Background Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.
This paper presents a new approach to text processing, based on textemes. These are atomic text units generalising the concepts of character and glyph by merging them in a common data structure, together with an arbitrary number of user-defined properties. In the first part, we give a survey of the notions of character and glyph and their relation with Natural Language Processing models, some visual text representation issues and strategies adopted by file formats (SVG, PDF, DVI) and software (Uniscribe, Pango).In the second part we show applications of textemes in various text processing issues: ligatures, variant glyphs and other OpenType-related properties, hyphenation, color and other presentation attributes, Arabic form and morphology, CJK spacing, metadata, etc. Finally we describe how the Omega typesetting system implements texteme processing as an example of a generalised approach to input character stream parsing, internal representation of text, and modular typographic transformations. In the data flow from input to output, whether in memory or through serializations in auxiliary data files, textemes progressively accumulate information that is used by Omega's paragraph builder engine and included in the output DVI file. We show how this additional information increases efficiency of conversions to other file formats such as PDF or SVG. We conclude this paper by presenting interesting potential applications of texteme methods in document engineering.
Abstract. Dynamic geometry systems (DGS) have become basic tools in many areas of geometry as, for example, in education. Geometry Automated Theorem Provers (GATP) are an active area of research and are considered as being basic tools in future enhanced educational software as well as in a next generation of mechanized mathematics assistants. Recently emerged Web repositories of geometric knowledge, like TGTP and Intergeo, are an attempt to make the already vast data set of geometric knowledge widely available. Considering the large amount of geometric information already available, we face the need of a query mechanism for descriptions of geometric constructions. In this paper we discuss two approaches for describing geometric figures (declarative and procedural), and present algorithms for querying geometric figures in declaratively and procedurally described corpora, by using a DGS or a dedicated controlled natural language for queries.
Abstract. In 1945, the Cairo Academy for the Arabic Language opened a contest to find the best project for simplification of the Arabic writing system. They received about 200 replies. We have chosen three of these projects (a preliminary project by an Academy subcommitee, and projects by Ahmed Lakhdar-Ghazal and Yahya Boutemene) and have implemented them via the if2 typesetting system. In this paper we describe and discuss these projects and their implementations. A text sample is presented both in simplified and in regular form. Showing new aspects of the Arabic script, these systems can be useful for Arabic typesetting, as well as in providing new directions for Arabic type design.
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