Children's Predictions Research Articles

IgA Nephropathy Prediction in Children with Machine Learning Algorithms

Immunoglobulin A nephropathy (IgAN) is the most common primary glomerular disease all over the world and it is a major cause of renal failure. IgAN prediction in children with machine learning algorithms has been rarely studied. We retrospectively analyzed the electronic medical records from the Nanjing Eastern War Zone Hospital, chose eXtreme Gradient Boosting (XGBoost), random forest (RF), CatBoost, support vector machines (SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM) models in order to predict the probability that the patient would not reach or reach end-stage renal disease (ESRD) within five years, used the chi-square test to select the most relevant 16 features as the input of the model, and designed a decision-making system (DMS) of IgAN prediction in children that is based on XGBoost and Django framework. The receiver operating characteristic (ROC) curve was used in order to evaluate the performance of the models and XGBoost had the best performance by comparison. The AUC value, accuracy, precision, recall, and f1-score of XGBoost were 85.11%, 78.60%, 75.96%, 76.70%, and 76.33%, respectively. The XGBoost model is useful for physicians and pediatric patients in providing predictions regarding IgAN. As an advantage, a DMS can be designed based on the XGBoost model to assist a physician to effectively treat IgAN in children for preventing deterioration.

Risk Prediction in Children With Congenital Heart Disease: Business As Usual-Or Not?

Risk Prediction in Children With Congenital Heart Disease: Business As Usual-Or Not?

Application of Machine Learning Techniques for Enuresis Prediction in Children.

As a subset of artificial intelligence, machine learning techniques (MLTs) may evaluate very large and raw datasets. In this study, the aim is to establish a model by MLT for the prediction of enuresis in children. The study included 8,071 elementary school students. A total of 704 children had enuresis. For analysis of data with MLT, another group including 704 nonenuretic children was structured with stratified sampling. Out of 34 independent variables, 14 with high feature values significantly affecting enuresis were selected. A model of estimation was created by training the data. Fourteen independent variables in order of feature importance value were starting age of toilet training, having urinary urgency, holding maneuvers to prevent voiding, frequency of defecation, history of enuresis in mother and father, having child's own room, parent's education level, history of enuresis in siblings, consanguineous marriage, incomplete bladder emptying, frequent voiding, gender, history of urinary tract infection, and surgery in the past. The best MLT algorithm for the prediction of enuresis was determined as logistic regression algorithm. The total accuracy rate of the model in prediction was 81.3%. MLT might provide a faster and easier evaluation process for studies on enuresis with a large dataset. The model in this study may suggest that selected variables with high feature values could be preferred with priority in any screening studies for enuresis. MLT may prevent clinical errors due to human cognitive biases and may help the physicians to be proactive in diagnosis and treatment of enuresis.

Hands-On Exploration of Cubes' Floating and Sinking Benefits Children's Subsequent Buoyancy Predictions.

Children accrue experiences with buoyancy on a daily basis, yet research paints a mixed picture of children’s buoyancy knowledge. Whereas children’s predictions and explanations of the floating and the sinking of common objects are often based on a single feature (e.g., mass or facts), children’s predictions of novel cubes reveal solution strategies based on mass and volume integrations. Correspondingly, category learning theory suggests that categories (e.g., floaters vs. sinkers) are easier to identify when items mainly vary from one another in the relevant defining features. For example, a set of cubes only varies in mass and volume and hence density, thereby being able to highlight the deterministic role of density when placed in water. Here we asked how item variation during hands-on exploration affects children’s subsequent predictions and explanations of buoyancy. Kindergarteners and first-, second-, and third-grade children individually explored either a set of 10 systematically varied cubes (i.e., systematic condition; n = 95) or a set of 10 common objects (i.e., non-systematic condition; n = 96) in a water basin. Next, the children predicted the buoyancy of five new cubes and five new common objects one at a time. Subsequently, the children explained their predictions one subset at a time. The children in the systematic condition were more accurate in their predictions of the test cubes than the children in the non-systematic condition. Latent class regression analyses identified three cube prediction solution strategies. The children in the systematic condition were more likely to use a strategy in which buoyancy decisions were made based on an accurate integration of mass and volume, while the children in the non-systematic condition were more likely to use a strategy in which mass was given more predictive load than volume. A third strategy was characterized by guessing. Latent class analyses of the children’s explanations revealed different explanation strategies, each appealing to several features, but as hypothesized, no clear condition differences were found. The findings indicate that even 5 min of exploration with systematically varied cubes can already help children use an advanced buoyancy prediction strategy. This provides evidence in favor of using category learning theory to inform early science education design.

