Classification of Huntington’s disease stage with support vector machines: A study on oculomotor performance

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

Performance, Achievement, and Success in Psychological Research: Towards a More Transparent Use of the Still Ambiguous Terminology

The present study focused on the terms performance, achievement, and success that have often been used in an ambiguous manner in psychological research. The way in which the meaning of the domain-specific constructs referred to with the terms was established for measurement purposes was investigated on a sample of 262 articles that stemmed from 35 randomly selected journals covering the full range of psychological research. The operational definitions of the constructs referred to with the terms performance, achievement, and success were analysed and compared in both inter- and intra-domain fashion. Additionally, we assessed the match among the observed operational definitions and general conceptual definitions available in the extant literature. The results revealed terminology-related issues in educational and occupational research. Within these domains, lack of adherence to the multidimensionality of the constructs of academic performance, academic achievement, academic success, job performance, and career success was identified as a general issue. Further, the tendency to measure job performance via indicators based on self-rating was considered inadequate given the objective nature of the term performance. In educational research, the overlap of the academic performance, academic achievement, and academic success constructs was confirmed, resulting from the tendency to use GPA as a universal indicator of academic outcomes. Based on the present findings, we provided several recommendations in order to encourage future research towards a more transparent way of dealing with the particular constructs referred to with the terms performance, achievement, and success. We suppose the present study may help researchers in the full range of psychological disciplines to add clarity to their own research.

The effect of impulsivity and inhibitory control deficits in the saccadic behavior of premanifest Huntington's disease individuals

BACKGROUND

This study aims to test response inhibition in premanifest Huntington's disease individuals (Pre-HD), in the context of a saccadic paradigm with working memory demands and fronto-executive load as a way to measure inhibitory control deficits and impulsive behavior in Huntington's disease (HD).

METHODS

The oculomotor function of 15 Pre-HD and 22 Control individuals was assessed using an experimental paradigm comprising four horizontal saccadic tasks: prosaccade (PS), antisaccade (AS), 1- or 2-back memory prosaccade (MPS), and 1- or 2-back memory antisaccade (MAS). Success rate, latency, directional and timing errors were calculated for each task. A comprehensive battery of neuropsychological tests was also used to assess the overall cognitive functioning of study participants. Statistical correlations between oculomotor, clinical and cognitive measures were computed for the Pre-HD group.

RESULTS

Pre-HD participants showed reduced success rate in the AS task, increased direction errors in the AS and MAS tasks and decreased latency in the MAS task when compared to Controls, despite presenting similar executive and memory scores in the conventional neuropsychological tests applied. Significant associations were identified between specific AS and MAS parameters and disease-related measures, cognitive skills and other oculomotor results of Pre-HD participants.

CONCLUSIONS

Our results show that oculomotor performance in premanifest Huntington's disease deteriorates once inhibitory control, working memory and/or fronto-executive load are added to the task. A more automatic pattern of performance, including a faster response time and directionally erroneous eye movements were detected in the oculomotor behavior of the Pre-HD group-these alterations were significantly correlated with disease stage and cognitive status. Our saccadic paradigm was able to capture impulsivity and inhibitory control deficits in a group of Pre-HD individuals on average far from symptom onset, thus holding the potential to identify the earliest disease-related changes.

A systematic literature review and classification of knowledge discovery in traditional medicine

INTRODUCTION AND OBJECTIVE

Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine.

METHOD

We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine.

RESULT

The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods.

CONCLUSION

Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles.