Decision tree

OBJECTIVE: To assess the cost-effectiveness of alternative approaches to diagnose and treat obstructive sleep apnea (OSA) in patients with traumatic brain injury (TBI) during inpatient rehabilitation. SETTING: Data collected during the Comparison of Sleep Apnea Assessment Strategies to Maximize TBI Rehabilitation Participation and Outcome (C-SAS) clinical trial (NCT03033901) on an inpatient rehabilitation TBI cohort were used in this study. STUDY DESIGN: Decision tree analysis was used to determine the cost-effectiveness of approaches to diagnosing and treating sleep apnea...

BACKGROUND: Accurate prediction of successful sphincter-preserving resection (SSPR) for low rectal cancer enables peer institutions to scrutinize their own performance and potentially avoid unnecessary permanent colostomy. The aim of this study is to evaluate the variation in SSPR and present the first artificial intelligence (AI) models to predict SSPR in low rectal cancer patients. STUDY DESIGN: This was a retrospective post hoc analysis of a multicenter, noninferiority randomized clinical trial (LASRE, NCT XXXXXX) conducted in 22 tertiary hospitals across China...

OBJECTIVE: The pathological features of immune-mediated necrotizing myopathy (IMNM) are dominated by the infiltration of macrophages. We aimed to perform a histopathologic semiquantitative analysis to investigate the relationship between macrophage markers and prognosis. METHODS: Semiquantitative analysis of histologic features was performed in 62 samples of IMNM. Independent risk factors were identified through univariate and multivariate regression analysis. Cluster analysis was performed using the partitioning around the medoids (PAM) method...

AIMS: A multi-stakeholder consensus has proposed MASLD (metabolic dysfunction-associated steatotic liver disease). We aimed to investigate the pathological findings related to the mid-term mortality of patients with biopsy-proven MASLD in Japan. METHODS: We enrolled 1349 patients with biopsy-proven MASLD. The observational period was 8010 person years. We evaluated independent factors associated with mortality in patients with MASLD by Cox regression analysis. We also investigated pathological profiles related to mortality in patients with MASLD using data-mining analysis...

Previous research suggests that group IIA-secreted phospholipase A2 (sPLA2 -IIA) plays a role in and predicts lethal COVID-19 disease. The current study reanalyzed a longitudinal proteomic data set to determine the temporal relationship between levels of several members of a family of sPLA2 isoforms and the severity of COVID-19 in 214 ICU patients. The levels of six secreted PLA2 isoforms, sPLA2 -IIA, sPLA2 -V, sPLA2 -X, sPLA2 -IB, sPLA2 -IIC, and sPLA2 -XVI, increased over the first 7 ICU days in those who succumbed to the disease but attenuated over the same time period in survivors...

BACKGROUND AND AIMS: Mental health problem is a rising public health concern. People of all ages, specially Bangladeshi university students, are more affected by this burden. Thus, the objective of the study was to use tree-based machine learning (ML) models to identify major risk factors and predict anxiety, depression, and insomnia in university students. METHODS: A social media-based cross-sectional survey was employed for data collection. We used Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index (ISI-7) scale for measuring students' anxiety, depression and insomnia problems...

AIMS: To estimate the cost-effectiveness of a treatment-pathway initiated with bimekizumab, a monoclonal IgG1 antibody that selectively inhibits interleukin (IL)-17F in addition to IL-17A, in patients with axial spondyloarthritis (axSpA) compared with IL-17A inhibitors, ixekizumab, and secukinumab, from the National Health Service (NHS) of Scotland perspective. METHODS: The axSpA treatment-pathway was modeled using a one-year decision tree followed by a lifetime Markov model...

Atopic dermatitis (AD) is a chronic inflammatory skin disease with a significant clinical, economic, and human burden. The JAK1 Atopic Dermatitis Efficacy and Safety (JADE) program's Phase 3 trials demonstrated that as a treatment for moderate-to-severe AD in adults with previous exposure to immunotherapy, abrocitinib showed superior efficacy and safety compared with standard of care (SoC), consisting of topical corticosteroids. This study assessed the cost-effectiveness of abrocitinib with SoC versus SoC alone for this patient population in Japan from a societal perspective...

