrinker (MGPS), were employed to quantify the signals. Finally, a comparative analysis was conducted between demographic data, outcomes, and inherent associations among the medications and the signals.ResultsAfter data analysis, we obtained 50 signals (a cumulative count of 762 cases) for pitolisant, 640 signals (corresponding to 46,962 cases) for sodium oxybate, 40 signals (equivalent to 1,228 cases) for solriamfetol, and finally, 72 signals (representing 632 cases) for modafinil. The majority of these patients were female. Psychiatric and nervous system disorders were identified as the predominant adverse drug events (ADEs). For sodium oxybate, it is crucial to consider psychiatric disorders (such as suicidal ideation), respiratory disorders (including sleep apnea syndrome and respiratory depression), and signs of pregnancy and congenital familial diseases. For solriamfetol, noteworthy new ADEs include drug inefficacy, suicidal ideation, restless legs syndrome, and somnambulism. Furthermore, a relationship has been observed between modafinil use and restricted fetal growth, spontaneous abortion, cognitive disorders, and drug inefficacy and abuse.ConclusionThe majority of observed adverse reactions in this study were consistent with those listed in the product instructions. However, potential novel or notable ADE signals were identified through real-world pharmacovigilance analysis. It is anticipated that this paper will offer additional information regarding safe and rational medication for narcolepsy.

Resilience, job satisfaction, occupational stress, and occupational accidents among healthcare professionals: A Bayesian network analysis.

Occupational accidents remain a critical challenge for healthcare professionals. In the present study, using the Bayesian network (BN) approach association among resilience, job satisfaction, stress, and occupational accidents among healthcare professionals is examined. Data was gathered using several valid questionnaires. The BN approach was utilized to analyze the r5/31/2024ionships between the variables of the current study. The performance of BN analysis was evaluated using related indexes. In total, 300 healthcare professionals participated in this study. Results showed that almost 23% of healthcare professionals had experienced occupational accidents. Results of the sensitivity analysis demonstrated that job satisfaction had the most significant influence on occupational accidents in healthcare settings. The belief updating analysis results showed that by increasing job satisfaction and decreasing stress of healthcare professionals the occurrence of occupational accidents decreased 9.8% and 6.4%, respectively. Moreover, decreasing the stress of healthcare professionals can lead to an increase in the level of job satisfaction. Evaluation indexes showed that the performance of the developed BN was acceptable (error rate: 16.09). The Findings reveal that both job satisfaction and stress had a significant influence on occupational accidents in healthcare professionals. Moreover, by influencing job satisfaction and stress, resilience can indirectly affect occupational accidents.

Socio-economic factors boosting the effectiveness of marine protected areas: A Bayesian network analysis

Socio-economic factors boosting the effectiveness of marine protected areas: A Bayesian network analysis

Unveiling the impact of siren noise exposure on cognitive function and mental health among firefighters

Firefighters reside in inherently noisy environments with sirens and emergency alarms. This noise can negatively affect the performance of firefighters and further pose a safety risk. This study investigates the relationship between siren noise exposure and its mental health disorders. In doing so, the study focuses on cognitive function among firefighters, using a Bayesian network model for the first time. This case-control study involved 92 firefighters and was conducted in 2023. Participants were categorized into case and control groups based on their exposure levels to siren noise. Noise exposures were initially measured according to ISO 9612 standards. The questionnaires covered demographic information, scales of their depression, anxiety, and stress, the Buss-Perry aggression questionnaire, and the cognitive failure questionnaire. GeNIe academic software version 2.3 was employed for Bayesian network analysis. The study results indicated a strong association that heightened siren noise exposure with a 100% probability increases depression (10%), stress (14%), anxiety (13%), and aggression (16%). In addition, each variable–noise exposure, depression, stress, anxiety, and aggression was linked to cognitive failure by 8%, 28%, 8%, and 13%, respectively. The combination of variables including noise exposure, depression, anxiety, and other mental health disorders had a 57% increase in cognitive failure. The most influence values were observed between depression and cognitive failures (0.564). The results of this study demonstrated the effect of siren noise exposure and its mental health disorders on cognitive function among firefighters. The results further highlight proper implementation of measures to mitigate these effects, and further required investigation on their effectiveness.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students

