A5101965354- Digital Mental Health Interventions
- Machine Learning in Healthcare
- Mobile Health and mHealth Applications
- Biomedical Text Mining and Ontologies
- Medical Coding and Health Information
- Artificial Intelligence in Healthcare
- Multiple Sclerosis Research Studies
- Telemedicine and Telehealth Implementation
- Mental Health Research Topics
- Nerve injury and regeneration
- Pharmacological Effects and Toxicity Studies
- Organ and Tissue Transplantation Research
- Education and Vocational Training
- Epilepsy research and treatment
- EEG and Brain-Computer Interfaces
- Competency Development and Evaluation
- Health disparities and outcomes
- Nerve Injury and Rehabilitation
- Data Quality and Management
- COVID-19 and Mental Health
- Educational Practices and Policies
- Electronic Health Records Systems
- Technology Adoption and User Behaviour
- Mental Health Treatment and Access
- Impact of Technology on Adolescents
Siemens (Portugal)
2013-2021
University of Lisbon
2016-2020
Siemens (Germany)
2012
Hospitais da Universidade de Coimbra
2000
Mobile technology has the potential to provide accurate, impactful data on symptoms of depression, which could improve health management or assist in early detection relapse. However, for this be achieved, it is essential that patients engage with technology. Although many barriers and facilitators use are common across therapeutic areas types, may specific cultural contexts.This study aimed determine engagement mobile (mHealth) remote measurement depression three Western European...
Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these persist. Many programs that RMT rely on commercially available, "off-the-shelf" devices to collect patient data. However, validation is sparse, technology landscape constantly changing, relative benefits between device options are often unclear, research provider preferences lacking.
This study proposes a methodology to support coding professionals in assigning ICD-9-CM codes inpatient episodes. subject has been predominantly addressed through the use of natural language processing methods, which show limited generalizability. To surpass this issue, paper entailing an adaptive data method based on structured electronic health record data, whereby raw clinical is mapped into feature set, and supervised learning algorithms are trained. After applying filter for selection,...
Summary Background EHR systems have high potential to improve healthcare delivery and management. Although structured data generates information in machine-readable formats, their use for decision support still poses technical challenges researchers due the need preprocess convert into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance on how build this while avoiding pitfalls. Objectives This article aims roadmap of main...
Remote measurement technology (RMT), including the use of mobile phone apps and wearable devices, may provide opportunity for real-world assessment intervention that will streamline clinical input years to come. In order establish benefits this approach, we need operationalize what is expected in terms a successful measurement. We focused on three long-term conditions where novel case has been made RMT: major depressive disorder (MDD), multiple sclerosis (MS), epilepsy.The aim study was...
Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support research. Nevertheless, such structured have not been explored clinical coding, which is an essential process requiring significant manual workload organisations. This article explores the extent to fully assignment of codes inpatient episodes, through a methodology that tackles high dimensionality issues, addresses multi-label nature coding optimises model parameters. The...
Clinical coding is an increasingly essential process within health organizations, usually performed manually and entailing several challenges: its administrative burden, raising costs eventual errors. To address this issue, support systems have been proposed across the literature. However, these are based on text processing methods that may be limited by poor quality, ambiguity lack of annotated resources. As electronic record tend to implement more structured data formats, we propose a...
<sec> <title>BACKGROUND</title> Remote measurement technology refers to the use of mobile health track and measure change in status real time as part a person’s everyday life. With accurate measurement, remote offers opportunity augment care by providing personalized, precise, preemptive interventions that support insight into patterns health-related behavior self-management. However, for successful implementation, users need be engaged its use. </sec> <title>OBJECTIVE</title> Our objective...
<sec> <title>BACKGROUND</title> Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these persist. Many programs that RMT rely on commercially available, “off-the-shelf” devices to collect patient data. However, validation is sparse, technology landscape constantly changing, relative benefits between device options are often unclear, research provider preferences...
<sec> <title>BACKGROUND</title> Mobile technology has the potential to provide accurate, impactful data on symptoms of depression, which could improve health management or assist in early detection relapse. However, for this be achieved, it is essential that patients engage with technology. Although many barriers and facilitators use are common across therapeutic areas types, may specific cultural contexts. </sec> <title>OBJECTIVE</title> This study aimed determine engagement mobile...
<sec> <title>BACKGROUND</title> Remote measurement technology (RMT), including the use of mobile phone apps and wearable devices, may provide opportunity for real-world assessment intervention that will streamline clinical input years to come. In order establish benefits this approach, we need operationalize what is expected in terms a successful measurement. We focused on three long-term conditions where novel case has been made RMT: major depressive disorder (MDD), multiple sclerosis (MS),...