How Do Heterogeneous Networks Affect a Firm's Innovation Performance? A Research Analysis Based on Clustering and Classification

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry-University-Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers.

Metacognitive Strategy Training Improves Decision-Making Abilities in Amnestic Mild Cognitive Impairment

Mild cognitive impairment (MCI) is associated with deficits in decision-making, which is of utmost importance for daily functioning. Despite evidence of declined decision-making abilities, research on decision-making interventions for MCI is scarce. As metacognition seems to play an important role in decision-making, the present study's aim was to examine whether a metacognitive strategy training can improve MCI patients' decision-making abilities. Older adults-patients of a day care center, diagnosed with amnestic MCI (n = 55) were randomly allocated in two groups, which were matched in gender, age and educational level. Τhe experimental group (n = 27, 18 women, mean age = 70.63, mean years of education = 13.44) received the metacognitive strategy training in parallel with the cognitive and physical training programs of the day care center, and the active control group (n = 28, 21 women, mean age = 70.86, mean years of education = 13.71) received only the cognitive and physical training of the center. The metacognitive strategy training included three online meeting sessions that took place once per week. The basis of the intervention was using analytical thinking, by answering four metacognitive-strategic questions, to make decisions about everyday situations. To examine the efficacy of the training, the ability to make decisions about everyday decision-making situations and the ability to apply decision rules were measured. Both groups participated in a pre-test session and a post-test session, while the experimental group also participated in a follow-up session, one month after the post-test session. The results showed that the experimental group improved its ability to decide, based on analytical thinking, about economic and healthcare-related everyday decision-making situations after they received the metacognitive strategy training. This improvement was maintained one month later. However, the ability to apply decision rules, which requires high cognitive effort, did not improve. In conclusion, it is important that some aspects of the analytical decision-making ability of amnestic MCI patients were improved due to the present metacognitive intervention.

Justification of Indication for Cranial CT Imaging after Mild Traumatic Brain Injury According to the Current National Guidelines

The primary aim was to evaluate the compliance of cranial CT indication with the national guideline-based decision rules in patients after mTBI. The secondary aim was to determine the incidence of CT pathologies among justified and unjustified CT scans and to investigate the diagnostic value of these decision rules. This is a retrospective, single-center study on 1837 patients (mean age = 70.7 years) referred to a clinic of oral and maxillofacial surgery following mTBI over a five-year period. The current national clinical decision rules and recommendations for mTBI were retrospectively applied to calculate the incidence of unjustified CT imaging. The intracranial pathologies among the justified and unjustified CT scans were presented using descriptive statistical analysis. The performance of the decision rules was ascertained by calculating the sensitivity, specificity, and predictive values. A total of 123 intracerebral lesions were radiologically detected in 102 (5.5%) of the study patients. Most (62.1%) of the CT scans strictly complied with the guideline recommendations, and 37.8% were not justified and likely avoidable. A significantly higher incidence of intracranial pathology was observed in patients with justified CT scans compared with patients with unjustified CT scans (7.9% vs. 2.5%, p < 0.0001). Patients with loss of consciousness, amnesia, seizures, cephalgia, somnolence, dizziness, nausea, and clinical signs of cranial fractures presented pathologic CT findings more frequently (p < 0.05). The decision rules identified CT pathologies with 92.28% sensitivity and 39.08% specificity. To conclude, compliance with the national decision rules for mTBI was low, and more than a third of the CT scans performed were identified as "likely avoidable". A higher incidence of pathologic CT findings was detected in patients with justified cranial CT imaging. The investigated decision rules showed a high sensitivity but low specificity for predicting CT pathologies.

Improved EAV-Based Algorithm for Decision Rules Construction

In this article, we present a modification of the algorithm based on EAV (entity-attribute-value) model, for induction of decision rules, utilizing novel approach for attribute ranking. The selection of attributes used as premises of decision rules, is an important stage of the process of rules induction. In the presented approach, this task is realized using ranking of attributes based on standard deviation of attributes' values per decision classes, which is considered as a distinguishability level. The presented approach allows to work not only with numerical values of attributes but also with categorical ones. For this purpose, an additional step of data transformation into a matrix format has been proposed. It allows to transform data table into a binary one with proper equivalents of categorical values of attributes and ensures independence of the influence of the attribute selection function from the data type of variables. The motivation for the proposed method is the development of an algorithm which allows to construct rules close to optimal ones in terms of length, while maintaining enough good classification quality. The experiments presented in the paper have been performed on data sets from UCI ML Repository, comparing results of the proposed approach with three selected greedy heuristics for induction of decision rules, taking into consideration classification accuracy and length and support of constructed rules. The obtained results show that for the most part of datasests, the average length of rules obtained for 80% of best attributes from the ranking is very close to values obtained for the whole set of attributes. In case of classification accuracy, for 50% of considered datasets, results obtained for 80% of best attributes from the ranking are higher or the same as results obtained for the whole set of attributes.

Impact of enterprise digitalization on green innovation performance under the perspective of production and operation

Introduction

How enterprises should practice digitalization transformation to effectively improve green innovation performance is related to the sustainable development of enterprises and the economy, which is an important issue that needs to be clarified.

Methods

This research uses the perspective of production and operation to deconstruct the digitalization of industrial listed enterprises from 2016 to 2020 into six features. A variety of machine learning methods are used, including DBSCAN, CART and other algorithms, to specifically explore the complex impact of enterprise digitalization feature configuration on green innovation performance.

Conclusions

(1) The more advanced digitalization transformation the enterprises have, the more possibly the high green innovation performance can be achieved. (2) Digitalization innovation is the digitalization element with the strongest influence ability on green innovation performance. (3) As the advancement of digitalization transformation, enterprises should also focus on digitalization innovation input and digitalization operation output, otherwise they should pay attention to digitalization management and digitalization operation output.

Discussion

The conclusions of this research will help enterprises understand their digitalization competitiveness and how to practice digitalization transformation to enhance green innovation performance, and also help the government to formulate policies to promote the development of green innovation in the digital economy era.

Performance and Accuracy Comparisons of Classification Methods and Perspective Solutions for UAV-Based Near-Real-Time "Out of the Lab" Data Processing

Today, integration into automated systems has become a priority in the development of remote sensing sensors carried on drones. For this purpose, the primary task is to achieve real-time data processing. Increasing sensor resolution, fast data capture and the simultaneous use of multiple sensors is one direction of development. However, this poses challenges on the data processing side due to the increasing amount of data. Our study intends to investigate how the running time and accuracy of commonly used image classification algorithms evolve using Altum Micasense multispectral and thermal acquisition data with GSD = 2 cm spatial resolution. The running times were examined for two PC configurations, with a 4 GB and 8 GB DRAM capacity, respectively, as these parameters are closer to the memory of NRT microcomputers and laptops, which can be applied "out of the lab". During the accuracy assessment, we compared the accuracy %, the Kappa index value and the area ratio of correct pixels. According to our results, in the case of plant cover, the Spectral Angles Mapper (SAM) method achieved the best accuracy among the validated classification solutions. In contrast, the Minimum Distance (MD) method achieved the best accuracy on water surface. In terms of temporality, the best results were obtained with the individually constructed decision tree classification. Thus, it is worth developing these two directions into real-time data processing solutions.

Selected Data Mining Tools for Data Analysis in Distributed Environment

In this paper, we deal with distributed data represented either as a finite set T of decision tables with equal sets of attributes or a finite set I of information systems with equal sets of attributes. In the former case, we discuss a way to the study decision trees common to all tables from the set T: building a decision table in which the set of decision trees coincides with the set of decision trees common to all tables from T. We show when we can build such a decision table and how to build it in a polynomial time. If we have such a table, we can apply various decision tree learning algorithms to it. We extend the considered approach to the study of test (reducts) and decision rules common to all tables from T. In the latter case, we discuss a way to study the association rules common to all information systems from the set I: building a joint information system for which the set of true association rules that are realizable for a given row ρ and have a given attribute a on the right-hand side coincides with the set of association rules that are true for all information systems from I, have the attribute a on the right-hand side, and are realizable for the row ρ. We then show how to build a joint information system in a polynomial time. When we build such an information system, we can apply various association rule learning algorithms to it.

A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting

In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle this challenge, in this paper, we propose a novel WiFi fingerprinting method that uses the valued tolerance rough set theory-based classification method. In the offline phase, the conventional received signal strength (RSS) fingerprinting database is converted into a decision table. Then a new fingerprinting database with decision rules is constructed based on the decision table, which includes the credibility degrees and the support object set values for all decision rules. In the online phase, various classification levels are applied to find out the best match between the RSS values in the decision rules database and the measured RSS values at the unknown position. The experimental results compared the performance of the proposed method with those of the nearest-neighbor-based and the random statistical methods in two different test cases. The results show that the proposed method greatly outperforms the others in both cases, where it achieves high accuracy with 98.05% of right position classification, which is approximately 50.49% more accurate than the others. The mean positioning errors at wrong estimated positions for the two test cases are 1.71 m and 1.99 m, using the proposed method.

The Influence of Knowledge Base on the Dual-Innovation Performance of Firms

Dual innovation, which includes exploratory innovation and exploitative innovation, is crucial for firms to obtain a sustainable competitive advantage. The knowledge base of firms greatly influences or even determines the scope, direction, and path of their dual-innovation activities, which drive their innovation process and produce different innovation performances. This study uses data source patents obtained by 285 focal firms in the Chinese new-energy vehicle industry in the period 2015–2020. Five knowledge-base features are selected by analyzing the correlation and multicollinearity, and four different firm clusters are found by using the k-means clustering algorithm. Based on the classification and regression tree (CART) algorithm, we mine the potential decision rules governing the dual-innovation performance of firms. The results show that the exploratory innovation performance of firms in different clusters is mainly affected by two different knowledge-base features. Knowledge-base scale is a key factor affecting the exploitative innovation performance of firms. Firms in different clusters can improve their dual-innovation performance by rationally tuning the combination of knowledge-base features.

External Validation of Clinical Prediction Models in Unilateral Primary Aldosteronism

BACKGROUND

Targeted treatment of primary aldosteronism (PA) is informed by adrenal vein sampling (AVS), which remains limited to specialized centers. Clinical prediction models have been developed to help select patients who would most likely benefit from AVS. Our aim was to assess the performance of these models for PA subtyping.

METHODS

This external validation study evaluated consecutive patients referred for PA who underwent AVS at a tertiary care referral center in Alberta, Canada during 2006–2018. In alignment with the original study designs and intended uses of the clinical prediction models, the primary outcome was the presence of lateralization on AVS. Model discrimination was evaluated using the C-statistic. Model calibration was assessed by comparing the observed vs. predicted probability of lateralization in the external validation cohort.

RESULTS

The validation cohort included 342 PA patients who underwent AVS (mean age, 52.1 years [SD, 11.5]; 201 [58.8%] male; 186 [54.4%] with lateralization). Six published models were assessed. All models demonstrated low-to-moderate discrimination in the validation set (C-statistics; range, 0.60–0.72), representing a marked decrease compared with the derivation sets (range, 0.80–0.87). Comparison of observed and predicted probabilities of unilateral PA revealed significant miscalibration. Calibration-in-the-large for every model was >0 (range, 0.35–1.67), signifying systematic underprediction of lateralizing disease. Calibration slopes were consistently <1 (range, 0.35–0.87), indicating poor performance at the extremes of risk.

CONCLUSIONS

Overall, clinical prediction models did not accurately predict AVS lateralization in this large cohort. These models cannot be reliably used to inform the decision to pursue AVS for most patients.

The Use of Cost-Effectiveness Thresholds for Evaluating Health Interventions in Low- and Middle-Income Countries From 2015 to 2020: A Review

OBJECTIVES

Evidence-informed priority setting, in particular cost-effectiveness analysis (CEA), can help target resources better to achieve universal health coverage. Central to the application of CEA is the use of a cost-effectiveness threshold. We add to the literature by looking at what thresholds have been used in published CEA and the proportion of interventions found to be cost-effective, by type of threshold.

