Problem choice and decision trees in science and engineering.

Scientists and engineers often spend days choosing a problem and years solving it. This imbalance limits impact. Here, we offer a framework for problem choice: prompts for ideation, guidelines for evaluating impact and likelihood of success, the importance of fixing one parameter at a time, and opportunities afforded by failure.

Factors that contributed to Ontario adults' mental health during the first 16 months of the COVID-19 pandemic: a decision tree analysis.

The COVID-19 pandemic has negatively impacted the mental health of individuals globally. However, less is known about the characteristics that contributed to some people having mental health problems during the pandemic, while others did not. Mental health problems can be understood on a continuum, ranging from acute (e.g., depression following a stressful event) to severe (e.g., chronic conditions that disrupt everyday functioning). Therefore, the purpose of this article was to generate profiles of adults who were more or less at risk for the development of mental health problems, in general, during the first 16-months of the COVID-19 pandemic in Ontario, Canada. Data were collected via online surveys at two time points: April-July 2020 and July-August 2021; 2,188 adults (Mage = 43.15 years; SD = 8.82) participated. Surveys included a demographic questionnaire and four previously validated tools to measure participants' mental health, subjective wellbeing, physical activity and sedentary behaviour, and sleep. A decision tree was generated at each time point for those with mental health problems, and those with no mental health problems. Results showed that subjective wellbeing was the biggest contributor to mental health status. Characteristics associated with no mental health problems among adults included having good wellbeing, being a good sleeper (quantity, quality, and patterns of sleep), and being over the age of 42. Characteristics associated with mental health problems included having poor wellbeing and being a poor sleeper. Findings revealed that specific characteristics interacted to contribute to adults' mental health status during the first 16 months of the COVID-19 pandemic. Given that wellbeing was the biggest contributor to mental health, researchers should focus on targeting adults' wellbeing to improve their mental health during future health crises.

[Economic evaluation of Annao Pills combined with antihypertensive drugs in treatment of primary hypertension: a study based on decision tree model].

The purpose of this study was to evaluate the economics of Annao Pills combined with antihypertensive drugs in the treatment of primary hypertension in the Chinese medical setting. TreeAge pro 2018 was used for cost-effect analysis and sensitivity analysis of the two treatment regimens. The intervention time of the simulation model was 2 weeks. The cost parameters were derived from Yaozhi.com, and the effect parameters were based on Meta-analysis of randomized controlled trial(RCT) involving Annao Pills. The experimental group was treated with Annao Pills combined with anti-hypertensive drugs(nifedipine controlled-release tablets + losartan potassium tablets), and the control group was treated with anti-hypertensive drugs(nifedipine controlled-release tablets + losartan potassium tablets). The basic analysis showed that the incremental cost-effect ratio(ICER) of the two groups was 2 678.67 yuan, which was less than 7.26% of the per capita disposable income in 2022. That is, compared with anti-hypertensive drugs alone, Annao Pills combined with antihypertensive drugs cost 2 678.67 yuan more for each additional patient with primary hypertension. The results of sensitivity analysis verified the robustness of the basic analysis results. The probability sensitivity results showed that when the patient's personal willingness to pay the price was higher than 2 650 yuan, the probability of the regimen in the experimental group was higher, which was consistent with the results of the basic analysis. In conclusion, when the price was higher than 2 650 yuan, Annao Pills combined with anti-hypertensive drugs was more economical than anti-hypertensive drugs alone in terms of improving the response rate of the patients with primary hypertension.

Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis.

