First­line endocrine therapy for hormone receptor positive and HER­2 negative metastatic breast cancer: A Bayesian network meta­analysis.

Endocrine therapy has become the fundamental treatment option for hormone receptor-positive (HR+) and receptor tyrosine-protein kinase erbB-2-negative (HER2-) metastatic breast cancer (mBC). While treatments incorporating cyclin-dependent kinase (CDK)4 and 6 inhibitors are more prevalent than ever, comparisons among those regimens are scarce. The aim of the present study was to identify the most effective maintenance treatment for patients with HR+ and HER2- mBC. To this end, databases including PubMed, Embase, Cochrane Library, Scopus and Google Scholar were searched from inception to August, 2023. The endpoints comprised overall survival (OS) and progression free survival (PFS). For dichotomous variants, hazard ratios (HRs) and odds ratios (ORs) were generated, while standard mean difference (SMD) was used for consecutive variants by Bayesian network meta-analysis to make pairwise comparisons among regimens, to determine the optimal therapy. These processes were conducted using Rstudio 4.2.2 orchestrated with STATA 17.0 MP. A total of 16 randomized controlled trials including 7,174 patients with 11 interventions were analyzed. Compared with aromatase inhibitor (AI), palbociclib plus AI (PalboAI) exhibited a significantly longer PFS up to the 36th month of follow-up [HR=1.7; 95% credible interval, 1.36-2.16], including on the 3rd [OR=2.22; 95% confidence interval (CI), 1.10-4.47], 6th (OR=2.39; 95% CI, 1.21-4.69), 12th (OR=1.94; 95% CI, 1.34-2.79), 18th (OR=2.38; 95% CI, 1.65-3.44), 24th (OR=2.39; 95% CI, 1.67-3.43), 30th (OR=2.10; 95% CI, 1.62-2.74) and 36th (OR=2.66; 95% CI, 1.37-5.18) month of follow-up. Additionally, abemaciclib plus fulvestrant exhibited significant effects compared with AI alone between 12 and 36 months. Ribociclib plus fulvestrant, ribociclib plus AI and dalpiciclib plus AI exerted significant effects compared with AI alone between 12 and 30 months. Considering the effect on OS and PFS together with adverse reactions, safety, medical compliance and route of administration, PalboAI was found to be the optimal treatment for HR+/HER2-mBC. However, additional head-to-head clinical trials are warranted to confirm these findings.

Pharmacological treatments for vascular dementia: a systematic review and Bayesian network meta-analysis.

Background: Vascular dementia (VaD) is one of the most prevalent, burdensome, and costly forms of dementia. Pharmacological treatment is often the first-line choice for clinicians; however, there is a paucity of comparative information regarding the multiple available drug options. Methods and Analysis: A systematic review and network meta-analysis were conducted on randomized trials involving adult patients with VaD, sourced from PubMed, the Cochrane Library, EMBASE, Web of Science, OPENGREY, ClinicalTrials.gov, Wanfang Data, and CNKI. The primary outcomes included changes in Mini-Mental State Examination (MMSE) scores, activities of daily living (ADL) scores, and the incidence of adverse reactions. Efficacy and safety of intervention strategies were comprehensively analyzed using forest plots, cumulative ranking probability curves (SUCRA), and funnel plots, all generated with R software. Results: A total of 194 RCTs comparing 21 different anti-VaD drugs with placebos or no treatment were analysed. Regarding MMSE scores, the five most effective drugs were Butylphthalide, Huperzine A, Edaravone, Rivastigmine, and Memantine. For ADL scores, the top five drugs in efficacy were Huperzine A, Butylphthalide, Tianzhi granule, Nicergoline, and Idebenone. In terms of the incidence of adverse drug reactions, Co-dergocrine Mesylate, Tongxinluo capsule, Butylphthalide, Piracetam, and Oxiracetam demonstrated favourable safety profiles. Conclusion: This study enhances the understanding of the relative benefits and risks associated with various VaD treatments, providing a valuable reference for clinical decision-making. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier registration number.