Smart bed based daytime behavior prediction in Children with autism spectrum disorder - A Pilot Study

Monitoring the sleep patterns of children with autism spectrum disorders (ASD) and understanding how sleep quality influences their daytime behavior is an important issue that has received very limited attention. Polysomnography (PSG) is commonly used as a gold standard for evaluating sleep quality in children and adults. However, the intrusive nature of sensors used as part of PSG can themselves affect sleep and is, therefore, not suitable for children with ASD. In this study, we evaluate an unobtrusive and inexpensive bed system for in-home, long-term sleep quality monitoring using ballistocardiogram (BCG) signals. Using the BCG signals from this smart bed system, we define “restlessness” as a surrogate sleep quality estimator. Using this sleep feature, we build predictive models for daytime behavior based on 1-8 previous nights of sleep. Specifically, we use two supervised machine learning algorithms namely support vector machine (SVM) and artificial neural network (ANN). For all daytime behaviors, we achieve more than 78% and 79% accuracy of correctly predicting behavioral issues with both SVM and ANN classifiers, respectively. Our findings indicate the usefulness of our designed bed system and how the restlessness feature can improve the prediction performance.

Prediction of Children’s Blood Lead Levels from Exposure to Lead in Schools’ Drinking Water—A Case Study in Tennessee, USA

Lead (Pb) exposure can delay children’s mental development and cause behavioral disorders and IQ deficits. With children spending a significant portion of their time at schools, it is critical to investigate the lead concentration in schools’ drinking water to prevent children’s exposure. The objectives of this work were to predict students’ geometric mean (GM) blood lead levels (BLLs), the fractions of at-risk students (those with BLLs > 5 μg/dL), and the total number of at-risk students in one Tennessee school district. School drinking water lead concentration data collected in 2019 were input into the Integrated Exposure Uptake Biokinetic (IEUBK) model and the Bowers’ model to predict BLLs for elementary school students and secondary school students, respectively. Sensitivity analyses were conducted for both models. Drinking water concentrations were qualitatively compared with data collected in 2017. Two scenarios were evaluated for each model to provide upper and median estimates. The weighted GM BLL upper and median estimates for elementary school students were 2.35 μg/dL and 0.99 μg/dL, respectively. This equated to an upper estimate of 1300 elementary school students (5.8%) and a median estimate of 140 elementary school students (0.6%) being at risk of elevated BLLs. Similarly, the weighted GM BLL upper and median estimates for secondary school students were 2.99 μg/dL and 1.53 μg/dL, respectively, and equated to an upper estimate of 6900 secondary school students (13.6%) and a median estimate of 300 secondary school students (0.6%) being at risk of elevated BLLs. Drinking water remediation efforts are recommended for schools exhibiting water lead concentrations greater than 15 μg/L. Site-specific soil lead concentration data are recommended since the IEUBK was deemed sensitive to soil lead concentrations. For this reason, soil lead remediation may have a greater impact on lowering children’s BLLs than drinking water lead remediation. Remediation efforts are especially vital at elementary schools to reduce the population’s baseline BLL and thus the BLL projected by Bowers’ model.