BACKGROUND: As the population ages, mild cognitive impairment (MCI) and type 2 diabetes mellitus (T2DM) become common conditions that often coexist. Evidence has shown that MCI could lead to reduced treatment compliance, medication management, and self-care ability in T2DM patients. Therefore, early identification of those with increased risk of MCI is crucial from a preventive perspective. Given the growing utilization of decision trees in prediction of health-related outcomes, this study aimed to identify MCI in T2DM patients using the decision tree approach. METHODS: This hospital-based case-control study was performed in the Endocrinology Department of Xiangya Hospital affiliated to Central South University between March 2021 and December 2022. MCI was defined based on the Petersen criteria. Demographic characteristics, lifestyle factors, and T2DM-related information were collected. The study sample was randomly divided into the training and validation sets in a 7:3 ratio. Univariate and multivariate analyses were performed, and a decision tree model was established using the chi-square automatic interaction detection (CHAID) algorithm to identify key predictor variables associated with MCI. The area under the curve (AUC) value was used to evaluate the performance of the established decision tree model, and the performance of multivariate regression model was also evaluated for comparison. RESULTS: A total of 1001 participants (705 in the training set and 296 in the validation set) were included in this study. The mean age of participants in the training and validation sets was 60.2  ±  10.3 and 60.4  ±  9.5 years, respectively. There were no significant differences in the characteristics between the training and validation sets (p > .05). The CHAID decision tree analysis identified six key predictor variables associated with MCI, including age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy. The established decision tree model had 15 nodes composed of 4 layers, and age is the most significant predictor variable. It performed well (AUC = .75 [95% confidence interval (CI): .71-.78] and .67 [95% CI: .61-.74] in the training and validation sets, respectively), was internally validated, and had comparable predictive value compared to the multivariate logistic regression model (AUC = .76 [95% CI: .72-.80] and .69 [95% CI: .62-.75] in the training and validation sets, respectively). CONCLUSION: The established decision tree model based on age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy performed well with comparable predictive value compared to the multivariate logistic regression model and was internally validated. Due to its superior classification accuracy and simple presentation as well as interpretation of collected data, the decision tree model is more recommended for the prediction of MCI in T2DM patients in clinical practice.

Predictive modeling of burnout based on organizational culture perceptions among health systems employees: a comparative study using correlation, decision tree, and Bayesian analyses.

Burnout is a significant concern, particularly within the healthcare field, affecting both nurses and physicians. It is a common issue in health systems, which encompass a range of healthcare facilities, such as hospitals, physician practices, ambulatory sites, and administrative offices like finance. Despite this, there has not been an extensive exploration of burnout in employees working directly with patients versus those in non-patient-facing roles within these health systems. It is important to note that organizational culture plays a crucial role in influencing various aspects of employees' work-life balance and their experiences of burnout. This study adopts a cross-sectional design, involving the distribution of a 57-question Likert scale survey to employees in health systems. These employees serve in various roles, both patient-facing and non-patient-facing, within jointly owned healthcare organizations, which encompass hospitals, ambulatory sites, and administrative offices. The survey was disseminated through trade organizations and employees at the managerial level and above within these health systems. Data was collected between October 2022 and January 2023, resulting in a total of 67 responses. The study employs correlation analysis to explore the connection between organizational culture and burnout. Furthermore, a decision tree model is constructed to predict burnout scores based on survey responses, specifically the question regarding the perceived positivity of the organizational culture. The decision tree models indicate that perceiving organizational culture as positive, safety-oriented, and supportive predicts various outcomes for individuals, including job retention, positive experiences with patients, increased callousness, and stimulation while working with colleagues. Bayesian analysis, considering the small sample size, reinforces these findings and provides a different perspective, incorporating prior knowledge and credible intervals. An association test suggests a strong link between a positive organizational culture and burnout symptoms, while another test supports a connection with engagement signs. Similar to nurses and physicians, administrative health systems' personnel are susceptible to burnout. Organizational culture can affect burnout. Therefore, health systems' leaders should cultivate an organizational culture that protects against burnout.

The utilization of decision trees on orthopantomographic and lateral panoramic graphs for the diagnosis of unilateral anterior disc displacement of the temporomandibular joint.

BACKGROUND: Investigation is to utilize decision trees in conjunction with orthopantomography (OPT) and lateral panoramic graphy (LPG) to diagnose unilateral anterior disc displacement (ADD) of the temporomandibular joint. METHODS: In this study, 161 patients with images obtained through all three imaging methods, MRI, OPT, and LPG, were selected from the archives. The participants were categorized into two groups: the study group, comprising 89 patients with unilateral anterior disc displacement, and the control group, consisting of 72 healthy individuals. Measurements, including 2 angles (antero-posterior angle and superior-inferior angle) and 3 distance parameters (anterior joint space distance, superior joint space distance, and posterior joint space distance), were conducted on each imaging modality dataset. To assess the obtained measurement data within each patient, the differences from each measurement were calculated. Statistical analysis of the measurement differences between the control and study groups was carried out with independent t test, and decision trees were generated using the SPSS 25 decision tree module 5.0. RESULTS: In ADD patients, it was statistically significantly found that the APA increased while the SIA decreased for angle measurements. But for linear measurements, AS increased while the SS and PS decreased in MRI, OPT, and LPG. CONCLUSION: ADD can be diagnosed in OPT and LPG. The identification of the specific type of ADD that occurs in the temporomandibular joint is not feasible.