Effects of exercise in adults with cancer pain: a systematic review and Network meta-analysis.

CONTEXT: Pain is one of the most common symptoms of cancer patients, affecting the patient's physical, psychological, behavioral, social relations and other aspects. Previous studies have demonstrated that exercise is effective for cancer pain, and the optimal exercise is still unknown. OBJECTIVES: This study aimed to compare the effects of different exercise interventions on cancer pain in adults. METHODS: Randomized control trials identified from medical literature databases that reported effects of exercise in adults with cancer pain were included in this study. Literature screening and data extraction were conducted independently by two researchers. Cochrane Bias Assessment 2.0 was used to assess the quality of the literature, and Stata 15.0 software was used for Network meta-analysis. RESULTS: Forty-one studies were included, involving 3537 patients with cancer pain. The types of exercise involved included aerobic exercise, medium intensity continuous training, high-intensity interval training, resistance exercise, mind-body exercise and comprehensive exercise program (CEP). The results suggested that CEP was more effective than the usual care in relieving pain intensity in cancer patients [SMD=-1.96,95%CI (-3.47, -0.44)] (SUCRA=97.9%). Mind-body exercise outperformed usual care in reducing pain interference in cancer patients [SMD = -0.65, 95% CI (-1.21, -0.09)] (SUCRA=83.8%). CONCLUSION: Current evidence shows that CEP is the best way to relieve the pain intensity of cancer patients, and mind-body exercise is the best way to reduce pain interference of cancer patients. Due to the limited number and quality of the included studies, the above conclusions need to be further verified by more high-quality studies.

Fungi-sensitized individuals have unique profiles where Alt a 1 dominates promoting response to grass, ragweed and cat allergens.

Introduction: The aim of our work was to determine comprehensively the sensitization profile of patients hypersensitive to fungal allergenic components in the Ukrainian population, identifying features of their co-sensitization to allergens of other groups and establishing potential relationships between causative allergens and their ability to provoke this hypersensitivity. Methods: A set of programs was developed using Python and R programming languages, implementing the K-means++ clustering method. Bayesian networks were constructed based on the created clusters, allowing for the assessment of the probabilistic interplay of allergen molecules in the sensitization process of patients. Results and discussion: It was found that patients sensitive to fungi are polysensitized, with 84.77% of them having unique allergological profiles, comprising from 2 to several dozen allergens from different groups. The immune response to Alt a 1 may act as the primary trigger for sensitization to other allergens and may contribute to a high probability of developing sensitivity to grasses (primarily to Phl p 2), ragweed extract, and the Amb a 1 pectate lyase, as well as to pectate lyase Cry j 1 and cat allergen Fel d 1. Individuals polysensitized to molecular components of fungi were often sensitive to such cross-reactive molecules as lipocalins Fel d 4 and Can f 6, as well. Sensitivity to Ambrosia extract which dominated in the development of sensitization to ragweed pollen indicating the importance of different allergenic components of this plant's pollen. This hypothesis, along with the assumption that Phl p 2 may be the main trigger for sensitivity to grasses in patients with Alternaria allergy, requires further clinical investigation.

Human reliability analysis in maintenance and repair operations of mining trucks: A Bayesian network approach.

Failures in mining machinery can abruptly halt mineral production and operations, emphasizing the indispensable role of humans in maintenance and repair operations. Addressing human errors is crucial for ensuring a safe and reliable system, particularly during maintenance activities where accidents frequently occur. This paper focuses on evaluating Human Reliability (HR) to enhance activity implementation effectiveness. Given the challenge of limited and uncertain data on human errors, this study aims to estimate the probability of human errors using Bayesian networks (BN) under uncertain parameters. Applying this approach to assess HR in the maintenance and repair operations of mining trucks at Golgohar Iron Ore Mine in Iran, the study identifies critical factors influencing error occurrence in a fuzzy environment. The results highlight key factors impacting human error and offer insights into estimating HR with minimal human intervention.

Machine learning algorithms predict canine structural epilepsy with high accuracy.

