Predicting informal dementia caregivers' desire to institutionalize through mining data from an eHealth platform.

BACKGROUND: Dementia is a leading factor in the institutionalization of older adults. Informal caregivers' desire to institutionalize (DI) their care recipient with dementia (PwD) is a primary predictor of institutionalization. This study aims to develop a prediction model for caregivers' DI by mining data from an eHealth platform in a high-prevalence dementia country. METHODS: Cross-sectional data were collected from caregivers registering on isupport-portugal.pt. One hundred and four caregivers completed the Desire to Institutionalize Scale (DIS) and were grouped into DI (DIS score ≥ 1) and no DI (DIS score = 0). Participants completed a comprehensive set of sociodemographic, clinical, and psychosocial measures, pertaining to the caregiver and the PwD, which were accounted as model predictors. The selected model was a classification tree, enabling the visualization of rules for predictions. RESULTS: Caregivers, mostly female (82.5%), offspring of the PwD (70.2), employed (65.4%), and highly educated (M 15 years of schooling), provided intensive care (Mdn 24 h. week) over a median course of 2.8 years. Two-thirds (66.3%) endorsed at least one item on the DIS (DI group). The model, with caregivers' perceived stress as the root of the classification tree (split at 28.5 points on the Zarit Burden Interview) and including the ages of caregivers and PwD (split at 46 and 88 years, respectively), as well as cohabitation, employed five rules to predict DI. Caregivers scoring 28.5 and above on burden and caring for PwD under 88 are more prone to DI than those caring for older PwD (rules 1-2), suggesting the influence of expectations on caregiving duration. The model demonstrated high accuracy (0.83, 95%CI 0.75, 0.89), sensitivity (0.88, 95%CI 0.81, 0.95), and good specificity (0.71, 95%CI 0.56, 0.86). CONCLUSIONS: This study distilled a comprehensive range of modifiable and non-modifiable variables into a simplified, interpretable, and accurate model, particularly useful at identifying caregivers with actual DI. Considering the nature of variables within the prediction rules, this model holds promise for application to other existing datasets and as a proxy for actual institutionalization. Predicting the institutional placement of PwD is crucial for intervening on modifiable factors as caregiver burden, and for care planning and financing.

Depth importance in precision medicine (DIPM): a tree- and forest-based method for right-censored survival outcomes.

Many clinical trials have been conducted to compare right-censored survival outcomes between interventions. Such comparisons are typically made on the basis of the entire group receiving one intervention versus the others. In order to identify subgroups for which the preferential treatment may differ from the overall group, we propose the depth importance in precision medicine (DIPM) method for such data within the precision medicine framework. The approach first modifies the split criteria of the traditional classification tree to fit the precision medicine setting. Then, a random forest of trees is constructed at each node. The forest is used to calculate depth variable importance scores for each candidate split variable. The variable with the highest score is identified as the best variable to split the node. The importance score is a flexible and simply constructed measure that makes use of the observation that more important variables tend to be selected closer to the root nodes of trees. The DIPM method is primarily designed for the analysis of clinical data with two treatment groups. We also present the extension to the case of more than two treatment groups. We use simulation studies to demonstrate the accuracy of our method and provide the results of applications to two real-world data sets. In the case of one data set, the DIPM method outperforms an existing method, and a primary motivation of this article is the ability of the DIPM method to address the shortcomings of this existing method. Altogether, the DIPM method yields promising results that demonstrate its capacity to guide personalized treatment decisions in cases with right-censored survival outcomes.

Migration timing and tributary use of spawning flannelmouth sucker (Catostomus latipinnis).