Immediate postnatal prediction of death or bronchopulmonary dysplasia among very preterm and very low birth weight infants based on gradient boosting decision trees algorithm: A nationwide database study in Japan.

INTRODUCTION: Bronchopulmonary dysplasia (BPD) poses a substantial global health burden. Individualized treatment strategies based on early prediction of the development of BPD can mitigate preterm birth complications; however, previously suggested predictive models lack early postnatal applicability. We aimed to develop predictive models for BPD and mortality based on immediate postnatal clinical data. METHODS: Clinical information on very preterm and very low birth weight infants born between 2008 and 2018 was extracted from a nationwide Japanese database. The gradient boosting decision trees (GBDT) algorithm was adopted to predict BPD and mortality, using predictors within the first 6 h postpartum. We assessed the temporal validity and evaluated model adequacy using Shapley additive explanations (SHAP) values. RESULTS: We developed three predictive models using data from 39,488, 39,096, and 40,291 infants to predict "death or BPD," "death or severe BPD," and "death before discharge," respectively. These well-calibrated models achieved areas under the receiver operating characteristic curve of 0.828 (95% CI: 0.828-0.828), 0.873 (0.873-0.873), and 0.887 (0.887-0.888), respectively, outperforming the multivariable logistic regression models. SHAP value analysis identified predictors of BPD, including gestational age, size at birth, male sex, and persistent pulmonary hypertension. In SHAP value-based case clustering, the "death or BPD" prediction model stratified infants by gestational age and persistent pulmonary hypertension, whereas the other models for "death or severe BPD" and "death before discharge" commonly formed clusters of low mortality, extreme prematurity, low Apgar scores, and persistent pulmonary hypertension of the newborn. CONCLUSIONS: GBDT models for predicting BPD and mortality, designed for use within 6 h postpartum, demonstrated superior prognostic performance. SHAP value-based clustering, a data-driven approach, formed clusters of clinical relevance. These findings suggest the efficacy of a GBDT algorithm for the early postnatal prediction of BPD.

Using decision tree to predict non-suicidal self-injury among young adults: the role of depression, childhood maltreatment and recent bullying victimization.

Importance: Non-suicidal self-injury (NSSI) is a significant mental health issue requiring a deeper understanding of its underlying causes, such as childhood maltreatment, adult bullying victimization, and depression. Previous studies have not adequately addressed the cumulative risks of these factors on NSSI among college students. This population-based study investigates these cumulative risk factors.Design, setting, and participants: The cross-sectional study included 63 university's college students with a mean age of 19.6 years (N = 95,833).Main outcomes and measures: Two Chi-Square Automatic Interaction Detection (CHAID) decision tree models were used to classify subgroups based on childhood maltreatment and adult bullying victimization experiences and to investigate their cumulative risks of NSSI. Recursive partitioning algorithms determined each predictor variable's relative importance.Results: The CHAID model accurately predicted NSSI behaviours with an overall accuracy rate of 77.8% for individuals with clinically relevant depressive symptoms and 97.2% for those without. Among depressed individuals, childhood emotional abuse was the strongest NSSI predictor (Chi-Square, 650.747; adjusted P < .001), followed by sexual and physical abuse. For non-depressed individuals, emotional abuse in childhood was the strongest NSSI predictor (Chi-Square, 2084.171; adjusted P < .001), with sexual and verbal bullying in the past year representing the most significant proximal risks.Conclusions and relevance: Emotional abuse during childhood profoundly impacts individuals, increasing the risk of NSSI in both depressed and non-depressed individuals. Clinically relevant depressive symptoms have a moderating effect on the relationship between childhood maltreatment, adult bullying victimization, and NSSI. Identifying these factors can inform targeted interventions to prevent NSSI development among young adults.

