RESUMO
This annotation briefly reviews the history of artificial intelligence and machine learning in health care and orthopaedics, and considers the role it will have in the future, particularly with reference to statistical analyses involving large datasets. Cite this article: Bone Joint J 2019;101-B:1476-1478.
Assuntos
Inteligência Artificial/história , Regras de Decisão Clínica , Procedimentos Ortopédicos/história , Inteligência Artificial/tendências , Interpretação Estatística de Dados , Previsões , História do Século XX , Humanos , Aprendizado de Máquina/história , Aprendizado de Máquina/tendências , Procedimentos Ortopédicos/tendências , Prognóstico , Reino Unido , Estados UnidosRESUMO
Temperament and Psychological Types can be defined as innate psychological characteristics associated with how we relate with the world, and often influence our study and career choices. Furthermore, understanding these features help us manage conflicts, develop leadership, improve teaching and many other skills. Assigning temperament and psychological types is usually made by filling specific questionnaires. However, it is possible to identify temperamental characteristics from a linguistic and behavioral analysis of social media data from a user. Thus, machine-learning algorithms can be used to learn from a user's social media data and infer his/her behavioral type. This paper initially provides a brief historical review of theories on temperament and then brings a survey of research aimed at predicting temperament and psychological types from social media data. It follows with the proposal of a framework to predict temperament and psychological types from a linguistic and behavioral analysis of Twitter data. The proposed framework infers temperament types following the David Keirsey's model, and psychological types based on the MBTI model. Various data modelling and classifiers are used. The results showed that Random Forests with the LIWC technique can predict with 96.46% of accuracy the Artisan temperament, 92.19% the Guardian temperament, 78.68% the Idealist, and 83.82% the Rational temperament. The MBTI results also showed that Random Forests achieved a better performance with an accuracy of 82.05% for the E/I pair, 88.38% for the S/N pair, 80.57% for the T/F pair, and 78.26% for the J/P pair.
Assuntos
Pesquisa Comportamental/métodos , Psicolinguística/métodos , Comportamento Social , Mídias Sociais , Temperamento , Pesquisa Comportamental/história , Feminino , História do Século XXI , Humanos , Aprendizado de Máquina/história , Masculino , Modelos Psicológicos , Psicolinguística/históriaRESUMO
BACKGROUND: Glioblastoma (GB, formally glioblastoma multiforme) is a malignant type of brain cancer that currently has no cure and is characterized by being highly heterogeneous with high rates of re-incidence and therapy resistance. Thus, it is urgent to characterize the mechanisms of GB pathogenesis to help researchers identify novel therapeutic targets to cure this devastating disease. Recently, a promising approach to identifying novel therapeutic targets is the integration of tumor omics data with clinical information using machine learning (ML) techniques. RECENT FINDINGS: ML has become a valuable addition to the researcher's toolbox, thanks to its flexibility, multidimensional approach, and a growing community of users. The goal of this review is to introduce basic concepts and applications of ML for studying GB to clinicians and practitioners who are new to data science. ML applications include exploring large data sets, finding new relevant patterns, predicting outcomes, or merely understanding associations of the complex molecular networks presented within the tumor. Here, we review ML applications published between 2008 and 2018 and discuss ML strategies intending to identify new potential therapeutic targets to improve the management and treatment of GB. CONCLUSIONS: ML applications to study GB vary in purpose and complexity, with positive results. In GB studies, ML is often used to analyze high-dimensional datasets with prediction or classification as a primary goal. Despite the strengths of ML techniques, they are not fail-safe and methodological issues can occur in GB studies that use them. This is why researchers need to be aware of these issues when planning and appraising studies that apply ML to the study of GB.
Assuntos
Neoplasias Encefálicas/genética , Heterogeneidade Genética , Genômica/métodos , Glioblastoma/genética , Aprendizado de Máquina/tendências , Neoplasias Encefálicas/patologia , Conjuntos de Dados como Assunto , Genômica/história , Genômica/tendências , Glioblastoma/patologia , História do Século XXI , Humanos , Aprendizado de Máquina/históriaRESUMO
This is the obituary for Toshio Fujita, pioneer of the quantitative structure activity relationship (QSAR) paradigm.