Spawning phenology and associated migrations of fishes are often regulated by factors such as temperature and stream discharge, but flow regulation of mainstem rivers coupled with climate change might disrupt these cues and affect fitness. Flannelmouth sucker (Catostomus latipinnis) persisting in heavily modified river networks are known to spawn in tributaries that might provide better spawning habitat than neighboring mainstem rivers subject to habitat degradation (e.g., embedded sediments, altered thermal regimes, and disconnected floodplains). PIT tag data and radio telemetry were used to quantify the timing and duration of flannelmouth sucker tributary spawning migrations in relation to environmental cues in McElmo Creek, a tributary of the San Juan River in the American Southwest. We also tested the extent of the tributary migration and assessed mainstem movements prior to and after tributary migrations. Additionally, multiyear data sets of PIT detections from other tributaries in the Colorado River basin were used to quantify interannual and cross-site variation in the timing of flannelmouth sucker spawning migrations in relation to environmental cues. The arrival and residence times of fish spawning in McElmo Creek varied among years, with earlier migration and a 3-week increase in residence time in relatively wet years compared to drier years. Classification tree analysis suggested a combination of discharge- and temperature-determined arrival timing. Of fish PIT tagged in the fall, 56% tagged within 10 km of McElmo Creek spawned in the tributary the following spring, as did 60% of radio-tagged fish, with a decline in its use corresponding to increased distance of tagging location. A broader analysis of four tributaries in the Colorado River basin, including McElmo Creek, found photoperiod and temperature of tributary and mainstem rivers were the most important variables in determining migration timing, but tributary and mainstem discharge also aided in classification success. The largest tributary, the Little Colorado River, had more residential fish or fish that stayed for longer periods (median = 30 days), whereas McElmo Creek fish stayed an average of just 10 days in 2022. Our results generally suggest that higher discharge, across years or across sites, results in extended use of tributaries by flannelmouth suckers. Conservation actions that limit water extraction and maintain natural flow regimes in tributaries, while maintaining open connection with mainstem rivers, may benefit migratory species, including flannelmouth suckers.

Predicting the Behavioral Health Needs of Asian Americans in Public Mental Health Treatment: A Classification Tree Approach.

Given the fact that experiencing pandemic-related hardship and racial discrimination worsen Asian Americans' mental health, this study aimed to identify unique characteristics of behavioral health needs among Asian Americans (N = 544) compared to White Americans (N = 78,704) and Black Americans (N = 11,252) who received publicly funded behavioral health services in Indiana before and during the COVID-19 pandemic. We used 2019-2020 Adults Needs and Strengths Assessment (ANSA) data for adults eligible for Medicaid or funding from the state behavioral health agency. Chi-squared automatic interaction detection (CHAID) was used to detect race-specific differences among demographic variables, the pandemic status, and ANSA items. Results indicated that, regardless of age, gender, or pandemic status, Asian Americans who received behavioral health services, struggled more with cultural-related factors compared to White and Black individuals. Within this context, intersections among behavioral/emotional needs (psychosis), life functioning needs (involvement in recovery, residential stability, decision making, medical/physical health), and strengths (job history, interpersonal, and spiritual) further differentiated the mental health functioning of Asian from White and Black Americans. Classification tree algorithms offer a promising approach to detecting complex behavioral health challenges and strengths of populations based on race, ethnicity, or other characteristics.

Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows.

Lameness is one of the culling factors such as mastitis, low milk yield, and infertility that cause economic losses in herd management as they threaten animal health and welfare. The purpose of this study was to evaluate the early detection of lameness in Brown Swiss cattle by using a data mining algorithm by both integrating lameness scores and some image parameters such as Lab (CIE L*, a*, b*), HSB (hue, saturation, brightness), RGB (red, green, blue) by processing thermal images with ImageJ program. In the study, the variables obtained as a result of processing the skin surface temperatures and thermal images taken at the fetlock joint of 33 Brown Swiss cattle were used as independent variables. Also, healthy cows (lameness scores 1 and 2) and unhealthy cows (lameness scores 3, 4, and 5) used in the diagnosis of lameness were used as a binary response variable. Classification and regression tree (CART) was used as a data mining algorithm in the diagnosis of lameness. As a result, the CART algorithm correctly classified 12 of the 13 heads unhealthy cows according to locomotion scores. According to locomotion scores by using CART analysis in this study, independent variables that are used to classify healthy and unhealthy (lame) animals were determined as maximum temperature (Tmax), green (mean), L (max), and age (P<0.05). The cut-off values of these independent variables were predicted as 32.40, 149.14, 97.11, and 5.50 for Tmax, green (mean), L (max), and age, respectively. Also, the sensitivity, specificity, and area under the ROC curve (AUC) of the CART algorithm for locomotion scoring were found as 92.31%, 95%, and 93.7% respectively. The area under ROC curve (AUC) was found to be significant in the diagnosis of lameness (P<0.01). Results showed that the use of CART classification algorithm together with thermal camera and image processing methods is a usefull tool in the detection of lameness in the herds. It is recommended that more comprehensive studies by increasing the number of animals in the future would be more beneficial.

Obtaining optimal cutoff values for tree classifiers using multiple biomarkers.