Introduction: Clinical reasoning in veterinary medicine is often based on clinicians' personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures. Materials and methods: Dogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features. Results: A total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year. Conclusion: Machine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process.

Research on accident early warning of metallurgical enterprises based on grey DEMATEL/ISM and Bayesian network.

To clarify the complex relationship between the factors causing safety accidents in metallurgical enterprises and predict the risk of accidents in enterprises, a correlation analysis model of the factors causing safety accidents in metallurgical enterprises based on grey Decision-Making Trial and Evaluation Laboratory/Interpretative Structural Modeling (DEMATEL/ISM) was established, and a Bayesian network early warning model was constructed on this basis. The relationship and action path of accident-causing factors in metallurgical enterprises were clarified. The factors were hierarchically divided and a multi-layer hierarchical structure model was established to obtain the neighboring cause, transitional cause, and essential cause of the accident. The results showed that the employee violation rate, the hazardous substances reserves, the toxic gas and dust pollution control compliance rate, the pass rate for equipment maintenance, and the qualification rate of special equipment were the neighboring causes of the accident. The perfection of the safety production management system was the essential cause. The Bayesian network early warning model was applied to the Fuxin Jiuxing Titanium work site. The expected risk probability of an accident was 17.9%, which was in a comparatively safe state (State2). The results obtained by the Bayesian model are consistent with those obtained by AHP and fuzzy comprehensive evaluation method, which proved the accuracy of the early warning model. The Bayesian model can give the risk probability value of the accident and the risk probability value of the accident cause factors at the same time, and include the causal relationship and conditional correlation relationship among the indicator variables in the reasoning process, which can provide targeted technical support for the construction of the emergency system of risk classification management and control.

Sustainable resilient E-waste management in London: A circular economy perspective.

The circular economy (CE) is reasoned to organize complex systems supporting sustainable resilience by distinguishing between waste materials and economic growth. This is crucial to the electronic waste (e-waste) industry of developed countries, and e-waste operation management has become their top priority because e-waste contains toxic materials and valuable sources of elements. In the UK, although London Metropolitan city boasts an ambitious sustainable resilience target underlying the context of CE, practical implementation has yet to be feasible, with few investigations detailing if and how the existing target implications enable industrial and social-ecological sectors to continue their performance functionalities in the face of undesired disruptions. In this paper, a dynamic Bayesian Network (dynamic BN) approach is developed to address a range of potential risks. The existing London e-waste operation management is considered as an application of study for sustainable resilience development. Through the utilization of dynamic BN, a comprehensive analysis yields a Resilience Index (RI) of 0.5424, coupled with a StdDev of 0.01350. These metrics offer a profound insight into the intricate workings of a sustainable system and its capacity to swiftly rebound from unexpected shocks and disturbances. This newfound understanding equips policymakers with the knowledge needed to navigate the complexities of sustainable e-waste management effectively. The implications drawn from these in-depth analyses furnish policymakers with invaluable information, enabling them to make judicious decisions that advance the cause of sustainable e-waste management. The findings underscore that the absorptive capacity of a sustainable and resilient e-waste operation management system stands as the foremost defense mechanism against unforeseen challenges. Furthermore, it becomes evident that two pivotal factors, namely "diversifying the supply chain" and "enhancing supply chain transparency," play pivotal roles in augmenting the sustainability and resilience of e-waste operation management within the context of London's ambitious sustainability targets. These factors are instrumental in steering the trajectory of e-waste management towards a more sustainable and resilient future, aligning with London's aspirations for a greener and more eco-conscious future.

Bayesian network analysis of drug treatment strategies for thyroid associated ophthalmopathy.