METHODS

We identified CEA studies in low- and middle-income countries from the Global Health Cost-Effectiveness Analysis Registry that were published between January 1, 2015, and January 6, 2020. We extracted data on the country of focus, type of interventions under consideration, funder, threshold used, and recommendations.

RESULTS

A total of 230 studies with a total 713 interventions were included in this review; 1 to 3× gross domestic product (GDP) per capita was the most common type of threshold used in judging cost-effectiveness (84.3%). Approximately a third of studies (34.2%) using 1 to 3× GDP per capita applied a threshold at 3× GDP per capita. We have found that no study used locally developed thresholds. We found that 79.3% of interventions received a recommendation as "cost-effective" and that 85.9% of studies had at least 1 intervention that was considered cost-effective. The use of 1 to 3× GDP per capita led to a higher proportion of study interventions being judged as cost-effective compared with other types of thresholds.

CONCLUSIONS

Despite the wide concerns about the use of 1 to 3× GDP per capita, this threshold is still widely used in the literature. Using this threshold leads to more interventions being recommended as "cost-effective." This study further explore alternatives to the 1 to 3× GDP as a decision rule.

Decision tree-based rules outperform risk scores for childhood asthma prognosis

BACKGROUND

There are no widely accepted prognostic tools for childhood asthma; this is in part due to the multifactorial and time-dependent nature of mechanisms and risk factors that contribute to asthma development. Our study objective was to develop and evaluate the prognostic performance of conditional inference decision tree-based rules using the Pediatric Asthma Risk Score (PARS) predictors as an alternative to the existing logistic regression-based risk score for childhood asthma prediction at 7 years in a high-risk population.

METHODS

The Canadian Asthma Primary Prevention Study data were used to develop, compare, and contrast the prognostic performance (area under the curve [AUC], sensitivity, and specificity) of conditional inference tree-based decision rules to the pediatric asthma risk score for the prediction of childhood asthma at 7 years.

RESULTS

Conditional inference decision tree-based rules have higher prognostic performance (AUC: 0.85; 95% CI: 0.81, 0.88; sensitivity = 47%; specificity = 93%) than the pediatric asthma risk score at an optimal cutoff of ≥6 (AUC: 0.71; 95% CI: 0.67, 0.76; sensitivity = 60%; specificity = 74%). Moreover, the pediatric asthma risk score is not linearly related to asthma risk, and at any given pediatric asthma risk score value, different combinations of its pediatric asthma risk score clinical variables differentially predict asthma risk.

CONCLUSION

Conditional inference tree-based decision rules could be a useful childhood asthma prognostic tool, providing an alternative way to identify unique subgroups of at-risk children, and insights into associations and effect mechanisms that are suggestive of appropriate tailored preventive interventions. However, the feasibility and effectiveness of such decision rules in clinical practice is warranted.

Majority Decision-Making Works Best Under Conditions of Leadership Ambiguity and Shared Task Representations

The effectiveness of decision-making teams depends largely on their ability to integrate and make sense of information. Consequently, teams which more often use majority decision-making may make better quality decisions, but particularly so when they also have task representations which emphasize the elaboration of information relevant to the decision, in the absence of clear leadership. In the present study we propose that (a) majority decision-making will be more effective when task representations are shared, and that (b) this positive effect will be more pronounced when leadership ambiguity (i.e., team members’ perceptions of the absence of a clear leader) is high. These hypotheses were put to the test using a sample comprising 81 teams competing in a complex business simulation for seven weeks. As predicted, majority decision-making was more effective when task representations were shared, and this positive effect was more pronounced when there was leadership ambiguity. The findings extend and nuance earlier research on decision rules, the role of shared task representations, and leadership clarity.

Decision Rules Construction: Algorithm Based on EAV Model

In the paper, an approach for decision rules construction is proposed. It is studied from the point of view of the supervised machine learning task, i.e., classification, and from the point of view of knowledge representation. Generated rules provide comparable classification results to the dynamic programming approach for optimization of decision rules relative to length or support. However, the proposed algorithm is based on transformation of decision table into entity-attribute-value (EAV) format. Additionally, standard deviation function for computation of averages' values of attributes in particular decision classes was introduced. It allows to select from the whole set of attributes only these which provide the highest degree of information about the decision. Construction of decision rules is performed based on idea of partitioning of a decision table into corresponding subtables. In opposite to dynamic programming approach, not all attributes need to be taken into account but only these with the highest values of standard deviation per decision classes. Consequently, the proposed solution is more time efficient because of lower computational complexity. In the framework of experimental results, support and length of decision rules were computed and compared with the values of optimal rules. The classification error for data sets from UCI Machine Learning Repository was also obtained and compared with the ones for dynamic programming approach. Performed experiments show that constructed rules are not far from the optimal ones and classification results are comparable to these obtained in the framework of the dynamic programming extension.

Establishment of Decision Rules and Risk Assessment Model for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

Background: Preoperative accurate prediction of lymph node status is especially important for the formulation of treatment plans for patients with gastric cancer (GC). The purpose of this study was to establish decision rules and a risk assessment model for lymph node metastasis (LNM) in GC using preoperative indicators. Methods: The clinical data of 554 patients who underwent gastrectomy with D2 lymphadenectomy were collected. A 1:1 propensity score matching (PSM) system was used, and the clinical data of the matched 466 patients were further analyzed. The important risk factors for LNM were extracted by the random forest algorithm, and decision rules and nomogram models for LNM were constructed with a classification tree and the "rms" package of R software, respectively. Results: Tumor size (OR: 2.058; P = 0.000), computed tomography (CT) findings (OR: 1.969; P = 0.001), grade (OR: 0.479; P = 0.000), hemoglobin (Hb) (OR: 1.211; P = 0.005), CEA (OR: 1.111; P = 0.017), and CA19-9 (OR: 1.040; P = 0.033) were independent risk factors for LNM in GC. Tumor size did rank first in the ranking of important factors for LNM in GC and was the first-level segmentation of the two initial branches of the classification tree. The accuracy, sensitivity, specificity, and positive predictive value of the decision rules in diagnosing preoperative LNM in GC were 75.6, 85.7, 73.9, 73.5, and 79.3%, respectively. The accuracy, sensitivity, and specificity of the risk assessment model in predicting preoperative LNM in GC were 79.3, 80.3, and 79.4%, respectively. Conclusion: Tumor size was the most important factor for evaluating LNM in GC. This decision rules and nomogram model constructed to take into account tumor size, CT findings, grade, hemoglobin, CEA, and CA19-9 effectively predicted the incidence of LNM in preoperative GC.