BackgroundA better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.MethodsThe current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).ResultsOf 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.ConclusionThese findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.Trial registrationTrial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Assessing the Impact of Artificial Intelligence Tools on Employee Productivity: Insights from a Comprehensive Survey Analysis

This study provides a nuanced understanding of AI’s impact on productivity and employment using machine learning models and Bayesian Network Analysis. Data from 233 employees across various industries were analyzed using logistic regression, Random Forest, and XGBoost, with 5-fold cross-validation. The findings reveal that high levels of AI tool usage and integration within organizational workflows significantly enhance productivity, particularly among younger employees. A significant interaction between AI tools usage and integration (β = 0.4319, p < 0.001) further emphasizes the importance of comprehensive AI adoption. Bayesian Network Analysis highlights complex interdependencies between AI usage, innovation, and employee characteristics. This study confirms that strategic AI integration, along with targeted training programs and ethical frameworks, is essential for maximizing AI’s economic potential.

Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population.

Hepatitis C virus (HCV) infection progresses through various phases, starting with inflammation and ending with hepatocellular carcinoma. There are several invasive and non-invasive methods to diagnose chronic HCV infection. The invasive methods have their benefits but are linked to morbidity and complications. Thus, it is important to analyze the potential of non-invasive methods as an alternative. Shear wave elastography (SWE) is a non-invasive imaging tool widely validated in clinical and research studies as a surrogate marker of liver fibrosis. Liver fibrosis determination by invasive liver biopsy and non-invasive SWE agree closely in clinical studies and therefore both are gold standards. To analyzed the diagnostic efficacy of non-invasive indices [serum fibronectin, aspartate aminotransferase to platelet ratio index (APRI), alanine aminotransferase ratio (AAR), and fibrosis-4 (FIB-4)] in relation to SWE. We have used an Artificial Intelligence method to predict the severity of liver fibrosis and uncover the complex relationship between non-invasive indices and fibrosis severity. We have conducted a hospital-based study considering 100 untreated patients detected as HCV positive using a quantitative Real-Time Polymerase Chain Reaction assay. We performed statistical and probabilistic analyses to determine the relationship between non-invasive indices and the severity of fibrosis. We also used standard diagnostic methods to measure the diagnostic accuracy for all the subjects. The results of our study showed that fibronectin is a highly accurate diagnostic tool for predicting fibrosis stages (mild, moderate, and severe). This was based on its sensitivity (100%, 92.2%, 96.2%), specificity (96%, 100%, 98.6%), Youden's index (0.960, 0.922, 0.948), area under receiver operating characteristic curve (0.999, 0.993, 0.922), and Likelihood test (LR+ > 10 and LR- < 0.1). Additionally, our Bayesian Network analysis revealed that fibronectin (> 200), AAR (> 1), APRI (> 3), and FIB-4 (> 4) were all strongly associated with patients who had severe fibrosis, with a 100% probability. We have found a strong correlation between fibronectin and liver fibrosis progression in HCV patients. Additionally, we observed that the severity of liver fibrosis increases with an increase in the non-invasive indices that we investigated.

Differences in ischemic heart disease between males and females using predictive artificial intelligence models