In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre-specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree-structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined.

Prevalence and Correlates of Insomnia Symptoms in Older Chinese Adults During the COVID-19 Outbreak: A Classification Tree Analysis.

OBJECTIVE: To examine prevalence and correlates of insomnia symptoms in older Chinese adults (OCAs) during the COVID-19 outbreak. BACKGROUND: During the COVID-19 pandemic, insomnia is a major health concern of elderly individuals, but its subtypes have not been investigated. METHODS: Altogether, 590 OCAs (50+ years) were recruited via snowball sampling during the COVID-19 outbreak. Standardized self-report questions were used to assess the presence of difficulty initiating sleep (DIS), difficulty maintaining sleep (DMS), and early morning awakening (EMA). Classification tree analysis (CTA) was used to identify correlates of insomnia. RESULTS: The one-month prevalence (95% confidence interval) of any subtype of insomnia symptoms was 23.4% (20.0-26.8%), with DIS, DMS, and EMA being 15.4% (12.5-18.3%), 17.1% (14.1-20.2%), and 11.2% (8.64-13.7%), respectively. Worry about being infected with COVID-19 emerged as the most salient correlate of insomnia (P < .001); compared to participants who were not worried about being infected, those who were worried and very worried were 3.2-fold (24.3% vs 7.5%) and 5.5-fold (24.3% vs 7.5%) more likely to have insomnia, respectively. Among participants in the "very worried" branch, those residing in Wuhan were 1.8-fold more likely to have insomnia than those residing in other places (50.0% vs 27.5%, P = .011). Among participants in the "worried" branch, unemployed persons were 2.0-fold more likely to have insomnia than employed persons (37.0% vs 18.1%, P < .001). CONCLUSIONS: Insomnia symptoms were prevalent among OCAs during the COVID-19 outbreak. Selective intervention programs targeting elderly individuals who are worried about being infected, living in the epicenter of COVID-19, and unemployed might be effective.

Do we need a more flexible use of Team Timeout calling? Evidence from the Handball Bundesliga.

Calling a Team Timeout (TTO) is one of the coaches' most important tools. Given the key competitive advantage to determine your own timing, it is crucial to make a good decision when to use a TTO. Existing research shows that teams can benefit in general from TTOs and that they are called at the end of the game and when trailing (Gomes et al., 2014; Gutiérrez-Aguilar et al., 2016; Prieto et al., 2016). However, to generate relevant findings, situational variables must be included (Fernandez-Navarro et al., 2020; Gómez, Lago-Peñas et al., 2015). By integrating situational variables like scoring streak and player difference and higher-order interactions, this study aims to identify specific game situations where TTOs are most effective. Based on 850 games of the German Handball Bundesliga, game situations are identified by Classification Tree Analysis and efficacies are evaluated. Findings indicate a strong impact of timing. Frequently used TTOs, e.g., at the end of periods, are beneficial to the teams. However, strongest effect occurs for TTOs taken at the early stages of the game and with a positive run. Results indicate that TTO is a powerful tactical tool and an application at uncommon timings may even enhance the success rate.

Pyridazinones and Structurally Related Derivatives with Anti-Inflammatory Activity.

Persistent inflammation contributes to a number of diseases; therefore, control of the inflammatory response is an important therapeutic goal. In an effort to identify novel anti-inflammatory compounds, we screened a library of pyridazinones and structurally related derivatives that were used previously to identify N-formyl peptide receptor (FPR) agonists. Screening of the compounds for their ability to inhibit lipopolysaccharide (LPS)-induced nuclear factor κB (NF-κB) transcriptional activity in human THP1-Blue monocytic cells identified 48 compounds with anti-inflammatory activity. Interestingly, 34 compounds were FPR agonists, whereas 14 inhibitors of LPS-induced NF-κB activity were not FPR agonists, indicating that they inhibited different signaling pathways. Further analysis of the most potent inhibitors showed that they also inhibited LPS-induced production of interleukin 6 (IL-6) by human MonoMac-6 monocytic cells, again verifying their anti-inflammatory properties. Structure-activity relationship (SAR) classification models based on atom pair descriptors and physicochemical ADME parameters were developed to achieve better insight into the relationships between chemical structures of the compounds and their biological activities, and we found that there was little correlation between FPR agonist activity and inhibition of LPS-induced NF-κB activity. Indeed, Cmpd43, a well-known pyrazolone-based FPR agonist, as well as FPR1 and FPR2 peptide agonists had no effect on the LPS-induced NF-κB activity in THP1-Blue cells. Thus, some FPR agonists reported to have anti-inflammatory activity may actually mediate their effects through FPR-independent pathways, as it is suggested by our results with this series of compounds. This could explain how treatment with some agonists known to be inflammatory (i.e., FPR1 agonists) could result in anti-inflammatory effects. Further research is clearly needed to define the molecular targets of pyridazinones and structurally related compounds with anti-inflammatory activity and to define their relationships (if any) to FPR signaling events.