On Intersectionality: How Complex Patterns of Discrimination Can Emerge From Simple Stereotypes

Patterns of discrimination are often complex (i.e., multiplicative), with different identities combining to yield especially potent discrimination. For example, Black men are disproportionately stopped by police to a degree that cannot be explained by the simple (i.e., additive) effects of being Black and being male. Researchers often posit corresponding mental representations (e.g., intersectional stereotypes for Black men) to account for these complex outcomes. We suggest that complex discrimination can be explained by simple stereotypes combined with threshold models of behavior-for example, "if someone's threat level seems higher than X, stop that person." Simulations provide proof of this concept. We show how gender-by-race discrimination in both promotions and police stops can be explained by simple stereotypes. We also explore race-by-age discrimination in police stops, in which racial disparities are greater for young adolescents. This work suggests that complex behaviors can sometimes arise from relatively simple cognitions.

Clinical Significance of Serum CA125, CA19-9, CA72-4, and Fibrinogen-to-Lymphocyte Ratio in Gastric Cancer With Peritoneal Dissemination

Background: Relevant serum tumor markers have been indicated to be associated with peritoneal dissemination (PD) of gastric cancer (GC). Fibrinogen has been shown to play an important role in the systemic inflammatory response (SIR) and in tumor progression. However, the clinical significance of the fibrinogen-to-lymphocyte ratio (FLR) in GC with PD has not been studied.

Methods: The clinical data of 391 patients with GC were collected, including 86 cases of PD. Then, 1:3 matching was performed by propensity score matching (PSM), and the clinical data of the matched 344 patients were analyzed by univariate and multivariate conditional logistic regression. Classification tree analysis was used to obtain the decision rules and a random forest algorithm to extract the important risk factors of PD in GC. A nomogram model for risk assessment of PD in GC was established by using the rms package of R software.

Results: Univariate analysis showed that the factors related to PD in GC were: carbohydrate antigen (CA) 125 (P < 0.0001), CA19-9 (P < 0.0001), CA72-4 (P < 0.0001), FLR (P < 0.0001), neutrophil-to-lymphocyte ratio (NLR) (P < 0.0001), albumin-to- lymphocyte ratio (ALR) (P < 0.0001), platelet-to-lymphocyte ratio (PLR) (P = 0.013), and carcinoembryonic antigen (CEA) (P = 0.031). Conditional logistic regression found that CA125 (OR: 1.046; P < 0.0001), CA19-9 (OR: 1.002; P < 0.0001), and FLR (OR: 1.266; P = 0.024) were independent risk factors for GC with PD. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the decision rules for detecting PD of GC were 89.5, 77.4, 94.0, 82.8, and 91.8%, respectively. According to the important variables identified by the classification tree and random forest algorithm, the risk assessment model of PD in GC was established. The accuracy, sensitivity, and specificity of the model were 91, 89.5, and 79.5%, respectively.

Conclusion: CA125 > 17.3 U/ml, CA19-9 > 27.315 U/ml, and FLR > 2.555 were the risk factors for GC with PD. The decision rules and nomogram model constructed by CA125, CA19-9, CA72-4, and FLR can correctly predict the risk of PD in GC.

Decision Tree Ensemble Method for Analyzing Traffic Accidents of Novice Drivers in Urban Areas

Presently, there is a critical need to analyze traffic accidents in order to mitigate their terrible economic and human impact. Most accidents occur in urban areas. Furthermore, driving experience has an important effect on accident analysis, since inexperienced drivers are more likely to suffer fatal injuries. This work studies the injury severity produced by accidents that involve inexperienced drivers in urban areas. The analysis was based on data provided by the Spanish General Traffic Directorate. The information root node variation (IRNV) method (based on decision trees) was used to get a rule set that provides useful information about the most probable causes of fatalities in accidents involving inexperienced drivers in urban areas. This may prove useful knowledge in preventing this kind of accidents and/or mitigating their consequences.

What, where and when: spatial foraging decisions in primates

When exploiting the environment, animals have to discriminate, track, and integrate salient spatial cues to navigate and identify goal sites. Actually, they have to know what can be found (e.g. what fruit), where (e.g. on which tree) and when (in what season or moment of the year). This is very relevant for primate species as they often live in seasonal and relatively unpredictable environments such as tropical forests. Here, we review and compare different approaches used to investigate primate spatial foraging strategies: from direct observations of wild primates to predictions from statistical simulations, including experimental approaches on both captive and wild primates, and experiments in captivity using virtual reality technology. Within this framework, most of these studies converge to show that many primate species can (i) remember the location of most of food resources well, and (ii) often seem to have a goal-oriented path towards spatially permanent resources. Overall, primates likely use mental maps to plan different foraging strategies to enhance their fitness. The majority of studies suggest that they may organise spatial information on food resources into topological maps: they use landmarks to navigate and encode local spatial information with regard to direction and distance. Even though these studies were able to show that primates can remember food quality (what) and its location (where), still very little is known on how they incorporate the temporal knowledge of available food (when). Future studies should attempt to increase our understanding of the potential of primates to learn temporal patterns and how both socio-ecological differences among species and their cognitive abilities influence such behavioural strategies.