&lt;b&gt;Background: &lt;/b&gt;Cardiovascular health and preventative strategies are influenced by the sex of the individuals. To forecast cardiac events or detect ischemic heart disease (IHD) early, machine-learning algorithms can analyze complex patient data patterns. Early detection allows for lifestyle changes, medication management, or invasive treatments to slow disease progression and improve outcomes.&lt;br /&gt; &lt;b&gt;Aim&lt;/b&gt;: To compare and predict the differences in the primary sources of IHD burden between males and females in various age groups, geographical regions, death versus alive, and comorbidity levels.&lt;br /&gt; &lt;b&gt;Methods: &lt;/b&gt;A predictive and retrospective design was implemented in this study. Electronic health records were extracted, which were equally distributed among males and females with IHD. The dataset consisted of patients who were admitted between 2015 and 2022. Two of the eight models generated by Modeler software were implemented in this study: the Bayesian network model, which achieved the highest area under curve score (0.600), and the Chi-squared automatic interaction detection (CHAID) model, which achieved the highest overall accuracy score (57.199%).&lt;br /&gt; &lt;b&gt;Results: &lt;/b&gt;The study sample included 17,878 men and women, 58% of whom had no comorbidities and 1.7% who died. Age, the Charlson comorbidity index score, and geographical location all predicted IHD, but age was more influential. Bayesian network analysis showed that IHD odds were highest in males 40-59 and females 60-79, with the highest mortality risk in females 80-100. North and south Jordan had higher IHD rates and middle-aged males from north and middle governorates had higher IHD rates according to CHAID.&lt;br /&gt; &lt;b&gt;Conclusion: &lt;/b&gt;By using artificial intelligence, clinicians can improve patient outcomes, treatment quality, and save lives in the fight against cardiovascular illnesses. To predict IHD early, machine-learning algorithms can analyze complex patient data patterns to improve outcomes.

Bayesian network analysis enhancing alternative design schemes of large-scale offshore systems

Bayesian network analysis enhancing alternative design schemes of large-scale offshore systems

Harnessing the power of child development records to detect early neurodevelopmental disorders using Bayesian analysis.

This study aims to analyse the developmental data from public health nurses (PHNs) to identify early indicators of neurodevelopmental disorders (NDDs) in young children using Bayesian network (BN) analysis to determine factor combinations that improve diagnosis accuracy. The study cohort was 501 children who underwent health checkups at 18 and 36-month. Data included demographics, pregnancy, delivery, neonatal factors, maternal interviews, and physical and neurological findings. Diagnoses were made by paediatricians and child psychiatrists using standardised tools. Predictive accuracy was assessed by the receiver operating characteristic (ROC) curve analysis. We identified several infant/toddler factors significantly associated with NDD diagnoses. Predictive factors included meconium-stained amniotic fluid, 1 min Apgar score, and early developmental milestones. ROC curve analysis showed varying predictive accuracies based on evaluation timing. The 10-month checkup was valid for screening but less reliable for excluding low-risk cases. The 18-month evaluation accurately identified children at NDD risk. The study demonstrates the potential of using developmental records for early NDD detection, emphasising early monitoring and intervention for at-risk children. These findings could guide future infant mental health initiatives in the community.

Prevalence, patterns and predictors of paranormal beliefs in The Netherlands: a several-analysts approach

Paranormal beliefs encompass a wide variety of phenomena, including the existence of supernatural entities such as ghosts and witches, as well as extraordinary human abilities such as telepathy and clairvoyance. In the current study, we used a nationally representative sample ( N = 2534 ) to investigate the presence and correlates of paranormal beliefs among the secular Dutch population. The results indicated that most single paranormal phenomena (e.g. belief in clairvoyance) are endorsed by 10–20% of Dutch respondents; however, 55.6% of respondents qualify as paranormal believers based on the preregistered criterion that they believe in at least one phenomenon with considerable certainty. In addition, we invited four analysis teams with different methodological expertise to assess the structure of paranormal beliefs using traditional factor analysis, network analysis, Bayesian network analysis and latent class analysis (LCA). The teams’ analyses indicated adequate fit of a four-factor structure reported in a 1985 study, but also emphasized different conclusions across techniques; network analyses showed evidence against strong connectedness within most clusters, and suggested a five-cluster structure. The application of various analytic techniques painted a nuanced picture of paranormal beliefs and believers in The Netherlands and suggests that despite increased secularization, subgroups of the general population still believe in paranormal phenomena.

A Bayesian network analysis of the probabilistic relationships between metabolically healthy obesity and cardiovascular disease risk under new diagnostic criteria.