Emotional abuse during childhood has a profound impact on individuals, increasing their risk of non-suicidal self-injury (NSSI), regardless of whether they are depressed or non-depressed.Among depressed individuals, childhood emotional abuse emerges as the strongest predictor of NSSI, followed by sexual and physical abuse.In non-depressed individuals, emotional abuse in childhood assumes a similar role as the strongest NSSI predictor, with sexual abuse and verbal bullying in the past year representing the most significant proximal risks.

Classified diagnosis and treatment scheme of oral cosmetic restoration based on aesthetic analysis (part â ): basic concept, decision tree and clinical pathway.

At present, the commonly used clinical protocols of oral comestic restoration are mostly based on the aesthetic indicators proposed by Western developed countries (referred to as Western aesthetics). Mechanically copying the Western aesthetic scheme, ignoring the difference between it and the Chinese oral aesthetic indicators (referred to as Chinese aesthetics), is unable to effectively support personalized cosmetic restoration diagnosis and treatment. In addition, new technologies and new solutions for cosmetic restoration, which are developing rapidly in recent years, are emerging one after another, but many popular concepts are confusing and lack of proper hierarchical diagnosis and treatment norms, and there is indeed an urgent need for discussion and clarity. From the perspective of serving clinical application, this paper discusses the deficiencies of the Chinese translation of the word "aesthetics", the diffe-rence and connection between aesthetics and cosmetology, and the relationship between cosmetic restoration and fixed restoration. It also discusses the difference between anterior teeth, esthetic zone and exposed zone, the diagnostic and therapeutic value of oral aesthetic analysis, as well as the application methods of desensitization, suggestion, and other therapies in difficult oral cosmetic restoration cases. We further introduce the decision tree and the clinical pathway for restoration and reconstruction of teeth in exposed zone guided by aesthetic analysis, and introduce the clinical process of aesthetic analysis and evaluation, the clinical triclassification of oral cosmetic restoration, and the corresponding clinical classification diagnosis and treatment points.

STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data.

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

Exploring risk factors and their differences on suicidal ideation and suicide attempts among depressed adolescents based on decision tree model.

BACKGROUND: Suicide has been recognized as a major global public health issue. Depressed adolescents are more prone to experiencing it. We explore risk factors and their differences on suicidal ideation and suicide attempts to further enhance our understanding of suicidal behavior. METHODS: 2343 depressed adolescents aged 12-18 from 9 provinces/cities in China participated in this cross-sectional study. We utilized decision tree model, incorporating 32 factors encompassing participants' suicidal behavior. The feature importance of each factor was measured using Gini coefficients. RESULTS: The decision tree model demonstrated a good fit with high accuracy (SI = 0.86, SA = 0.85 and F-Score (SI = 0.85, SA = 0.83). The predictive importance of each factor varied between groups with suicidal ideation and with suicide attempts. The most significant risk factor in both groups was depression (SI = 16.7 %, SA = 19.8 %). However, factors such as academic stress (SI = 7.2 %, SA = 1.6 %), hopelessness (SI = 9.1 %, SA = 5.0 %), and age (SI = 7.1 %, SA = 3.2 %) were more closely associated with suicidal ideation than suicide attempts. Factors related to the schooling status (SI = 3.5 %, SA = 10.1 %), total years of education (SI = 2.6 %, SA = 8.6 %), and loneliness (SI = 2.3 %, SA = 7.4 %) were relatively more important in the suicide attempt stage compared to suicidal ideation. LIMITATIONS: The cross-sectional design limited the ability to capture changes in suicidal behavior among depressed adolescents over time. Possible bias may exist in the measurement of suicidal ideation. CONCLUSION: The relative importance of each risk factor for suicidal ideation and attempted suicide varies. These findings provide further empirical evidence for understanding suicide behavior. Targeted treatment measures should be taken for different stages of suicide in clinical interventions.

The Prediction Model of Non-Suicidal Self-Injury in Psychiatric Patients Using Decision Tree Analysis.