This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40 days for variations in pH and VFA concentrations under different experimental conditions...

This study aimed to analyze peripheral blood lymphocyte subsets in lupus nephritis (LN) patients and use machine learning (ML) methods to establish an effective algorithm for predicting co-infection in LN. This study included 111 non-infected LN patients, 72 infected LN patients, and 206 healthy controls (HCs). Patient information, infection characteristics, medication, and laboratory indexes were recorded. Eight ML methods were compared to establish a model through a training group and verify the results in a test group...

While prior studies have examined the effects of urban form on transportation carbon emissions, the exploration of nonlinear influences remains limited. This study presents an approach that transcends simple quantification of urban form's impact on transportation carbon emissions by also identifying the threshold range over which urban form variables exert their influence. Using 282 Chinese prefecture-level cities as the sample, this study employs gradient boosting decision trees to identify the nonlinear effects and the relative importance of urban form factors on transportation carbon emissions...

BACKGROUND: Electronic health records are a valuable source of patient information that must be properly deidentified before being shared with researchers. This process requires expertise and time. In addition, synthetic data have considerably reduced the restrictions on the use and sharing of real data, allowing researchers to access it more rapidly with far fewer privacy constraints. Therefore, there has been a growing interest in establishing a method to generate synthetic data that protects patients' privacy while properly reflecting the data...

BACKGROUND: Productivity costs of STIs reflect the value of lost time due to STI morbidity and mortality, including time spent travelling to, waiting for, and receiving STI treatment. The purpose of this study was to provide updated estimates of the average lifetime productivity cost for chlamydia, gonorrhea, and syphilis, per incident infection. METHODS: We adapted published decision tree models from recent studies of the lifetime medical costs of chlamydia, gonorrhea, and syphilis in the United States...

Thrombocythemia (ET), polycythemia vera (PV), primary myelofibrosis (PMF), prefibrotic/early (pre-PMF), and overt fibrotic PMF (overt PMF) are classical Philadelphia-Negative (Ph-negative) myeloproliferative neoplasms (MPNs). Differentiating between these types based on morphology and molecular markers is challenging. This study aims to clarify the application of flow cytometry in the diagnosis and differential diagnosis of classical MPNs. This study retrospectively analyzed the immunophenotypes, clinical characteristics, and laboratory findings of 211 Ph-negative MPN patients, including ET, PV, pre-PMF, overt PMF, and 47 controls...

Data bias is a major concern in biomedical research, especially when evaluating large-scale observational datasets. It leads to imprecise predictions and inconsistent estimates in standard regression models. We compare the performance of commonly used bias-mitigating approaches (resampling, algorithmic, and post hoc approaches) against a synthetic data-augmentation method that utilizes sequential boosted decision trees to synthesize under-represented groups. The approach is called synthetic minority augmentation (SMA)...

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean...

The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time...

Opioids are small-molecule agonists of µ -opioid receptor ( µ OR), while reversal agents such as naloxone are antagonists of mOR. Here we developed machine learning models to classify the intrinsic activities of ligands at the human µ OR. We first manually curated a database of 983 small molecules with measured E max values at the human µ OR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands...

The Fracture Risk Assessment Tool (FRAX®) is a widely utilized country-specific calculator for identifying individuals with high fracture risk; its score is calculated from 12 variables, but its formulation is not publicly disclosed. We aimed to decompose and simplify the FRAX® by utilizing a nationwide community survey database as a reference module for creating a local assessment tool for osteoporotic fracture community screening in any country. Participants ( n  = 16384; predominantly women (75%); mean age = 64...

BACKGROUND: Bacterial vaginosis (BV) is a most common microbiological syndrome. The use of molecular methods, such as multiplex real-time PCR (mPCR) and next-generation sequencing, has revolutionized our understanding of microbial communities. Here, we aimed to use a novel multiplex PCR test to evaluate the microbial composition and dominant lactobacilli in non-pregnant women with BV, and combined with machine learning algorithms to determine its diagnostic significance. METHODS: Residual material of 288 samples of vaginal secretions derived from the vagina from healthy women and BV patients that were sent for routine diagnostics was collected and subjected to the mPCR test...