The relationship between metabolically healthy obesity (MHO) and cardiovascular disease (CVD) risk remains debated. The critical point may be the lack of consensus on MHO's definition and diagnostic criteria. This study aimed to investigate the association of MHO status with arteriosclerosis-CVD (ASCVD) risk in Chinese under new diagnostic criteria. Participants aged 35-79 in the 2009 China Health and Nutrition Survey cohort were included. The 10-year ASCVD risk was predicted by the prediction for ASCVD risk in China, and participants with a predicted risk of ⩾ 10% were classified into the high-risk group. The Bayesian network (BN) models were constructed to characterize the multivariable probabilistic connections between metabolically obesity phenotypes and ASCVD risk. The 10-year ASCVD risk score and the proportion of individuals at ASCVD high risk were significantly different between metabolically obesity phenotypes (P< 0.001). BN reasoning results showed that MHO individuals were not significantly associated with a 10-year ASCVD risk. Among metabolically unhealthy individuals, the conditional probability of high ASCVD risk increased with the Body Mass Index (BMI), with the conditional probability of high ASCVD risk was 24.63% (95% CI: 22.81-26.55%), 32.97% (95% CI: 30.75-35.27%) and 40.2% (95% CI: 36.64-43.86%) for metabolically unhealthy normal weight (MUNW), metabolically healthy overweight weight (MHOW), and metabolically unhealthy obesity (MUO) group, respectively. Subgroup analysis showed that MHO individuals were at increased risk of CVD compared with metabolically healthy normal weight (MHNW) individuals only in females. These results showed that there was no significant increase in ASCVD risk of MHO phenotype based on the new diagnostic criteria, suggesting that MHO is in a relatively healthy state.

Can green firebreaks help balance biodiversity, carbon storage and wildfire risk?

Green firebreaks (strategically placed plantings of low-flammability vegetation) are designed to reduce the rate of fire spread and thereby increase the suppressibility of fires. Successful examples have led to some fire-prone regions investing heavily in the establishment of green firebreaks as a method of reducing fire risk while improving biodiversity and carbon storage. However, beyond small-scale case studies there has been little research quantitatively exploring the interactions among biodiversity, carbon, and wildfire risk in relation to green firebreaks. Here, we combine a Bayesian Network (BN) analysis, and fire simulations in PHOENIX RapidFire (hereafter Phoenix), to identify planting designs that reduce wildfire risks while also providing positive biodiversity and carbon outcomes. Using a BN analysis, we prioritised optimal planting designs as the combination of elements (e.g., stem density, distance from houses, shrub design, age etc.) that delivered the greatest increase in biodiversity and carbon while reducing fire risk to people and property for eight sites across south-eastern Australia. We ranked combinations of planting designs, prioritising house, and life loss first, to identify optimal designs. Optimal planting designs varied among sites, although the design elements that best reduced risk to houses and lives were consistent. These elements included ‘scattered’ shrubs and planting densities of trees consistent with an open forest structure. Estimated fuel loads for the optimal planting design at each site were used to create a simulated revegetation area in Phoenix. We simulated fire behaviour in Phoenix across a grid of ∼1000 ignitions for each site, and for up to 54 historic weather conditions for a 'current fuel’ scenario (no green firebreaks present) compared with a ‘green firebreak fuel’ scenario. We found that the establishment of a green firebreak did not result in significant changes to fire behaviour at most sites. In some cases, it reduced risk to people and property, and where fire behaviour did change in terms of intensity, frequency, ember attack and overall risk, the differences relative to the current fuel scenario were less than two percent. Overall, simulated green firebreaks in most cases were found to provide biodiversity and carbon benefits without increasing fire risk. These findings illustrate their potential as an effective nature-based solution for managing multiple priorities; however, further testing of real plantings is required to evaluate this potential as an at-scale solution.

A Bayesian Longitudinal Network Analysis of Panic-Disorder Symptoms and Respiratory Biomarkers

The network theory of psychopathology is gaining popularity as a conceptualization of psychological disorders that may aid the identification of mechanisms of therapeutic change. However, many existing networks do not consider other relevant variables beyond the symptoms themselves. We present a large-scale ( n = 1,873), longitudinal Bayesian network analysis of panic disorder using the symptom items from the Panic Disorder Severity Scale (PDSS) and two respiratory biomarkers (respiration rate and end-tidal CO2) collected during routine monitoring of a capnometry-guided respiratory intervention (CGRI). Our findings offer support for avoidance and fear of panic as drivers of subsequent panic-disorder symptoms over the 4-week course of treatment. Moreover, respiration rate but not end-tidal CO2 was associated with downstream PDSS symptoms. These findings provide further evidence supporting the role of respiratory biomarkers in the maintenance of panic disorder and some support for normalization of dysfunctional breathing as one therapeutic mechanism governing CGRI.