PURPOSE: The prevalence rate of non-suicidal self-injury (NSSI) in the clinical population is higher than that in the community sample, necessitating the need to investigate the predicting factors of NSSI in this group. The present study aimed to develop a prediction model of NSSI among psychiatric patients in Korea. MATERIALS AND METHODS: Decision tree analysis was conducted on a sample of 224 psychiatric patients. Emotion regulation strategies (rumination, cognitive reappraisal, and expressive suppression), impulsivity, problematic alcohol use, working memory, depressive mood, and gender were included in the model as predictors of NSSI. RESULTS: Results indicated that rumination, problematic alcohol use, and working memory predicted lifetime NSSI engagement among psychiatric patients. The best predictor of lifetime NSSI engagement was rumination. Specifically, when the level of rumination was high, the level of working memory was lower, and the risk of NSSI was higher. In the case of low levels of rumination, the higher the level of problematic alcohol use, the higher the risk of NSSI. The highest prevalence of lifetime NSSI engagement was found in a subgroup of patients with high levels of rumination and low levels of working memory. CONCLUSION: The major contribution of this study is finding a combination of factors to predict the high-risk group of NSSI among psychiatric patients in Korea. This study provides evidence on the effect of rumination, working memory, and problematic alcohol use on NSSI. It is suggested that clinicians and researchers should pay more attention to emotion regulation and related vulnerabilities in preventing and treating NSSI.

A Decision Tree to Guide Human and Mouse Mammary Organoid Model Selection.

Over the past 50 years, researchers from the mammary gland field have launched a collection of distinctive 3D cell culture systems to study multiple aspects of mammary gland physiology and disease. As our knowledge about the mammary gland evolves, more sophisticated 3D cell culture systems are required to answer more and more complex questions. Nowadays, morphologically complex mammary organoids can be generated in distinct 3D settings, along with reproduction of multiple aspects of the gland microenvironment. Yet, each 3D culture protocol comes with its advantages and limitations, where some culture systems are best suited to study stemness potential, whereas others are tailored towards the study of mammary gland morphogenesis. Therefore, prior to starting a 3D mammary culture experiment, it is important to consider and select the ideal culture model to address the biological question of interest. The number and technical requirements of novel 3D cell culture methods vastly increased over the past decades, making it currently challenging and time consuming to identify the best experimental testing. In this chapter, we provide a summary of the most promising murine and human 3D organoid models that are currently used in mammary gland biology research. For each model, we will provide a brief description of the protocol and an overview of the expected morphological outcome, the advantages of the model, and the potential pitfalls, to guide the reader to the best model of choice for specific applications.

The role of lactate-to-albumin ratio to predict 30-day risk of death in patients with sepsis in the emergency department: a decision tree analysis.

BACKGROUND: Accurately estimating the prognosis of septic patients on arrival in the emergency department (ED) is clinically challenging. The lactate-to-albumin ratio (LAR) has recently been proposed to improve the predictive performance of septic patients admitted to the ICU. OBJECTIVES: This study aims to assess whether the LAR could be used as an early prognostic marker of 30-day mortality in patients with sepsis in the ED. METHODS: A prospective observational study was conducted in the ED of the Hospital of Merano. All patients with a diagnosis of sepsis were considered. The LAR was recorded on arrival in the ED. The primary outcome measure was mortality at 30 days. The predictive role of the LAR for mortality was evaluated with the area under the ROC curve, logistic regression adjusted for the Charlson Comorbidity Index value, National Early Warning Score, and Sequential Organ Failure score, and with decision tree analysis. RESULTS: 459 patients were enrolled, of whom 17% (78/459) died at 30 days. The median LAR of the patients who died at 30 days (0.78 [0.45-1.19]) was significantly higher than the median LAR of survivors (0.42 [0.27-0.65]) (p < 0.001). The discriminatory ability of the LAR for death at 30 days was 0.738, higher than that of lactate alone (0.692), and slightly lower than that of albumin alone (0.753). The decision trees confirmed the role of the LAR as an independent risk factor for mortality. CONCLUSION: The LAR can be used as an index to better predict the 30-day risk of death in septic patients.

Vesical Imaging-Reporting and Data System (VI-RADS) as a grouping imaging biomarker combined with a decision-tree mode to preoperatively predict the pathological grade of bladder cancer.