Zebrafish aggression on the sub-second time scale: evidence for mutual motor coordination and multi-functional attack manoeuvres

Most animals fight by repeating complex stereotypic behaviours, yet the internal structure of these behaviours has rarely been dissected in detail. We characterized the internal structure of fighting behaviours by developing a machine learning pipeline that measures and classifies the behaviour of individual unmarked animals on a sub-second time scale. This allowed us to quantify several previously hidden features of zebrafish fighting strategies. We found strong correlations between the velocity of the attacker and the defender, indicating a dynamic matching of approach and avoidance efforts. While velocity matching was ubiquitous, the spatial dynamics of attacks showed phase-specific differences. Contest-phase attacks were characterized by a paradoxical sideways attraction of the retreating animal towards the attacker, suggesting that the defender combines avoidance manoeuvres with display-like manoeuvres. Post-resolution attacks lacked display-like features and the defender was avoidance focused. From the perspective of the winner, game-theory modelling further suggested that highly energetically costly post-resolution attacks occurred because the winner was trying to increase its relative dominance over the loser. Overall, the rich structure of zebrafish motor coordination during fighting indicates a greater complexity and layering of strategies than has previously been recognized.

With a little help from a computer: discriminating between bacterial and viral meningitis based on dominance-based rough set approach analysis

Differential Diagnosis of bacterial and viral meningitis remains an important clinical problem. A number of methods to assist in the diagnoses of meningitis have been developed, but none of them have been found to have high specificity with 100% sensitivity.We conducted a retrospective analysis of the medical records of 148 children hospitalized in St. Joseph Children's Hospital in Poznań. In this study, we applied for the first time the original methodology of dominance-based rough set approach (DRSA) to diagnostic patterns of meningitis data and represented them by decision rules useful in discriminating between bacterial and viral meningitis. The induction algorithm is called VC-DomLEM; it has been implemented as software package called jMAF (http://www.cs.put.poznan.pl/jblaszczynski/Site/jRS.html), based on java Rough Set (jRS) library.In the studied group, there were 148 patients (78 boys and 70 girls), and the mean age was 85 months. We analyzed 14 attributes, of which only 4 were used to generate the 6 rules, with C-reactive protein (CRP) being the most valuable.Factors associated with bacterial meningitis were: CRP level ≥86 mg/L, number of leukocytes in cerebrospinal fluid (CSF) ≥4481 μL, symptoms duration no longer than 2 days, or age less than 1 month. Factors associated with viral meningitis were CRP level not higher than 19 mg/L, or CRP level not higher than 84 mg/L in a patient older than 11 months with no more than 1100 μL leukocytes in CSF.We established the minimum set of attributes significant for classification of patients with meningitis. This is new set of rules, which, although intuitively anticipated by some clinicians, has not been formally demonstrated until now.

Diagnostic Accuracy of Clinical Decision Rules to Exclude Fractures in Acute Ankle Injuries: Systematic Review and Meta-analysis

BACKGROUND

Ankle decision rules are developed to expedite patient care and reduce the number of radiographs of the ankle and foot. Currently, only three systematic reviews have been conducted on the accuracy of the Ottawa Ankle and Foot Rules (OAFR) in adults and children. However, no systematic review has been performed to determine the most accurate ankle decision rule.

OBJECTIVES

The purpose of this study is to examine which clinical decision rules are the most accurate for excluding ankle fracture after acute ankle trauma.

METHODS

A systematic search was conducted in the databases PubMed, CINAHL, PEDro, ScienceDirect, and EMBASE. The sensitivity, specificity, likelihood ratios, and diagnostic odds ratio of the included studies were calculated. A meta-analysis was conducted if the accuracy of a decision rule was available from at least three different experimental studies.

RESULTS

Eighteen studies satisfied the inclusion criteria. These included six ankle decision rules, specifically, the Ottawa Ankle Rules, Tuning Fork Test, Low Risk Ankle Rule, Malleolar and Midfoot Zone Algorithms, and the Bernese Ankle Rules. Meta-analysis of the Ottawa Ankle Rules (OAR), OAFR, Bernese Ankle Rules, and the Malleolar Zone Algorithm resulted in a negative likelihood ratio of 0.12, 0.14, 0.39, and 0.23, respectively.

CONCLUSION

The OAR and OAFR are the most accurate decision rules for excluding fractures in the event of an acute ankle injury.

Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning

Background: Markov decision process (MDP) models are powerful tools. They enable the derivation of optimal treatment policies but may incur long computational times and generate decision rules that are challenging to interpret by physicians. Methods: In an effort to improve usability and interpretability, we examined whether Poisson regression can approximate optimal hypertension treatment policies derived by an MDP for maximizing a patient's expected discounted quality-adjusted life years. Results: We found that our Poisson approximation to the optimal treatment policy matched the optimal policy in 99% of cases. This high accuracy translates to nearly identical health outcomes for patients. Furthermore, the Poisson approximation results in 104 additional quality-adjusted life years per 1000 patients compared to the Seventh Joint National Committee's treatment guidelines for hypertension. The comparative health performance of the Poisson approximation was robust to the cardiovascular disease risk calculator used and calculator calibration error. Limitations: Our results are based on Markov chain modeling. Conclusions: Poisson model approximation for blood pressure treatment planning has high fidelity to optimal MDP treatment policies, which can improve usability and enhance transparency of more personalized treatment policies.

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease's symptoms, with the help of various therapies. In the case of Parkinson's disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist's diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient 'well-being' scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naïve Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD.

Age Weights for Health Services Derived from the Relative Social Willingness-to-Pay Instrument

The effect of a patient's age on the social valuation of health services remains controversial, with empirical results varying in magnitude and implying a different age-value profile. This article employs a new methodology to re-examine these questions. Data were obtained from 2 independent Web-based surveys that administered the Relative Social Willingness to Pay instrument. In the first survey, the age of the patient receiving a life-saving service was varied. Patients were left with either poor mental or physical health. In the second survey, patient age was varied for a service that fully cured the patient's poor mental or physical health. In total, therefore, 4 sets of age weights were obtained: weights for life-extending services with poor physical or mental health outcomes and weights for quality-of-life improvement for patients in poor mental or physical health. Results were consistent. Increasing age was associated in each case with a monotonic decrease in the social valuation of the services. The decrease in value was quantitatively small until age 60 years. By age 80 years, the social value of services had declined by about 50%. The decline commenced at an earlier age in the context of physical health, although the magnitude of the decrement by age 80 years was unrelated to the type of service. With 1 exception, there was little difference in the valuation of services by the age of the survey respondent. Respondents aged >60 years placed a lower, not higher, value on quality-of-life improvement for elderly individuals than other respondents. There was no difference in the valuation of life-extending services.