Analysis of the eutrophication in a wetland using a data-driven model.

Eutrophication is a major problem in the international Anzali wetland (northern Iran). The present research initially aimed to determine the trophic state index (TSI) in ten sampling sites in the main parts of the Anzali wetland (western, eastern, central, and Siahkeshim parts). After determining the TSI in the wetland, a data-driven method (classification tree model with a J48 algorithm) was implemented to predict the trophic condition in the wetland based on a set of water quality and physical-structural variables. One hundred twenty samples related to chlorophyll-a (the model's output) and environmental variables (the model's inputs) were measured monthly during 1-year study period (2017-2018). Based on the TSI calculation, the western, Siahkeshim, eastern, and central parts of the wetland are classified as eutrophic, super-eutrophic, hyper-eutrophic, and hyper-eutrophic, respectively. When all environmental variables were introduced to the model (with five-time randomization effort, pruning confidence factor = 0.01, and seven-fold cross-validation), eight variables (bicarbonate, pH, water temperature, electric conductivity, dissolved oxygen, total phosphorus, water depth, and water turbidity) were predicted by the model. The model predicted that an increase in total phosphate, water turbidity, and electric conductivity concentration may contribute to the hyper-eutrophic state of the wetland. In contrast, the hyper-eutrophic of the wetland is associated with a decrease in water depth, dissolved oxygen, and pH concentration. According to ANOVA test, the trophic condition in the wetland can be affected by spatial and temporal patterns. Anthropogenic pressures such as the influx of chemicals particularly the nutrients (phosphorus and nitrogen) are the main cause of water enrichment (eutrophication problem) in main parts of the Anzali wetland ecosystem.

Characteristics of patients with Coronavirus Disease 2019 (COVID-19) and seasonal influenza at time of hospital admission: a single center comparative study.

BACKGROUND: In the future, co-circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses A/B is likely. From a clinical point of view, differentiation of the two disease entities is crucial for patient management. We therefore aim to detect clinical differences between Coronavirus Disease 2019 (COVID-19) and seasonal influenza patients at time of hospital admission. METHODS: In this single-center observational study, we included all consecutive patients hospitalized for COVID-19 or influenza between November 2019 and May 2020. Data were extracted from a nationwide surveillance program and from electronic health records. COVID-19 and influenza patients were compared in terms of baseline characteristics, clinical presentation and outcome. We used recursive partitioning to generate a classification tree to discriminate COVID-19 from influenza patients. RESULTS: We included 96 COVID-19 and 96 influenza patients. Median age was 68 vs. 70 years (p = 0.90), 72% vs. 56% (p = 0.024) were males, and median Charlson Comorbidity Index (CCI) was 1 vs. 2 (p = 0.027) in COVID-19 and influenza patients, respectively. Time from symptom onset to hospital admission was longer for COVID-19 (median 7 days, IQR 3-10) than for influenza patients (median 3 days, IQR 2-5, p < 0.001). Other variables favoring a diagnosis of COVID-19 in the classification tree were higher systolic blood pressure, lack of productive sputum, and lack of headache. The tree classified 86/192 patients (45%) into two subsets with ≥80% of patients having influenza or COVID-19, respectively. In-hospital mortality was higher for COVID-19 patients (16% vs. 5%, p = 0.018). CONCLUSION: Discriminating COVID-19 from influenza patients based on clinical presentation is challenging. Time from symptom onset to hospital admission is considerably longer in COVID-19 than in influenza patients and showed the strongest discriminatory power in our classification tree. Although they had fewer comorbidities, in-hospital mortality was higher for COVID-19 patients.

A classification tree to assist with routine scoring of the Clinical Frailty Scale.