Developing a Scenario-Based Optimization Model for Planning Risks in Construction Projects by Integrating a Decision Support System with Bayesian Belief Network Analysis Approach: A Case Study in High-Rise Buildings

Developing a Scenario-Based Optimization Model for Planning Risks in Construction Projects by Integrating a Decision Support System with Bayesian Belief Network Analysis Approach: A Case Study in High-Rise Buildings

Efficacy and Safety of Orally Administered East Asian Herbal Medicine Combined with Narrowband Ultraviolet B against Psoriasis: A Bayesian Network Meta-Analysis and Network Analysis.

Psoriasis is a chronic, immune-mediated inflammatory skin disease with many complications and a poor prognosis that imposes a significant burden on individuals and society. Narrowband ultraviolet B (NB-UVB) represents a cost-effective non-drug therapeutic intervention for psoriasis. East Asian herbal medicine (EAHM) is currently being investigated for its potential as a safe and effective psoriasis treatment. Consequently, it has the potential to be employed as a combination therapy with NB-UVB. The objective was to ascertain the efficacy and safety of the EAHM with NB-UVB combination therapy and to identify important drugs for further research. In this study, randomized controlled trials (RCTs) were retrieved from ten databases in Korea, China, and Japan. All statistical analyses were conducted using R software version 4.3.0. The primary outcomes were the Psoriasis Area and Severity Index (PASI) and the incidence rate of adverse events (AEs), while the secondary outcomes were hematologic markers and the Dermatology Life Quality Index (DLQI), which reflect the immune-mediated inflammatory pathology of psoriasis. The analysis of 40 RCTs, including 3521 participants, demonstrated that EAHM with NB-UVB combination therapy exhibited a statistically significant superiority over NB-UVB monotherapy with respect to primary and secondary outcomes. The Bayesian network meta-analysis revealed that Investigator Presciption 3 and Ziyin Liangxue Decoction exhibited a consistent relative advantage with respect to each PASI-based efficacy metric. The network analysis estimated the potential influence ranking for all individual herbs according to PageRank centrality. The findings of this study suggest that EAHMs co-administered with NB-UVB may provide additional efficacy and safety-related benefits for patients with psoriasis. However, the quality of evidence is still low, and further high-quality trials are needed to reach more definitive conclusions.

Species-level, metagenomic and proteomic analysis of microbe-immune interactions in severe asthma.

The airway microbiome in severe asthma has not been characterised at species-level by metagenomic sequencing, nor have the relationships between specific species and mucosal immune responses in 'type-2 low', neutrophilic asthma been defined. We performed an integrated species-level metagenomic data with inflammatory mediators to characterise prevalence of dominant potentially pathogenic organisms and host immune responses. Sputum and nasal lavage samples were analysed using long-read metagenomic sequencing with Nanopore and qPCR in two cross-sectional adult severe asthma cohorts, Wessex (n = 66) and Oxford (n = 30). We integrated species-level data with clinical parameters and 39 selected airway proteins measured by immunoassay and O-link. The sputum microbiome in health and mild asthma displayed comparable microbial diversity. By contrast, 23% (19/81) of severe asthma microbiomes were dominated by a single respiratory pathogen, namely H. influenzae (n = 10), M. catarrhalis (n = 4), S. pneumoniae (n = 4) and P. aeruginosa (n = 1). Neutrophilic asthma was associated with H. influenzae, M. catarrhalis, S. pneumoniae and T. whipplei with elevated type-1 cytokines and proteases; eosinophilic asthma with higher M. catarrhalis, but lower H. influenzae, and S. pneumoniae abundance. H. influenzae load correlated with Eosinophil Cationic Protein, elastase and IL-10. R. mucilaginosa associated positively with IL-6 and negatively with FGF. Bayesian network analysis also revealed close and distinct relationships of H. influenzae and M. catarrhalis with type-1 airway inflammation. The microbiomes and cytokine milieu were distinct between upper and lower airways. This species-level integrated analysis reveals central, but distinct associations between potentially pathogenic bacteria and airways inflammation in severe asthma.