Correlated pay-offs are key to cooperation

The general belief that cooperation and altruism in social groups result primarily from kin selection has recently been challenged, not least because results from cooperatively breeding insects and vertebrates have shown that groups may be composed mainly of non-relatives. This allows testing predictions of reciprocity theory without the confounding effect of relatedness. Here, we review complementary and alternative evolutionary mechanisms to kin selection theory and provide empirical examples of cooperative behaviour among unrelated individuals in a wide range of taxa. In particular, we focus on the different forms of reciprocity and on their underlying decision rules, asking about evolutionary stability, the conditions selecting for reciprocity and the factors constraining reciprocal cooperation. We find that neither the cognitive requirements of reciprocal cooperation nor the often sequential nature of interactions are insuperable stumbling blocks for the evolution of reciprocity. We argue that simple decision rules such as 'help anyone if helped by someone' should get more attention in future research, because empirical studies show that animals apply such rules, and theoretical models find that they can create stable levels of cooperation under a wide range of conditions. Owing to its simplicity, behaviour based on such a heuristic may in fact be ubiquitous. Finally, we argue that the evolution of exchange and trading of service and commodities among social partners needs greater scientific focus.

Future Costs, Fixed Healthcare Budgets, and the Decision Rules of Cost-Effectiveness Analysis

Life-saving medical technologies result in additional demand for health care due to increased life expectancy. However, most economic evaluations do not include all medical costs that may result from this additional demand in health care and include only future costs of related illnesses. Although there has been much debate regarding the question to which extent future costs should be included from a societal perspective, the appropriate role of future medical costs in the widely adopted but more narrow healthcare perspective has been neglected. Using a theoretical model, we demonstrate that optimal decision rules for cost-effectiveness analyses assuming fixed healthcare budgets dictate that future costs of both related and unrelated medical care should be included. Practical relevance of including the costs of future unrelated medical care is illustrated using the example of transcatheter aortic valve implantation. Our findings suggest that guidelines should prescribe inclusion of these costs.

Understanding and Reducing Inconsistency in Seatbelt-Use Decisions: Findings from a Cardinal Decision Issue Perspective

This article has two aims. The first is to present results that partly explain why some automobile drivers choose to use their seatbelts only part time, thereby exposing themselves to unnecessary risk. The second is to offer and illustrate the "cardinal decision issue perspective"((1)) as a tool for guiding research and development efforts that focus on complex real-life decision behaviors that can entail wide varieties of risk, including but not limited to inconsistent seatbelt use. Each of 24 young male participants drove an instrumented vehicle equipped to record continuously seatbelt use as well as other driving data. After all trips were finished, each participant completed an interview designed to reconstruct how he made randomly selected seatbelt-use decisions under specified conditions. The interview also examined whether and how drivers established "decision policies" regarding seatbelt use. Such policies were good predictors of inconsistent seatbelt use. Drivers who had previously adopted policies calling for consistent seatbelt use were significantly more likely than others to actually drive belted. Meta-decisions about seatbelt policy adoption appeared to rest on factors such as whether the driver had ever been asked to consider selecting a policy. Whether a driver made an ad hoc, on-the-spot seatbelt-use decision was associated with a perceived need to make such a decision. Finally, participants with full-time policies were especially likely to deploy their seatbelts by default, without recognizing the need to decide about belt use on a trip-by-trip basis. We end with recommendations for reducing inconsistencies in seatbelt use in actual practice.

Robust detection of premature ventricular contractions using sparse signal decomposition and temporal features

An automated noise-robust premature ventricular contraction (PVC) detection method is proposed based on the sparse signal decomposition, temporal features, and decision rules. In this Letter, the authors exploit sparse expansion of electrocardiogram (ECG) signals on mixed dictionaries for simultaneously enhancing the QRS complex and reducing the influence of tall P and T waves, baseline wanders, and muscle artefacts. They further investigate a set of ten generalised temporal features combined with decision-rule-based detection algorithm for discriminating PVC beats from non-PVC beats. The accuracy and robustness of the proposed method is evaluated using 47 ECG recordings from the MIT/BIH arrhythmia database. Evaluation results show that the proposed method achieves an average sensitivity of 89.69%, and specificity 99.63%. Results further show that the proposed decision-rule-based algorithm with ten generalised features can accurately detect different patterns of PVC beats (uniform and multiform, couplets, triplets, and ventricular tachycardia) in presence of other normal and abnormal heartbeats.

Identifying cost-effective predictive rules of amyloid-β level by integrating neuropsychological tests and plasma-based markers

BACKGROUND

Detecting participants who are positive for amyloid-β (Aβ) pathology is germane in designing prevention trials by enriching for those cases that are more likely to be amyloid positive. Existing brain amyloid measurement techniques, such as the Pittsburgh Compound B-positron emission tomography and cerebrospinal fluid, are not reasonable first-line approaches limited by either feasibility or cost.

OBJECTIVE

We aimed to identify simple and cost-effective rules that can predict brain Aβ level by integrating both neuropsychological measurements and blood-based markers.

METHOD

Several decision tree models were built for extracting the predictive rules based on the Alzheimer's Disease Neuroimaging Initiative cohort.

RESULTS

We successfully extracted predictive rules of Aβ level. For cognitive function variables, cases above the 45th percentile in total cognitive score (TOTALMOD), above the 52nd percentile of delayed word recall, and above the 70th percentile in orientation resulted in a group that was highly enriched for amyloid negative cases. Conversely scoring below the 15th percentile of TOTALMOD resulted in a group highly enriched for amyloid positive cases. For blood protein markers, scoring below the 57th percentile for apolipoprotein E (ApoE) levels (irrespective of genotype) enriched two fold for the risk of being amyloid positive. In the high ApoE cases, scoring above the 60th percentile for transthyretin resulted in a group that was >90% amyloid negative. A third decision tree using both cognitive and blood-marker data slightly improved the classification of cases.

CONCLUSION

Our study demonstrated that the integration of the neuropsychological measurements and blood-based markers significantly improved prediction accuracy. The prediction model has led to several simple rules, which have a great potential of being naturally translated into clinical settings such as enrichment screening for AD prevention trials of anti-amyloid treatments.