BACKGROUND: the Clinical Frailty Scale (CFS) was originally developed to summarise a Comprehensive Geriatric Assessment and yield a care plan. Especially since COVID-19, the CFS is being used widely by health care professionals without training in frailty care as a resource allocation tool and for care rationing. CFS scoring by inexperienced raters might not always reflect expert judgement. For these raters, we developed a new classification tree to assist with routine CFS scoring. Here, we test that tree against clinical scoring. OBJECTIVE/METHODS: we examined agreement between the CFS classification tree and CFS scoring by novice raters (clerks/residents), and the CFS classification tree and CFS scoring by experienced raters (geriatricians) in 115 older adults (mean age 78.0 ± 7.3; 47% females) from a single centre. RESULTS: the intraclass correlation coefficient (ICC) for the CFS classification tree was 0.833 (95% CI: 0.768-0.882) when compared with the geriatricians' CFS scoring. In 93%, the classification tree rating was the same or differed by at most one level with the expert geriatrician ratings. The ICC was 0.805 (0.685-0.883) when CFS scores from the classification tree were compared with the clerk/resident scores; 88.5% of the ratings were the same or ±1 level. CONCLUSIONS: a classification tree for scoring the CFS can help with reliable scoring by relatively inexperienced raters. Though an incomplete remedy, a classification tree is a useful support to decision-making and could be used to aid routine scoring of the CFS.

Osteoperiostitis in children: proposal for a diagnostic algorithm.

Juvenile osteoperiostites (JOP) are a group of inflammatory bone diseases whose differential diagnosis is often difficult. The main conditions are acute osteomyelitis (AOM), chronic non-bacterial osteomyelitis (CNO) and the Goldbloom syndrome (GS). The study was aimed to develop an algorithm to enable an early diagnosis of JOP. Clinical records of patients with AOM, CNO and GS, followed at our Center over the past 10 years, were reviewed. Twelve additional patients with GS were selected from PubMed/MEDLINE literature search. Data collected included demographics, clinical manifestations, laboratory and instrumental investigations at disease onset. The association between categorical variables was investigated, and the segmentation of patients with different diagnoses was analyzed through a classification tree model (CTREE package) in order to build up a diagnostic algorithm. Ninety-two patients (33 CNO, 44 AOM, 15 GS) entered the study. Among 30 variables considered at onset, nine (age at onset, fever, weight loss, symmetry, focality, functional limitation, anemia, elevated ESR, CRP) resulted statistically significant in differentiating the three clinical entities from each other and were chosen to build up a decisional tree. Three variables, symmetry of bone involvement, presence of fever and age at disease onset, resulted significant to discriminate each of the three diseases from the others. The performance of the diagnostic algorithm was validated by comparing the diagnoses provided by the model with the real diagnoses and showed 85.9% accuracy.Conclusion: We propose a diagnostic algorithm, based on simple clinical data, which can help guide a prompt and appropriate diagnosis of JOP. What is Known: • Juvenile osteoperiostitis (JOP) are a group of inflammatory bone diseases followed by various pediatric specialists. • The distinction between these conditions is not easy as clinical and laboratory features often overlap. What is New: • We propose a diagnostic algorithm, based on clinical data of real patients, with high degree accuracy. • This instrument can help guide the prompt and appropriate diagnosis of JOP.

Occupational stress among farm and ranch operators in the midwestern United States.

BACKGROUND: This study used surveillance data from 2018 and 2020 to test the stability of work-related strain symptoms (high stress, sleep deprivation, exhaustion) with demographic factors, work characteristics, and musculoskeletal symptoms among farm and ranch operators in seven midwestern states of the United States. METHODS: Cross-sectional surveys were conducted among farm and ranch operators in 2018 (n = 4423) and 2020 (n = 3492). Operators were asked whether, in the past 12 months, they experienced extended work periods that resulted in high stress levels, sleep deprivation, exhaustion/fatigue, or other work-related strain symptoms. Covariates included personal and demographic factors, work characteristics, number of injuries, work-related health conditions, and exposures on the operation. Summary statistics were tabulated for explanatory and outcome variables. The classification (decision) tree approach was used to assess what variables would best separate operators with and without reported strain symptoms, based on a set of explanatory variables. Regularized regression was used to generate effect estimates between the work strain variables and explanatory variables. RESULTS: High stress level, sleep deprivation, and exhaustion were reported more frequently in 2018 than 2020. The classification tree reproduced the 2018 model using 2020 data with approximately 80% accuracy. The mean number of reported MSD symptoms increased slightly from 1.23 in 2018 to 1.41 in 2020. Older age, more time spent in farm work, higher gross farm income (GFI), and MSD symptoms in six body regions (ankles/feet, knees, lower back, neck, shoulders, wrists/hands) were associated with all three work strain symptoms. CONCLUSIONS: Musculoskeletal pain and discomfort was a strong predictor for stress, sleep deprivation, and exhaustion among farmers and ranchers. This finding indicates that reducing MSD pain and discomfort is beneficial for both physical and mental health.

Who are the beneficiaries and what are the reasons for non-utilization of care respite and support services? A cross-sectional study on family caregivers.

BACKGROUND: Family caregivers assume substantial caregiving responsibilities for persons with chronic conditions, such as individuals with spinal cord injury, which leads to negative impacts on their lives. Respite care and other services are provided as a temporary relief and support for them. Design of appropriate respite care programs depends on identification of beneficiary subgroups for the different types of service. This study aimed to quantify the uptake of different respite and support services for family caregivers, the reasons for non-use, and to explore the respective predictors. METHODS: A cross-sectional survey of family caregivers of persons with spinal cord injury was conducted nationwide in Switzerland. The use of 11 different respite and support services during the previous 12 months was investigated, along with caregivers' reasons for not using any respite. Classification trees were used to characterize the beneficiaries and reasons for not using respite. RESULTS: About a third of family caregivers used at least one type of respite or support service during the previous 12 months. Utilization of respite care was greater among those who employed professional home care (57% vs 24% of those without professional home care). Marked cantonal differences were also observed in utilization of respite care. The primary reason for not using respite services was "no demand" (80% of non-users of respite services), mainly among caregivers who were less emotionally affected by their caregiving tasks. CONCLUSIONS: Utilization of respite and support services depends more on place of residence and use of home care services than on functional status of the care recipient. Accordingly, programs should be tailored to the cultural context of their potential users. This is best achieved through coordination with local health care professionals who can identify needs, provide information, initiate referrals, and integrate the care into a larger support plan.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

Diagnostic performance of classification trees and hematological functions in hematologic disorders: an application of multidimensional scaling and cluster analysis.

BACKGROUND: Several hematological indices have been already proposed to discriminate between iron deficiency anemia (IDA) and ß-thalassemia trait (ßTT). This study compared the diagnostic performance of different hematological discrimination indices with decision trees and support vector machines, so as to discriminate IDA from ßTT using multidimensional scaling and cluster analysis. In addition, decision trees were used to determine the diagnostic classification scheme of patients. METHODS: Consisting of 1178 patients with hypochromic microcytic anemia (708 patients with ßTT and 470 patients with IDA), this cross-sectional study compared the diagnostic performance of 43 hematological discrimination indices with classification tree algorithms and support vector machines in order to discriminate IDA from ßTT. Moreover, multidimensional scaling and cluster analysis were used to identify the homogeneous subgroups of discrimination methods with similar performance. RESULTS: All the classification tree algorithms except the LOTUS tree algorithm showed acceptable accuracy measures for discrimination between IDA and ßTT in comparison with other hematological discrimination indices. The results indicated that the CRUISE and C5.0 tree algorithms had better diagnostic performance and efficiency among other discrimination methods. Moreover, the AUC of CRUISE and C5.0 tree algorithms indicated more precise classification with values of 0.940 and 0.999, indicating excellent diagnostic accuracy of such models. Moreover, the CRUISE and C5.0 tree algorithms showed that mean corpuscular volume can be considered as the main variable in discrimination between IDA and ßTT. CONCLUSIONS: CRUISE and C5.0 tree algorithms as powerful methods in data mining techniques can be used to develop accurate differential methods along with other laboratory parameters for the discrimination of IDA and ßTT. In addition, the multidimensional scaling method and cluster analysis can be considered as the most appropriate techniques to determine the discrimination indices with similar performance for future hematological studies.

Classification trees identify shared and distinct correlates of nonsuicidal self-injury and suicidal ideation across gender identities in emerging adults.