Bayesian network analysis of risk classification strategies in the regulation of cellular products

Cell therapy, a burgeoning therapeutic strategy, necessitates a scientific regulatory framework but faces challenges in risk-based regulation due to the lack of a global consensus on risk classification. This study applies Bayesian network analysis to compare and evaluate the risk classification strategies for cellular products proposed by the Food and Drug Administration (FDA), Ministry of Health, Labour and Welfare (MHLW), and World Health Organization (WHO), using real-world data to validate the models. The appropriateness of key risk factors is assessed within the three regulatory frameworks, along with their implications for clinical safety. The results indicate several directions for refining risk classification approaches. Additionally, a substudy focuses on a specific type of cell and gene therapy (CGT), chimeric antigen receptor (CAR) T cell therapy. It underscores the importance of considering CAR targets, tumor types, and costimulatory domains when assessing the safety risks of CAR T cell products. Overall, there is currently a lack of a regulatory framework based on real-world data for cellular products and a lack of risk-based classification review methods. This study aims to improve the regulatory system for cellular products, emphasizing risk-based classification. Furthermore, the study advocates for leveraging machine learning in regulatory science to enhance the assessment of cellular product safety, illustrating the role of Bayesian networks in aiding regulatory decision-making for the risk classification of cellular products.

Family history of cancer and lung cancer: Utility of big data and artificial intelligence for exploring the role of genetic risk

ObjectivesLung Cancer (LC) is a multifactorial disease for which the role of genetic susceptibility has become increasingly relevant. Our aim was to use artificial intelligence (AI) to analyze differences between patients with LC based on family history of cancer (FHC). Materials and methodsFrom August 2016 to June 2020 clinical information was obtained from Thoracic Tumors Registry (TTR), a nationwide database sponsored by the Spanish Lung Cancer Group. In addition to descriptive statistical analysis, an AI-assisted analysis was performed. The German Technical Information Library supported the merging of data from the electronic medical records and database of the TTR. The results of the AI-assisted analysis were reported using Knowledge Graph, Unified Schema and descriptive and predictive analyses. ResultsAnalyses were performed in two phases: first, conventional statistical analysis including 11,684 patients of those 5,806 had FHC. Median overall survival (OS) for the global population was 23 months (CI 95 %: 21.39–24.61) in patients with FHC versus 21 months (CI 95 %: 19.53–22.48) in patients without FHC (NFHC), p < 0.001. The second AI-assisted analysis included 5,788 patients of those 939 had FHC. 58.48 % of women with FHC had LC. 9.53 % of patients had an EGFR or HER2 mutation or ALK translocation and at least one relative with cancer. A family history of LC was associated with an increased risk of smoking-related LC. Non-smokers with a family history of LC were more likely to have an EGFR mutation in NSCLC. In Bayesian network analysis, 55 % of patients with a family history of LC and never-smokers had an EGFR mutation. ConclusionIn our population, the incidence of LC in patients with a FHC is higher in women and younger patients. FHC is a risk factor and predictor of LC development, especially in people ≤ 50 years. These results were confirmed by conventional statistics and AI-assisted analysis.

Identification and prediction of frailty among community-dwelling older Japanese adults based on Bayesian network analysis: a cross-sectional and longitudinal study

BackgroundFrailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.MethodsWe conducted a cross-sectional and longitudinal study using data from the “Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),” which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).ResultsAt baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model’s AUC was 0.943 (95%CI 0.913–0.974), indicating good discrimination, with Hosmer–Lemeshow (H–L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model’s AUC was 0.722 (95%CI 0.656–0.788), indicating moderate discrimination, with H–L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.ConclusionFrailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.