A framework to assess the value of subgroup analyses when the overall treatment effect is significant

Although subgroup analysis has been developed and widely used for many years, it is still not clear whether we should perform and how to perform such subgroup analyses when the overall treatment effect is significant. In this paper, we develop a framework to assess and compute the long-term impact of different strategies to perform subgroup analysis. We propose two performance measures: the average gain for patients in the future (E) and the probability of recommending a change to a worse treatment at individual patient level (P). Five families of decision rules are applied under different assumptions for the individual treatment effect (TE) variation. Three distributions reflecting optimistic, moderate, and pessimistic scenarios are assumed for true treatment effects across studies. This framework allows us to compare subgroup analyses decision rules, and we demonstrate through simulation studies that there are decision rules for subgroup analysis which can decrease P and increase E simultaneously compared to the situation of no subgroup analysis. These rules are much more liberal than the usual superiority testing. The latter typically implies a dramatic decrease in E.

Flexible designs for phase II comparative clinical trials involving two response variables

The aim of phase II clinical trials is to determine whether an experimental treatment is sufficiently promising and safe to justify further testing. The need for reduced sample size arises naturally in phase II clinical trials owing to both technical and ethical reasons, motivating a significant part of research in the field during recent years, while another significant part of the research effort is aimed at more complex therapeutic schemes that demand the consideration of multiple endpoints to make decisions. In this paper, our attention is restricted to phase II clinical trials in which two treatments are compared with respect to two dependent dichotomous responses proposing some flexible designs. These designs permit the researcher to terminate the clinical trial when high rates of favorable or unfavorable outcomes are observed early enough requiring in this way a small number of patients. From the mathematical point of view, the proposed designs are defined on bivariate sequences of multi-state trials, and the corresponding stopping rules are based on various distributions related to the waiting time until a certain number of events appear in these sequences. The exact distributions of interest, under a unified framework, are studied using the Markov chain embedding technique, which appears to be very useful in clinical trials for the sample size determination. Tables of expected sample size and power are presented. The numerical illustration showed a very good performance for these new designs.

Which decision rules meet methodological standards in children with febrile neutropenia? Results of a systematic review and analysis

BACKGROUND

Clinical decision rules (CDRs) have sought to identify the few children with chemotherapy-induced febrile neutropenia (FN) really at risk of severe infection to reduce the invasive procedures and costs for those at low risk. Several reports have shown that most rules do not perform well enough to be clinically useful. Our objective was to analyze the derivation methods and validation procedures of these CDRs.

PROCEDURE

A systematic review using Medline, Ovid, Refdoc, and the Cochrane Library through December 2012 searched for all CDRs predicting the risk of severe infection and/or complications in children with chemotherapy-induced FN. Their methodological quality was analyzed by 17 criteria for deriving and validating a CDR identified in the literature. The criteria published by the Evidence Based Medicine Working Group were applied to the published validations of each CDR to assess their level of evidence.

RESULTS

The systematic research identified 612 articles and retained 12 that derived CDRs. Overall, the CDRs met a median of 65% of the methodological criteria. The criteria met least often were that the rule made clinical sense, or described the course of action, or that the variables and the CDR were reproducible. Only one CDR, developed in South America, met all methodological criteria and provided the highest level of evidence; unfortunately it was not reproducible in Europe.

CONCLUSION

Only one CDR developed for children with FN met all methodological standards and reached the highest level of evidence.

Risk preferences: consequences for test and treatment thresholds and optimal cutoffs

Risk attitudes include risk aversion as well as higher-order risk preferences such as prudence and temperance. This article analyzes the effects of such preferences on medical test and treatment decisions, represented either by test and treatment thresholds or-when the test result is not given-by optimal cutoff values for diagnostic tests. For a risk-averse decision maker, effective treatment is a risk-reducing strategy since it prevents the low health outcome of forgoing treatment in the sick state. Compared with risk neutrality, risk aversion thus lowers both the test and the treatment threshold and decreases the optimal test cutoff value. Risk vulnerability, which combines risk aversion, prudence, and temperance, is relevant if there is a comorbidity risk: thresholds and optimal cutoff values decrease even more. Since common utility functions imply risk vulnerability, our findings suggest that diagnostics in low prevalence settings (e.g., screening) may be considered more beneficial when risk preferences are taken into account.

Evaluation of markers and risk prediction models: overview of relationships between NRI and decision-analytic measures

BACKGROUND

For the evaluation and comparison of markers and risk prediction models, various novel measures have recently been introduced as alternatives to the commonly used difference in the area under the receiver operating characteristic (ROC) curve (ΔAUC). The net reclassification improvement (NRI) is increasingly popular to compare predictions with 1 or more risk thresholds, but decision-analytic approaches have also been proposed.

OBJECTIVE

. We aimed to identify the mathematical relationships between novel performance measures for the situation that a single risk threshold T is used to classify patients as having the outcome or not.

METHODS

. We considered the NRI and 3 utility-based measures that take misclassification costs into account: difference in net benefit (ΔNB), difference in relative utility (ΔRU), and weighted NRI (wNRI). We illustrate the behavior of these measures in 1938 women suspect of having ovarian cancer (prevalence 28%).

RESULTS

. The 3 utility-based measures appear to be transformations of each other and hence always lead to consistent conclusions. On the other hand, conclusions may differ when using the standard NRI, depending on the adopted risk threshold T, prevalence P, and the obtained differences in sensitivity and specificity of the 2 models that are compared. In the case study, adding the CA-125 tumor marker to a baseline set of covariates yielded a negative NRI yet a positive value for the utility-based measures.

CONCLUSIONS

. The decision-analytic measures are each appropriate to indicate the clinical usefulness of an added marker or compare prediction models since these measures each reflect misclassification costs. This is of practical importance as these measures may thus adjust conclusions based on purely statistical measures. A range of risk thresholds should be considered in applying these measures.