College students have an elevated risk for self-injurious thoughts and behaviours (SITBs), and there are robust differences in prevalence rates for SITBs across gender identities. Although numerous constructs have been implicated as risk factors, researchers have not significantly improved at predicting SITBs, possibly owing to constraints of confirmatory analyses. Classification trees are exploratory, person-centred analyses that enable joint examination of numerous correlates and their interactions. Thus, classification trees may discern previously unstudied risk factors and identify distinct subpopulations with elevated risk for SITBs. We tested classification trees that evaluated 298 potential correlates of nonsuicidal self-injury and suicidal ideation across self-identified women and men. Data came from 5,131 college students who completed the National College Health Assessment, which assesses a wide range of health-related constructs. Models produced parsimonious decision trees that accounted for a substantial amount of outcome variability (38.3-51.5%). Psychopathology, poorer psychological well-being, and other SITBs emerged as important correlates for all participants. Trauma, disordered eating, and heavy alcohol use were salient among women, whereas alcohol use norms were important correlates among men. Importantly, models identified several constructs that may be amenable to intervention. Results support the use of exploratory analyses to explicate heterogeneity among individuals who engage in SITBs and suggest that gender identity is an important moderator for certain risk factors.

Ineffective Pregnancy Prevention During Adolescence: Assisting Healthcare Providers in Portugal With Individualized Risk Assessment.

In this study, we describe the relative contributions of and interactions between individual risk factors associated with ineffective pregnancy prevention among female adolescents in Portugal. Our sample consisted of 856 sexually experienced female adolescents (10-19 years) who did not intend to become pregnant. Of these, 379 were pregnant, and the residual (477) had never been pregnant. We used classification tree analysis to describe the interplay among a set of established sociodemographic, familial, reproductive, and relationship factors as predictors of ineffective pregnancy prevention. The tree model showed good predictive properties. Seven profiles predicted one-half to all the cases of ineffective pregnancy prevention. Ineffective pregnancy prevention was predicted by adolescents' grade level and different combinations of variables, specifically female age, age at the time of first sexual intercourse, religious beliefs, place of residence, maternal pregnancy before age 20, household structure in childhood, and partner's age difference. According to our findings, limiting assessments to the cumulative presence of risk factors may be insufficient to accurately identify adolescents at elevated risk of unwanted pregnancy, as the impact of any given risk factor may vary according to other factors. Our findings may contribute to the development of a risk assessment tool that may support healthcare providers' efforts to provide individualized risk assessment for adolescent patients and, thus, to better support pregnancy prevention.

A practical algorithm to predict postsurgical recurrence and progression of pituitary neuroendocrine tumours (PitNET)s.

OBJECTIVE: Pituitary neuroendocrine tumours (PitNET)s can be aggressive, thus presenting local invasion, postsurgical recurrence and/or resistance to treatment, responsible for significant morbidity. The study aimed at identifying prognostic factors of postsurgical outcome using data-driven classification of patients. DESIGN: Retrospective observational study. METHODS: Clinicopathological and radiological data of patients with PitNET treated via endoscopic endonasal surgery were collected. Tumour recurrence/progression and progression-free survival were assessed by classification tree analysis (CTA) and Kaplan-Meier curves, respectively. Histological subtype, cavernous/sphenoid sinus invasion, mitosis, Ki-67, p53, Trouillas' grading, degree of tumour exeresis and postsurgery disease activity were also evaluated. RESULTS: A total of 1066 (466 gonadotroph, 287 somatotroph, 148 lactotroph, 157 corticotroph and 8 thyrotroph) tumours were included; 21.7% invaded the cavernous/sphenoid sinus. Based on Trouillas' classification, 64.3% were grade 1a, 14.2% 1b, 16.1% 2a, and 5.4% 2b; 18.3% had >2/10 HPF mitoses, 24.9% had Ki-67 ≥3%; 15.8% were positive for p53. Exeresis was radical in 81.2% of the cases. Median follow-up was 59.2 months. At last evaluation, 79.4% of the patients were cured; 20.6% had disease persistence, controlled by medical treatment in 18.3% of them. Disease recurrence/progression was recorded in 10.9% of the cases. CTA identified 5 distinct patient subgroups with different risk of disease recurrence/progression. Grade 2 of the Trouillas' grading, >2/10 HPF mitoses, Ki-67 ≥3%, p53 protein expression (P < .001), tumour invasion (P = .002) and ACTH-subtype (P = .003) were identified as risk factors of disease recurrence/progression. CONCLUSIONS: The combined evaluation of Trouillas' grading, proliferation indexes and immunohistochemistry appears promising in the prediction of surgical outcome in PitNET.