BACKGROUND: The first line treatment for moderate to severe active thyroid associated ophthalmopathy is glucocorticoid pulse therapy, but for patients with contraindications to hormone therapy or hormone resistance, it is urgent to find a suitable treatment plan. AIMS: To find a reliable alternative to hormone pulse therapy for thyroid associated ophthalmopathy by comparing the efficacy with first-line treatment regimens. METHODS: Search PubMed, Ovid, Web of science, Cochrane library, and Clinical Trials.gov for randomized controlled trials on the treatment of thyroid associated ophthalmopathy published as of July 7, 2024. Quality evaluation and Bayesian network analysis were conducted using RevMan 5.3 software, STATA15.0 software, and ADDIS 1.16.8 software. RESULTS: A total of 666 patients were included in 11 studies and 8 interventions. Network analysis showed that the three interventions of mycophenolate mofetil combined with glucocorticoids, Teprotumumab and 99Tc-MDP were superior to glucocorticoid pulse therapy in improving clinical activity scores and proptosis. The regimen of glucocorticoids combined with statins can improve the quality of life score and diplopia score of patients. Neither methotrexate combined with glucocorticoids nor rituximab alone showed additional advantages when compared with glucocorticoid pulse therapy. CONCLUSION: Mycophenolate mofetil combined with glucocorticoid therapy is very beneficial for moderate to severe active thyroid associated ophthalmopathy. Mycophenolate mofetil may be a good choice when patients have contraindications to hormone use or hormone resistance. Teprotumumab is very promising and may be able to avoid patients undergoing orbital decompression surgery. The durability and safety of its long-term efficacy need to be further observed.

A dynamic decision-making approach for cabin unlawful interference emergency disposal using dynamic Bayesian network.

Disposal of unlawful interference incidents is essential for is crucial for the advancement of aviation security. Effective emergency disposal requires a comprehensive approach that includes the perspectives of airlines, airports, and passengers. In this context, each component of the disposal process can fail randomly. The objective of this research is to optimize emergency disposal decisions to enhance the efficiency of civil aviation operations, reduce accidents, and lower costs. Given the dynamic complexity of unlawful interference incidents, a dynamic fault tree consisting of 26 nodes was constructed to analyze the emergency disposal process. To explore the relationships and priorities of each event, the Dynamic Fault Tree is converted into a dynamic Bayesian network. Based on historical statistical data, simulation analysis is conducted in three aspects: posterior probability, sensitivity, and importance. Simulation results reveal that the top three critical nodes in cabin unlawful interference incidents are "structural damage to the cabin," "inadequate training by airlines," and "untimely airport police takeover of disruptive passengers." Further analysis shows that (1) most of the critical nodes are associated with airlines. (2) The decision-making rationale and pathways of the critical nodes can be clearly observed and prioritized. (3) Besides airlines, other entities such as airports can implement targeted emergency disposal measures. Through quantitative analysis and simulation, this study provides decision-making guidance for participating groups on dynamic emergency disposal, thereby enhancing civil aviation security.

Integrating Deep Learning-Based Dose Distribution Prediction with Bayesian Networks for Decision Support in Radiotherapy for Upper Gastrointestinal cancer.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or Magnetic Resonance (MR)-guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the PTV and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. Conclusion: We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Accounting for site-to-site DNA transfer on a packaged exhibit in an evaluation given activity level propositions.

Considering activity level propositions in the evaluation of forensic biology findings is becoming more common place. There are increasing numbers of publications demonstrating different transfer mechanisms that can occur under a variety of circumstances. Some of these publications have shown the possibility of DNA transfer from site to site on an exhibit, for instance as a result of packaging and transport. If such a possibility exists, and the case circumstances are such that the area on an exhibit where DNA is present or absent is an observation that is an important diagnostic characteristic given the propositions, then site to site transfer should be taken into account during the evaluation of observations. In this work we demonstrate the ways in which site to site transfer can be built into Bayesian networks when carrying out activity level evaluations of forensic biology findings. We explore the effects of considering qualitative vs quantitative categorisation of DNA results. We also show the importance of taking into account multiple individual's DNA being transferred (such as unknown or wearer DNA), even if the main focus of the evaluation is the activity of one individual.

A data-driven Bayesian network of management and organizational factors for human reliability analysis in the process industry.

