A new classification system for autism based on machine learning of artificial intelligence

2021 ◽  
pp. 1-18
Author(s):  
Seyed Reza Shahamiri ◽  
Fadi Thabtah ◽  
Neda Abdelhamid
Keyword(s):  
Machine Learning ◽  
Scoring Function ◽  
Autistic Traits ◽  
Well Being ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
The Social ◽  
Hidden Patterns ◽  
The Individual ◽  
Fold Cross Validation

BACKGROUND: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs’ performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

2020 ◽  
Vol 25 (40) ◽  
pp. 4296-4302 ◽  
Author(s):  
Yuan Zhang ◽  
Zhenyan Han ◽  
Qian Gao ◽  
Xiaoyi Bai ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inclusion Bodies ◽  
Cross Validation ◽  
Independent Set ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Validation Test ◽  
Excess Number ◽  
Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension

Hypertension ◽  
2021 ◽  
Vol 78 (5) ◽  
pp. 1595-1604
Author(s):  
Fabrizio Buffolo ◽  
Jacopo Burrello ◽  
Alessio Burrello ◽  
Daniel Heinrich ◽  
Christian Adolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arterial Hypertension ◽  
Primary Aldosteronism ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Clinical Score ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Individual Risk ◽  
The Individual

Primary aldosteronism (PA) is the cause of arterial hypertension in 4% to 6% of patients, and 30% of patients with PA are affected by unilateral and surgically curable forms. Current guidelines recommend screening for PA ≈50% of patients with hypertension on the basis of individual factors, while some experts suggest screening all patients with hypertension. To define the risk of PA and tailor the diagnostic workup to the individual risk of each patient, we developed a conventional scoring system and supervised machine learning algorithms using a retrospective cohort of 4059 patients with hypertension. On the basis of 6 widely available parameters, we developed a numerical score and 308 machine learning-based models, selecting the one with the highest diagnostic performance. After validation, we obtained high predictive performance with our score (optimized sensitivity of 90.7% for PA and 92.3% for unilateral PA [UPA]). The machine learning-based model provided the highest performance, with an area under the curve of 0.834 for PA and 0.905 for diagnosis of UPA, with optimized sensitivity of 96.6% for PA, and 100.0% for UPA, at validation. The application of the predicting tools allowed the identification of a subgroup of patients with very low risk of PA (0.6% for both models) and null probability of having UPA. In conclusion, this score and the machine learning algorithm can accurately predict the individual pretest probability of PA in patients with hypertension and circumvent screening in up to 32.7% of patients using a machine learning-based model, without omitting patients with surgically curable UPA.

scholarly journals A Study of the Understated Violence Within Social Contexts Against Adolescent Girls

2017 ◽  
Vol 8 (2) ◽  
pp. 29-41
Author(s):  
Shivangi Nigam ◽  
Niranjana Soperna
Keyword(s):  
Adolescent Girls ◽  
Rural Areas ◽  
Urban Areas ◽  
Social Issues ◽  
Well Being ◽  
Structured Interview ◽  
Social Contexts ◽  
The Social ◽  
The Difference ◽  
The Individual

Violence against women is linked to their disadvantaged position in the society. It is rooted in unequal power relationships between men and women in society and is a global problem which is not limited to a specific group of women in society. An adolescent girl’s life is often accustomed to the likelihood of violence, and acts of violence exert additional power over girls because the stigma of violence often attaches more to a girl than to the  perpetrator. The experience of violence is distressing at the individual emotional and physical level. The field of research and programmes for adolescent girls has traditionally focused on sexuality, reproductive health, and behaviour, neglecting the broader social issues that underpin adolescent girls’ human rights, overall development, health, and well-being. This paper is an endeavour to address the understated or disguised form of violence which the adolescent girls experience within the social contexts. The parameters exposed under this research had been ignored to a large extent when it comes to studying the dimension of violence under the social domain. Hence, the researchers attempted to explore this camouflaged form of violence and discovered some specific parameters such as: Diminished Self Worth and Esteem, Verbal Abuse, Menstruation Taboo and Social Rigidity, Negligence of Medical and Health Facilities and Complexion- A Prime Parameter for Judging Beauty. The study was conducted in the districts of Haryana (India) where personal interviews were taken from both urban and rural adolescent girls (aged 13 to 19 years) based on  a structured interview schedule. The results revealed that the adolescent girls, both in urban as well as rural areas were quite affected with the above mentioned issues. In urban areas, however, due to the higher literacy rate, which resulted in more rational thinking, the magnitude was comparatively smaller, but the difference was still negligible.  

scholarly journals Online machine learning algorithms to optimize performances of complex wireless communication systems

2021 ◽  
Vol 19 (2) ◽  
pp. 2056-2094
Author(s):  
Koji Oshima ◽  
◽  
Daisuke Yamamoto ◽  
Atsuhiro Yumoto ◽  
Song-Ju Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wireless Communication ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Optimal Decision ◽  
Wireless Communication Systems ◽  
Optimal Decision Making ◽  
Comprehensive Framework

<abstract><p>Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.</p></abstract>

Sources of Stability and Instability in Rural Thai Society

1968 ◽  
Vol 27 (4) ◽  
pp. 777-790 ◽  
Author(s):  
Steven Piker
Keyword(s):  
Well Being ◽  
Complete Reduction ◽  
External Pressures ◽  
Stability And Instability ◽  
The Social ◽  
The Face ◽  
Social Arrangements ◽  
The Individual ◽  
High Degree ◽  
Social Level

Ongoing cultures, by virtue of the personalities they produce and the social arrangements they embody, create tensions or strains for their individual members; and they provide as well for the institutionalized expression and alleviation, if not complete reduction, of these tensions in culturally approved channels. In this view, cultural stability refers not to the absence of persisting conflict on the individual or social level; but rather to a high degree of complementarity between institutionalized sources of strain or conflict for the individual, and institutionalized arrangements for tension reduction or expression. This conception of stability does not assume that all relatively stable cultures are equally productive of psychological well-being, even assuming this nebulous condition could be specified. Nor does it assert that all stable cultures are equally adaptive in the face of external pressures. It does imply, however, that sources of conflict and channels for its expression will be sufficiently balanced to insure perpetuation of culturally standardized social arrangements and beliefs over many generations.