When is it time to move to the next raspberry bush? Foraging rules in human visual search

Animals, including humans, engage in many forms of foraging behavior in which resources are collected from the world. This paper examines human foraging in a visual search context. A real-world analog would be berry picking. The selection of individual berries is not the most interesting problem in such a task. Of more interest is when does a forager leave one patch or berry bush for the next one? Marginal Value Theorem (MVT; Charnov, 1976) predicts that observers will leave a patch when the instantaneous yield from that patch drops below the average yield from the entire "field." Experiments 1, 2, 3, and 4 show that MVT gives a good description of human behavior for roughly uniform collections of patches. Experiments 5 and 6 show strong departures from MVT when patch quality varies and when visual information is degraded.

A transfer of technology from engineering: use of ROC curves from signal detection theory to investigate information processing in the brain during sensory difference testing

This article reviews a beneficial effect of technology transfer from Electrical Engineering to Food Sensory Science. Specifically, it reviews the recent adoption in Food Sensory Science of the receiver operating characteristic (ROC) curve, a tool that is incorporated in the theory of signal detection. Its use allows the information processing that takes place in the brain during sensory difference testing to be studied and understood. The review deals with how Signal Detection Theory, also called Thurstonian modeling, led to the adoption of a more sophisticated way of analyzing the data from sensory difference tests, by introducing the signal-to-noise ratio, d', as a fundamental measure of perceived small sensory differences. Generally, the method of computation of d' is a simple matter for some of the better known difference tests like the triangle, duo-trio and 2-AFC. However, there are occasions when these tests are not appropriate and other tests like the same-different and the A Not-A test are more suitable. Yet, for these, it is necessary to understand how the brain processes information during the test before d' can be computed. It is for this task that the ROC curve has a particular use.

Psychophysical reverse correlation with multiple response alternatives

Psychophysical reverse-correlation methods such as the "classification image" technique provide a unique tool to uncover the internal representations and decision strategies of individual participants in perceptual tasks. Over the past 30 years, these techniques have gained increasing popularity among both visual and auditory psychophysicists. However, thus far, principled applications of the psychophysical reverse-correlation approach have been almost exclusively limited to two-alternative decision (detection or discrimination) tasks. Whether and how reverse-correlation methods can be applied to uncover perceptual templates and decision strategies in situations involving more than just two response alternatives remain largely unclear. Here, the authors consider the problem of estimating perceptual templates and decision strategies in stimulus identification tasks with multiple response alternatives. They describe a modified correlational approach, which can be used to solve this problem. The approach is evaluated under a variety of simulated conditions, including different ratios of internal-to-external noise, different degrees of correlations between the sensory observations, and various statistical distributions of stimulus perturbations. The results indicate that the proposed approach is reasonably robust, suggesting that it could be used in future empirical studies.

Inference of cancer-specific gene regulatory networks using soft computing rules

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Clinical features from the history and physical examination that predict the presence or absence of pulmonary embolism in symptomatic emergency department patients: results of a prospective, multicenter study

STUDY OBJECTIVE

Prediction rules for pulmonary embolism use variables explicitly shown to estimate the probability of pulmonary embolism. However, clinicians often use variables that have not been similarly validated, yet are implicitly believed to modify probability of pulmonary embolism. The objective of this study is to measure the predictive value of 13 implicit variables.

METHODS

Patients were enrolled in a prospective cohort study from 12 centers in the United States; all had an objective test for pulmonary embolism (D-dimer, computed tomographic angiography, or ventilation-perfusion scan). Clinical features including 12 predefined previously validated (explicit) variables and 13 variables not part of existing prediction rules (implicit) were prospectively recorded at presentation. The primary outcome was venous thromboembolism (pulmonary embolism or deep venous thrombosis), diagnosed by imaging up to 45 days after enrollment. Variables with adjusted odds ratios from logistic regression with 95% confidence intervals not crossing unity were considered significant.

RESULTS

Seven thousand nine hundred forty patients (7.2% venous thromboembolism positive) were enrolled. Mean age was 49 years (standard deviation 17 years) and 67% were female patients. Eight of 13 implicit variables were significantly associated with venous thromboembolism; those with an adjusted odds ratio (OR) greater than 1.5 included non-cancer-related thrombophilia (OR 1.99), pleuritic chest pain (OR 1.53), and family history of venous thromboembolism (OR 1.51). Implicit variables that predicted no venous thromboembolism outcome included substernal chest pain, female sex, and smoking. Nine of 12 explicit variables predicted a positive outcome of venous thromboembolism, including patient history of pulmonary embolism or deep venous thrombosis in the past, unilateral leg swelling, recent surgery, estrogen, hypoxemia, and active malignancy.

CONCLUSION

In symptomatic outpatients being considered for possible pulmonary embolism, non-cancer-related thrombophilia, pleuritic chest pain, and family history of venous thromboembolism increase probability of pulmonary embolism or deep venous thrombosis. Other variables that are part of existing pretest probability systems were validated as important predictors in this diverse sample of US emergency department patients.

Microarray-based cancer prediction using soft computing approach

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

Cognitive processes underlying human mate choice: The relationship between self-perception and mate preference in Western society

This study tested two hypotheses concerning the cognitive processes underlying human mate choice in Western society: (i) mate preference is conditional in that the selectivity of individuals' mate preference is based on their perception of themselves as long-term partners, and (ii) the decision rule governing such conditional mate preference is based on translating perception of oneself on a given attribute into a comparable selectivity of preference for the same attribute in a mate. Both hypotheses were supported. A two-part questionnaire was completed by 978 heterosexual residents of Ithaca, New York, aged 18-24; they first rated the importance they placed on 10 attributes in a long-term partner and then rated their perception of themselves on those same attributes. Both women and men who rated themselves highly were significantly more selective in their mate preference. When the 10 attributes were grouped into four evolutionarily relevant categories (indicative of wealth and status, family commitment, physical appearance, and sexual fidelity), the greatest amount of variation in the selectivity of mate preference in each category was explained by self-perception in the same category of attributes. We conclude that, in Western society, humans use neither an "opposites-attract" nor a "reproductive-potentials-attract" decision rule in their choice of long-term partners but rather a "likes-attract" rule based on a preference for partners who are similar to themselves across a number of characteristics.