According to historical statistical data, management and organizational factors (MOFs) contribute more to process accidents than technique factors. Under the umbrella of socio-tech system theory, human reliability analysis (HRA) has become a critical part of systemic probability risk analysis. In many HRA techniques, MOFs are among the performance shaping factors (PSFs). However, the interactions and causality of MOFs to human errors are still difficult to quantify and lack validation. To fill these gaps, a framework is proposed, considering data source selection, CBN construction algorithm comparison, and results validation. The case study employed the open access eMARS database as a data source. The optimized hybrid structure learning algorithm and Bayesian criteria parameter learning algorithm are employed to build a Causal Bayesian Network (CBN) of (MOFs) that lead to human error. The proposed kernel CBN is validated through prediction accuracy and sensitivity analysis. For theoretical contribution, the validated kernel BN could generally serve as the heart part of more specific CBNs as a basis for future works. For practical applications, an application shows the model's ability to quantify the contribution of MOFs to system reliability. The results show that human-machine interacting system reliability is most sensitive to organizational factors such as adequate training and procedures.

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.

Combined interpretation of objective firearm evidence comparison algorithms using Bayesian networks.

Traditionally, firearm and toolmark examiners manually evaluate the similarity of features on two bullets using comparison microscopy. Advances in microscopy have made it possible to collect 3D topographic data, and several automated comparison algorithms have been introduced for the comparison of bullet striae using these data. In this study, open-source approaches for cross-correlation, congruent matching profile segments, consecutive matching striations, and a random forest model were evaluated. A statistical characterization of these automated approaches was performed using four datasets of consecutively manufactured firearms to provide a challenging comparison scenario. Each automated approach was applied to all samples in a pairwise fashion, and classification performance was compared. Based on these findings, a Bayesian network was empirically learned and constructed to leverage the strengths of each individual approach, model the relationship between the automated results, and combine them into a posterior probability for the given comparison. The network was evaluated similarly to the automated approaches, and the results were compared. The developed Bayesian network classified 99.6% of the samples correctly, and the resultant probability distributions were significantly separated more so than the automated approaches when used in isolation.

Enhancing Periodontal Treatment Through the Integration of Deep Learning-Based Detection with Bayesian Network Models.

This study incorporated deep learning for periodontal disease detection into a Bayesian network (BN) clinical decision support model for comprehensive periodontal care. BN structure and probabilities were based on clinical data and Faster R-CNN-detected radiographic images. Receiver operating characteristic curve analysis confirmed the model's high accuracy in treatment plan recommendations.

Deriving a Continuous Point of Departure for Skin Sensitization Risk Assessment Using a Bayesian Network Model.

Regulations of cosmetic ingredients and products have been the most advanced in embracing new approach methodologies (NAMs). Consequently, the cosmetic industry has assumed a forerunner role in the development and implementation of animal-free next-generation risk assessment (NGRA) that incorporates defined approaches (DAs) to assess the skin sensitization potency of ingredients. A Bayesian network DA predicting four potency categories (SkinSens-BN) was constructed against reference Local Lymph Node Assay data for a total of 297 substances, achieving a predictive performance similar to that of other DAs. With the aim of optimally informing risk assessment with a continuous point of departure (PoD), a weighted sum of the SkinSens-BN probabilities for four potency classes (non-, weak, moderate, and strong/extreme sensitizer) was calculated, using fixed weights based on associated LLNA EC3-values. The approach was promising, e.g., the derived PoDs for substances classified as non-sensitizers did not overlap with any others and 77% of PoDs were similar or more conservative than LLNA EC3. In addition, the predictions were assigned a level of confidence based on the probabilities to inform the evaluation of uncertainty in an NGRA context. In conclusion, the PoD derivation approach can substantially contribute to reliable skin sensitization NGRAs.

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.

Using joint probability density to create most informative unidimensional indices: a new method using pain and psychiatric severity as examples.