Mortality Analysis and Model Prediction of Children Under 5 years Old in a City of Northwest China

Mortality Analysis and Model Prediction of Children Under 5 years Old in a City of Northwest China

Gender Differences in the Prediction of Children’s Weight Status from Measures of Non-Eating and Eating Self-Regulation

Abstract Objectives The goal of this study was to investigate gender differences in the prediction of BMI z-scores in children ages 7 to 9. Predictors included well-known measures of non-eating and eating self-regulation assessed during the preschool years (ages 4 to 5). Methods Participants were part of a longitudinal study examining eating behaviors in Hispanic families with low-income levels living in a large urban city in southeast United States. Children and their parents were seen at Time 1 (ages 4–5), Time 2 (ages 5 ½-6 ½), and at Time 3 (ages 7–9). One hundred and thirty families had data on all variables needed for the current analyses. Both parent-report questionnaires as well as observational protocols were used to measure child self-regulation. Parent-report questionnaires included satiety responsiveness and food responsiveness (two child eating self-regulatory measures known to be associated with childhood obesity) and effortful control (a self-regulatory aspect of child temperament). Observational protocols included eating in the absence of hunger (a well-known child eating self-regulation measure) and executive functioning, delay of gratification, and emotional regulation (all measures of non-eating self-regulation). Height and weight were objectively measured on the children. Regressions were conducted to predict child weight status at ages 7 to 9 from measures of child eating and non-eating self-regulatory processes. Data on boys and girls were analyzed in different regressions. Parent-reported measures were analyzed separately from observational measures. Results Only the regression for girls, predicting Time 3 child BMI z-scores from observational measures, showed significant effects over and above the effects of Time 1 child BMI z-scores. Eating in the absence of hunger (eating self-regulation) positively predicted whereas delay of gratification (non-eating self-regulation) negatively predicted changes in girls’ BMI z-scores overtime. Conclusions The results of this study point to the importance of addressing non-eating self-regulation as well as eating self-regulation in future family-focused childhood obesity initiatives, especially those focused on Hispanic families with young children. Funding Sources Research was funded by the National Institutes of Health (R01 HD062567).

The Prediction of Children's Accident Proneness Based on Attention Deficit Hyperactivity Disorder and Sluggish Cognitive Tempo Symptoms: The Mediating Role of Sensitivity to Reward and Punishment

The Prediction of Children's Accident Proneness Based on Attention Deficit Hyperactivity Disorder and Sluggish Cognitive Tempo Symptoms: The Mediating Role of Sensitivity to Reward and Punishment

Does Repeated Measurement of a 6-Min Walk Test Contribute to Risk Prediction in Children with Dilated Cardiomyopathy?

A single 6-min walk test (6MWT) can be used to identify children with dilated cardiomyopathy (DCM) with a high risk of death or heart transplantation. To determine if repeated 6MWT has added value in addition to a single 6MWT in predicting death or heart transplantation in children with DCM. Prospective multicenter cohort study including ambulatory DCM patients ≥ 6 years. A 6MWT was performed 1 to 4 times per year. The distance walked was expressed as percentage of predicted (6MWD%). We compared the temporal evolution of 6MWD% in patients with and without the study endpoint (SE: all-cause death or heart transplantation), using a linear mixed effects model. In 57 patients, we obtained a median of 4 (IQR 2–6) 6MWTs per patient during a median of 3.0 years of observation (IQR 1.5–5.1). Fourteen patients reached a SE (3 deaths, 11 heart transplantations). At any time during follow-up, the average estimate of 6MWD% was significantly lower in patients with a SE compared to patients without a SE. In both patients groups, 6MWD% remained constant over time. An absolute 1% lower 6MWD% was associated with an 11% higher risk (hazard) of the SE (HR 0.90, 95% CI 0.86–0.95 p < 0.001). Children with DCM who died or underwent heart transplantation had systematically reduced 6MWD%. The performance of all patients was stable over time, so repeated measurement of 6MWT within this time frame had little added value over a single test.

Social inclusion and exclusion in same-race and interracial peer encounters.

