Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics.

The impact of micro-level people's activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants.

Neuro-semantic prediction of user decisions to contribute content to online social networks.

Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e.

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile.

In this paper, we propose and validate with data extracted from the city of Santiago, capital of Chile, a methodology to assess the actual impact of lockdown measures based on the anonymized and geolocated data from credit card transactions. Using unsupervised Latent Dirichlet Allocation (LDA) semantic topic discovery, we identify temporal patterns in the use of credit cards that allow us to quantitatively assess the changes in the behavior of the people under the lockdown measures because of the COVID-19 pandemic. An unsupervised latent topic analysis uncovers the main patterns of credit card transaction activity that explain the behavior of the inhabitants of Santiago City.

Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile.

Background: Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, structure, and analyze in the shortest time possible the knowledge encoded in the large volume of medical literature. In this paper, we use the Latent Dirichlet Allocation approach to analyze the correlation between funding efforts and actually published research results in order to provide the policy makers with a systematic and rigorous tool to assess the efficiency of funding programs in the medical area.

The development and validation of a marginalization index for inpatient psychiatry.

Background: Marginalization is a multidimensional social construct that influences the mental health status of individuals and their use of psychiatric services. However, its conceptualization and measurement are challenging due to inconsistencies in definitions, and the lack of standard data sources to measure this construct.

Aims: To create an index for screening marginalization based on an existing comprehensive assessment system used in inpatient psychiatry.

Computational Intelligence in Remote Sensing: An Editorial.

Computational intelligence is a very active and fruitful research of artificial intelligence with a broad spectrum of applications. Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/development of new data analysis tools. In this editorial paper we provide the setting of the special issue "Computational Intelligence in Remote Sensing" and an overview of the published papers.

Machine Learning Readmission Risk Modeling: A Pediatric Case Study.

Background: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.

Objective: To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.

Evaluation of an eye-pointer interaction device for human-computer interaction.

Advances in eye-tracking technology have led to better human-computer interaction, and involve controlling a computer without any kind of physical contact. This research describes the transformation of a commercial eye-tracker for use as an alternative peripheral device in human-computer interactions, implementing a pointer that only needs the eye movements of a user facing a computer screen, thus replacing the need to control the software by hand movements. The experiment was performed with 30 test individuals who used the prototype with a set of educational videogames.

Correction to: Antipsychotics and dementia in Canada: a retrospective cross-sectional study of four health sectors.

Following the publication of this article [1], the authors noticed that the results presented in the results section of the article were erroneously reported in the results section of the abstract.

Geographic Clustering of Admissions to Inpatient Psychiatry among Adults with Cognitive Disorders in Ontario, Canada: Does Distance to Hospital Matter?

Objective: This study examined relationships among hospital accessibility, socio-economic context, and geographic clustering of inpatient psychiatry admissions for adults with cognitive disorders in Ontario, Canada.

Method: A retrospective cross-sectional analysis was conducted using admissions data from 71 hospitals with inpatient psychiatry beds in Ontario, Canada between 2011 and 2014. Data included 7,637 unique admissions for 4,550 adults with a DSM-IV diagnosis of Delirium, Dementia, Amnestic and other Cognitive Disorders.

Antipsychotics and dementia in Canada: a retrospective cross-sectional study of four health sectors.

Background: Antipsychotic medications are not recommended for the management of symptoms of dementia, particularly among persons with no behavioral or psychological symptoms. We examine patterns of antipsychotic medication use among persons with dementia across health sectors in Canada, with a focus on factors related to use among those without behavioral or psychotic symptoms.

Methods: Using a retrospective cross-sectional design, this study examines antipsychotic use among adults aged 65 or older with dementia in home care (HC), complex continuing care (CCC), long-term care (LTC), and among alternate level care patients in acute hospitals (ALC).

Social Withdrawal Among Individuals Receiving Psychiatric Care: Derivation of a Scale Using Routine Clinical Assessment Data to Support Screening and Outcome Measurement.

Social withdrawal is a symptom experienced by individuals with an array of mental health conditions, particularly those with schizophrenia and mood disorders. Assessments of social withdrawal are often lengthy and may not be routinely integrated within the comprehensive clinical assessment of the individual. This study utilized item response and classical test theory methods to derive a Social Withdrawal Scale (SWS) using items embedded within a routine clinical assessment, the RAI-Mental Health (RAI-MH).

Corrigendum to "Content Patterns in Topic-Based Overlapping Communities".

[This corrects the article DOI: 10.1155/2014/105428.].

Innovations in healthcare and medicine editorial.

This special issue editorial begins with a brief discussion on the current trends of innovations in healthcare and medicine driven by the evolution of sensing devices as well as the information processing techniques, and the social media revolution. This discussion aims to set the stage for the actual papers accepted for the special issue which are extensions of the papers presented at the InMed 2014 conference held in San Sebastian, Spain, in July 2014.

Content patterns in topic-based overlapping communities.

Understanding the underlying community structure is an important challenge in social network analysis. Most state-of-the-art algorithms only consider structural properties to detect disjoint subcommunities and do not include the fact that people can belong to more than one community and also ignore the information contained in posts that users have made. To tackle this problem, we developed a novel methodology to detect overlapping subcommunities in online social networks and a method to analyze the content patterns for each subcommunities using topic models.