BACKGROUND: Dimension reduction methods do not always reduce their underlying indicators to a single composite score. Furthermore, such methods are usually based on optimality criteria that require discarding some information. We suggest, under some conditions, to use the joint probability density function (joint pdf or JPD) of p-dimensional random variable (the p indicators), as an index or a composite score. It is proved that this index is more informative than any alternative composite score. In two examples, we compare the JPD index with some alternatives constructed from traditional methods. METHODS: We develop a probabilistic unsupervised dimension reduction method based on the probability density of multivariate data. We show that the conditional distribution of the variables given JPD is uniform, implying that the JPD is the most informative scalar summary under the most common notions of information. B. We show under some widely plausible conditions, JPD can be used as an index. To use JPD as an index, in addition to having a plausible interpretation, all the random variables should have approximately the same direction(unidirectionality) as the density values (codirectionality). We applied these ideas to two data sets: first, on the 7 Brief Pain Inventory Interference scale (BPI-I) items obtained from 8,889 US Veterans with chronic pain and, second, on a novel measure based on administrative data for 912 US Veterans. To estimate the JPD in both examples, among the available JPD estimation methods, we used its conditional specifications, identified a well-fitted parametric model for each factored conditional (regression) specification, and, by maximizing the corresponding likelihoods, estimated their parameters. Due to the non-uniqueness of conditional specification, the average of all estimated conditional specifications was used as the final estimate. Since a prevalent common use of indices is ranking, we used measures of monotone dependence [e.g., Spearman's rank correlation (rho)] to assess the strength of unidirectionality and co-directionality. Finally, we cross-validate the JPD score against variance-covariance-based scores (factor scores in unidimensional models), and the "person's parameter" estimates of (Generalized) Partial Credit and Graded Response IRT models. We used Pearson Divergence as a measure of information and Shannon's entropy to compare uncertainties (informativeness) in these alternative scores. RESULTS: An unsupervised dimension reduction was developed based on the joint probability density (JPD) of the multi-dimensional data. The JPD, under regularity conditions, may be used as an index. For the well-established Brief Pain Interference Inventory (BPI-I (the short form with 7 Items) and for a new mental health severity index (MoPSI) with 6 indicators, we estimated the JPD scoring. We compared, assuming unidimensionality, factor scores, Person's scores of the Partial Credit model, the Generalized Partial Credit model, and the Graded Response model with JPD scoring. As expected, all scores' rankings in both examples were monotonically dependent with various strengths. Shannon entropy was the smallest for JPD scores. Pearson Divergence of the estimated densities of different indices against uniform distribution was maximum for JPD scoring. CONCLUSIONS: An unsupervised probabilistic dimension reduction is possible. When appropriate, the joint probability density function can be used as the most informative index. Model specification and estimation and steps to implement the scoring were demonstrated. As expected, when the required assumption in factor analysis and IRT models are satisfied, JPD scoring agrees with these established scores. However, when these assumptions are violated, JPD scores preserve all the information in the indicators with minimal assumption.

Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network.

Enhancing crop water productivity is crucial for regional water resource management and agricultural sustainability, particularly in arid regions. However, evaluating the spatial heterogeneity and temporal dynamics of crop water productivity in face of data limitations poses a challenge. In this study, we propose a framework that integrates remote sensing data, time series generative adversarial network (TimeGAN), dynamic Bayesian network (DBN), and optimization model to assess crop water productivity and optimize crop planting structure under limited water resources allocation in the Qira oasis. The results demonstrate that the combination of TimeGAN and DBN better improves the accuracy of the model for the dynamic prediction, particularly for short-term predictions with 4 years as the optimal timescale (R2 > 0.8). Based on the spatial distribution of crop suitability analysis, wheat and corn are most suitable for cultivation in the central and eastern parts of Qira oasis while cotton is unsuitable for planting in the western region. The walnuts and Chinese dates are mainly unsuitable in the southeastern part of the oasis. Maximizing crop water productivity while ensuring food security has led to increased acreage for cotton, Chinese dates and walnuts. Under the combined action of the five optimization objectives, the average increase of crop water productivity is 14.97%, and the average increase of ecological benefit is 3.61%, which is much higher than the growth rate of irrigation water consumption of cultivated land. It will produce a planting structure that relatively reduced irrigation water requirement of cultivated land and improved crop water productivity. This proposed framework can serve as an effective reference tool for decision-makers when determining future cropping plans.