This study investigated children's and adolescents' predictions of inclusion and evaluations of exclusion in interracial and same-race peer contexts. The sample (N = 246) consisted of African American (n = 115) and European American (n = 131) children and adolescents who judged the likelihood of including a new peer, evaluated the group's decision to exclude the new peer, and provided reasons for their judgments. European American participants, particularly adolescents, viewed same-race inclusion as more likely than interracial inclusion. In contrast, African American participants viewed interracial and same-race inclusion to be just as likely, and evaluated all forms of exclusion to be more wrong than did their European American counterparts. The findings are discussed with respect to peer messages about interracial peer encounters and the conditions that are necessary for prejudice reduction. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Young Children Are Wishful Thinkers: The Development of Wishful Thinking in 3‐ to 10‐Year‐Old Children

Previously, research on wishful thinking has found that desires bias older children's and adults' predictions during probabilistic reasoning tasks. In this article, we explore wishful thinking in children aged 3- to 10-years-old. Do young children learn to be wishful thinkers? Or do they begin with a wishful thinking bias that is gradually overturned during development? Across five experiments, we compare low- and middle-income United States and Peruvian 3- to 10-year-old children (N=682). Children were asked to make predictions during games of chance. Across experiments, preschool-aged children from all backgrounds consistently displayed a strong wishful thinking bias. However, the bias declined with age.

Physical Play with Children Predicts Better Hematological Health; Hematological Health Predicts Cognitive and Behavioral Development

Physical Play with Children Predicts Better Hematological Health; Hematological Health Predicts Cognitive and Behavioral Development

The effect of psychological distance on young children's future predictions

AbstractThe current study examined the impact of psychological distance on children's performance on the pretzel task. In this task, children eat pretzels (inducing thirst) and then are asked to reason about future preferences (pretzels or water). Children typically perform poorly on this task, indicating a future preference for water over pretzels, potentially due to conflicting current and future states. Given past work showing that children's future reasoning is more accurate for another person, we asked 90 thirsty 3‐ to 7‐year‐olds to reason about their own and an experimenter's future preference. Results showed that thirsty children had more difficulty predicting their own future preference compared with the experimenter's. Thirstier children were more likely to predict a future preference for water. Thirst interacted with age when making a future choice for the experimenter. How psychological distance might boost episodic foresight and possible reasons for children's poor pretzel task performance are discussed.Highlights Does psychological distancing improve children's ability to make accurate future predictions when current and future states conflict? Using the Pretzel task, thirsty children were less accurate when predicting their own future preferences compared with the future preferences of another person. Psychological distancing may help children overcome their current state to reason more accurately about the future.

Three- to Four-Year-Old Children Rapidly Adapt Their Predictions and Use Them to Learn Novel Word Meanings.

Adults create and update predictions about what speakers will say next. This study asks whether prediction can drive language acquisition, by testing whether 3- to 4-year-old children (n=45) adapt to recent information when learning novel words. The study used a syntactic context which can precede both nouns and verbs to manipulate children's predictions about what syntactic category will follow. Children for whom the syntactic context predicted verbs were more likely to infer that a novel word appearing in this context referred to an action, than children for whom it predicted nouns. This suggests that children make rapid changes to their predictions, and use this information to learn novel information, supporting the role of prediction in language acquisition.

When preferences are in the way: Children's predictions of goal-directed behaviors.

Across three studies, we examined 4- to 7-year-olds' predictions of goal-directed behaviors when goals conflict with preferences. In Study 1, when presented with stories in which a character had to act against basic preferences to achieve an interpersonal goal (e.g., playing with a partner), 6- and 7-year-olds were more likely than 4- and 5-year-olds to predict the actor would act in accordance with the goal to play with the partner, instead of fulfilling the basic preference of playing a favored activity. Similar results were obtained in Study 2 with scenarios that each involved a single individual pursuing intrapersonal goals that conflicted with his or her basic preferences. In Study 3, younger children's predictions of goal-directed behaviors did not increase for novel goals and preferences, when the influences of their own preferences, future thinking, or a lack of impulse control were minimized. The results suggest that between ages 4 and 7, children increasingly integrate and give more weight to other sources of motivational information (e.g., goals) in addition to preferences when predicting people's behaviors. This increasing awareness may have implications for children's self-regulatory and goal pursuit behaviors. (PsycINFO Database Record

'Things aren't so bad!': Preschoolers overpredict the emotional intensity of negative outcomes.

Adults often overpredict the emotional intensity of future events, but little is known about whether this 'intensity bias' is present in early childhood. We asked 48 3- to 5-year-olds to (1) predict and (2) report their emotions concerning two desirable (receiving four stickers, scoring up to two points in a ball toss) and two undesirable (receiving one sticker, scoring no points) outcomes. Children showed the intensity bias by overpredicting how negatively they would feel if they received one sticker, but not for scoring no points. We discuss how task factors (e.g., personal volition) and cognitive mechanisms (e.g., immune neglect) may influence children's tendency to show the intensity bias. Statement of contribution What is already known on this subject? Adults tend to overpredict the intensity of their emotional reactions to future events. Whether similar 'affective forecasting' errors characterize preschoolers' predictions is not known. What does this study add? We created two forecasting tasks ('sticker' and 'ball') with both desirable and undesirable outcomes. We obtained evidence for a 'negativity' but not a 'positivity' bias in children's predictions. On the sticker task, children overpredicted how badly they would feel after receiving one, versus, four stickers.

Changing predictions, stable recognition: Children's representations of downward incline motion.

Various studies to-date have demonstrated children hold ill-conceived expressed beliefs about the physical world such as that one ball will fall faster than another because it is heavier. At the same time, they also demonstrate accurate recognition of dynamic events. How these representations relate is still unresolved. This study examined 5- to 11-year-olds' (N=130) predictions and recognition of motion down inclines. Predictions were typically in error, matching previous work, but children largely recognized correct events as correct and rejected incorrect ones. The results also demonstrate while predictions change with increasing age, recognition shows signs of stability. The findings provide further support for a hybrid model of object representations and argue in favour of stable core cognition existing alongside developmental changes. Statement of contribution What is already known on this subject? Children's predictions of physical events show limitations in accuracy Their recognition of such events suggests children may use different knowledge sources in their reasoning What the present study adds? Predictions fluctuate more strongly than recognition, suggesting stable core cognition But recognition also shows some fluctuation, arguing for a hybrid model of knowledge representation.

She bought the unicorn from the pet store: Six- to seven-year-olds are strongly inclined to generate natural explanations.

In two experiments (N = 64), we told 6- to 7-year-olds about improbable or impossible outcomes (Experiment 1) and about impossible outcomes concerning ordinary or magical agents (Experiment 2). In both experiments, children claimed that the outcomes were impossible and could not happen, but nonetheless generated realistic and natural explanations for the outcomes. These findings show that 6- to 7-year-olds are strongly inclined to provide natural explanations. The findings are also informative about children's judgments about whether outcomes are possible, and further suggest that asymmetries between children's predictions and explanations may stem from differences in how these 2 forms of reasoning are constrained by possibility. (PsycINFO Database Record

"Proactive" in many ways: Developmental evidence for a dynamic pluralistic approach to prediction.

The anticipation of the forthcoming behaviour of social interaction partners is a useful ability supporting interaction and communication between social partners. Associations and prediction based on the production system (in line with views that listeners use the production system covertly to anticipate what the other person might be likely to say) are two potential factors, which have been proposed to be involved in anticipatory language processing. We examined the influence of both factors on the degree to which listeners predict upcoming linguistic input. Are listeners more likely to predict book as an appropriate continuation of the sentence "The boy reads a", based on the strength of the association between the words read and book (strong association) and read and letter (weak association)? Do more proficient producers predict more? What is the interplay of these two influences on prediction? The results suggest that associations influence language-mediated anticipatory eye gaze in two-year-olds and adults only when two thematically appropriate target objects compete for overt attention but not when these objects are presented separately. Furthermore, children's prediction abilities are strongly related to their language production skills when appropriate target objects are presented separately but not when presented together. Both influences on prediction in language processing thus appear to be context dependent. We conclude that multiple factors simultaneously influence listeners' anticipation of upcoming linguistic input and that only such a dynamic approach to prediction can capture listeners' prowess at predictive language processing.