Exploring social entrepreneurial boundary spanning for compassion-triggered opportunities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eliada Wosu Griffin-EL
Keyword(s):  
Emerging Market ◽  
Research Question ◽  
Well Being ◽  
Boundary Spanning ◽  
Comparative Case Study ◽  
Social Entrepreneurs ◽  
Content Type ◽  
The Social ◽  
Entrepreneurial Action ◽  
The Individual

Purpose The purpose of this paper is to address the research question: How does the social entrepreneur’s compassion inform how they engage with their environment to mobilize resources for social entrepreneurial action? Design/methodology/approach The study features a comparative case study analysis of seven high-profile social entrepreneurs within Cape Town, South Africa. Data via in-depth interviews, site visits and archival information and follow-up conversations were collected and then analyzed via thematic coding of qualitative analysis. Findings The findings suggest that compassion is an antecedent for the social entrepreneurial boundary spanning shaped by their orientation toward concern for others’ well-being. Propositions presented offer the groundwork for an emergent theoretical framework of social entrepreneurial boundary spanning. Originality/value The study builds upon the emerging compassion research within social entrepreneurship, extending the conceptualization of compassion to be shapers of the social structure – not just the individual or the organization – in an emerging market context.

Measurement of Users’ Well-Being Through Domotic Sensors and Machine Learning Algorithms

2020 ◽  
Vol 20 (14) ◽  
pp. 8029-8038 ◽  
Author(s):  
Sara Casaccia ◽  
Luca Romeo ◽  
Andrea Calvaresi ◽  
Nicole Morresi ◽  
Andrea Monteriu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Well Being ◽  
Machine Learning Algorithms

scholarly journals Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

2019 ◽  
Vol 10 (1) ◽  
pp. 46 ◽  
Author(s):  
Johannes Stübinger ◽  
Benedikt Mangold ◽  
Julian Knoll
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Home Team ◽  
Ensemble Strategy ◽  
The World ◽  
European Football ◽  
The Individual ◽  
Football Betting ◽  
Betting Odds

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.

Education Ethnography of Sensitive Issues

2019 ◽  
Author(s):  
Martin Bittner
Keyword(s):  
Sexual Violence ◽  
Possible Worlds ◽  
Practical Knowledge ◽  
Well Being ◽  
Physical Punishment ◽  
Pedagogical Practices ◽  
Ethnographic Approach ◽  
The Social ◽  
The Individual ◽  
Sensitive Issues

Ethnography and sensitive issues come together by way of the question, “What can someone know?,” which is a situational dilemma. An ethnography of sensitive issues creates a particular perspective of knowing. It distresses the overall social assumption that persons, practices, actions, structures, and institutions are based on their re-negotiation of stabilization and their safety of different forms of knowing. The ethnography of sensitive issues addresses the fluidity and fragility of the social and observes the vulnerability of persons, practices, fields, and settings. Sensitive issues of the social situate beyond the sociological and historical divide of (intimate) privacy and the public sphere. Sensitive issues touch on the violation of intimacy within public and private institutions by neglect, punishment, maltreatment, violence, bullying, and sexual violence. The problematizing perspectives on such disruptive social practices are particularly relevant for pedagogy and education. An education ethnography of sensitive issues thus asks for the risk of violation within pedagogical arrangements and describes the how and what of the vulnerability of the child and the indicated transgression of or within education practices. However, education settings—children engaging in institutions like the family, the school, and social care services—are constructed through the (unconscious) boundless aim of well-being, pedagogy for good, and positivity by education in its normativity. How do children learn to believe that what others say or do is for their good? How do educational arrangements cover vulnerable situations? Where are the borders or limitations within practices of education in pedagogical institutions? An education ethnography of sensitive issues problematizes the implicit, tacit, and practical knowledge of pedagogical arrangements and questions how those involved perform violence and, within the practices, at what stages of vulnerability. Questioning violence and vulnerability points out that children sadly are not always recognized as equals and are equated by the other (child or adult). Sensitive issues in education and care situations define a greater net of responsibilities and its totality of practices of the powerful. Thus, it seems socially and educationally mandatory to gain descriptions and theories about the circumstances of sensitive issues in the examples of neglect of the individual in his or her rights and psychological and emotional situatedness, as well as physical punishment and sexual violence against children. Focusing on violations and problematizing educational practices through research has ethical and moral restrictions that seem to contradict an ethnographic approach. It is (normatively) impossible for the ethnographer to participate in situ in situations of sensitive issues of violence and maltreatment against children. Additionally, seeing ethnography as a methodological and theoretical approach, an ethnography of sensitive issues could not be restricted to those who (autoethnographically) experience violations and maltreatment by themselves. Instead of arguing for a constrained ethnography of sensitive issues, the particular perspective on sensitive issues highlights the ethnographic approach. This goes along with understanding borders and transgressions as well as the taboos in the field and the challenging task of positioning oneself as an observer to be trusted in the uncertainty, unsafety, and instability of the nearest possible worlds. Hence, an education ethnography of sensitive issues considers researching intimacy at its boarders, limits, heterotopia, and transgressions of pedagogical practices within educational institutions and care situations.

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