AIM: To investigate whether the Vesical Imaging-Reporting and Data System (VI-RADS) could be used to develop a new non-invasive preoperative grade-prediction system to partially predict high-grade bladder cancer (HG-BC). MATERIALS AND METHODS: The present study enrolled 89 primary BC patients prospectively from March 2022 to June 2023. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of VI-RADS for predicting HG-BC and muscle-invasive bladder cancer (MIBC) in the entire group. In the low VI-RADS (≤2) group, the decision tree-based method was used to obtain significant predictors and construct the decision-tree model (DT model). The performance of the DT model and low VI-RADS scores for predicting HG-BC was determined using ROC, calibration, and decision curve analyses. RESULTS: At a cut-off of ≥3, the specificity and positive predictive value of VI-RADS for predicting HG-BC in the entire group was 100%, and the area under the ROC curve (AUC) was 0.697. Among 65 patients with low VI-RADS scores, the DT model showed an AUC of 0.884 in predicting HG-BC compared to 0.506 for low VI-RADS scores. Calibration and decision curve analyses showed that the DT model performed better than the low VI-RADS scores. CONCLUSION: Most VI-RADS scores ≥3 correspond to HG-BCs. VI-RADS could be used as a grouping imaging biomarker for a pathological grade-prediction procedure, which in combination with the DT model for low VI-RADS (≤2) populations, would provide a potential preoperative non-invasive method of predicting HG-BC.

Decision tree analysis for age estimation in living individuals: integrating cervical and dental radiographic evaluations within a South African population.

Age estimation in living individuals around the age of 18 years is medico-legally important in undocumented migrant cases and in countries like South Africa where many individuals are devoid of identification documents. Establishing whether an individual is younger than 18 years largely influences the legal procedure that should be followed in dealing with an undocumented individual. The aim of this study was to combine dental third molar and anterior inferior apophysis ossification data for purposes of age estimation, by applying a decision tree analysis. A sample comprising of 871 black South African individuals (n = 446 males, 425 = females) with ages ranging between 15 and 24 years was analyzed using panoramic and cephalometric radiographs. Variables related to the left upper and lower third molars and cervical vertebral ring apophysis ossification of C2, C3, and C4 vertebrae analyzed in previous studies were combined in a multifactorial approach. The data were analyzed using a pruned decision tree function for classification. Male and female groups were handled separately as a statistically significant difference was found between the sexes in the original studies. A test sample of 30 individuals was used to determine if this approach could be used with confidence in estimating age of living individuals. The outcomes obtained from the test sample indicated a close correlation between the actual ages (in years and months) and the predicted ages (in years only), demonstrating an average age difference of 0.47 years between the corresponding values. This method showed that the application of decision tree analysis using the combination of third molar and cervical vertebral development is usable and potentially valuable in this application.

An Evaluation of the National Early Warning Score 2 and the Laboratory Data Decision Tree Early Warning Score in Predicting Mortality in Geriatric Patients.

BACKGROUND: Due to the high rate of geriatric patient visits, scoring systems are needed to predict increasing mortality rates. OBJECTIVE: In this study, we aimed to investigate the in-hospital mortality prediction power of the National Early Warning Score 2 (NEWS2) and the Laboratory Data Decision Tree Early Warning Score (LDT-EWS), which consists of frequently performed laboratory parameters. METHODS: We retrospectively analyzed 651 geriatric patients who visited the emergency department (ED), were not discharged on the same day from ED, and were hospitalized. The patients were categorized according to their in-hospital mortality status. The NEWS2 and LDT-EWS values of these patients were calculated and compared on the basis of deceased and living patients. RESULTS: Median (interquartile range [IQR]) NEWS2 and LDT-EWS values of the 127 patients who died were found to be statistically significantly higher than those of the patients who survived (NEWS2: 5 [3-8] vs. 3 [1-5]; p < 0.001; LDT-EWS: 8 [7-10] vs. 6 [5-8]; p < 0.001). In the receiver operating characteristic curve analysis, the NEWS2, LDT-EWS, and NEWS2+LDT-EWS-formed by the sum of the two scoring systems-resulted in 0.717, 0.705, and 0.775 area under curve values, respectively. CONCLUSIONS: The NEWS2 and LDT-EWS were found to be valuable for predicting in-hospital mortality in geriatric patients. The power of the NEWS2 to predict in-hospital mortality increased when used with the LDT-EWS.

Decision tree-based identification of important molecular fragments for protein-ligand binding.

Fragment-based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein-ligand binding affinity. Specifically, the three-dimensional (3D) structures of protein-ligand complexes are encoded using extended-connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few-shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments.