The Expert Survey-Based Global Ranking of Management- and Clinical-Centered Health Informatics and IT Journals

The goal of this study is to develop an expert survey-based journal ranking for the Health Informatics & Information Technology (HIIT) field. Journal of the American Medical Informatics Association and Journal of Medical Internet Research were ranked as top HIIT management-focused journals, and BMC Medical Informatics & Decision Making and IEEE Journal of Biomedical & Health Informatics were ranked as top HIIT clinical-focused journals. This ranking benefits academics who conduct research in this field because it allows them to direct their research to appropriate journals, convey their accomplishments to tenure and promotion committees, and experience other benefits

Global Ranking of Management- and Clinical-centered E-health Journals

This study presents a ranking list of 35 management- and 28 clinical-centered e-health academic journals developed based on a survey of 398 active researchers from 46 countries. Among the management-centered journals, the researchers ranked Journal of the American Medical Informatics Association and Journal of Medical Internet Research as A+ journals; among the clinical-focused journals, they ranked BMC Medical Informatics and Decision Making and IEEE Journal of Biomedical and Health Informatics as A+ journals. We found that journal longevity (years in print) had an effect on ranking scores such that longer standing journals had an advantage over their more recent counterparts, but this effect was only moderately significant and did not guarantee a favorable ranking position. Various stakeholders may use this list to advance the state of the e-health discipline. There are both similarities and differences between the present ranking and the one developed earlier in 2010

Large AI Models in Health Informatics: Applications, Challenges, and the Future

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics.Comment: This article has been accepted for publication in IEEE Journal of Biomedical and Health Informatic

Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step

Increase in workload across many organisations and consequent increase in occupational stress is negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of self- reporting and variability between and within individuals. With the advent of smartphones it is now possible to monitor diverse aspects of human behaviour, including objectively measured behaviour related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behaviour that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (in comparison to location, video or audio recording, for example) and because its low power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. 30 subjects from two different organizations were provided with smartphones. The study lasted for 8 weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify self reported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.Comment: in IEEE Journal of Biomedical and Health Informatics, 201

Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of the American Medical Informatics Association following peer review. The version of record is available online at: http://dx.doi.org/10.1093/jamia/ocv008[EN] [Objective] This systematic review aims to identify and compare the existing processes and methodologies that have been published in the literature for defining clinical information models (CIMs) that support the semantic interoperability of electronic health record (EHR) systems. [Material and Methods] Following the preferred reporting items for systematic reviews and meta-analyses systematic review methodology, the authors reviewed published papers between 2000 and 2013 that covered that semantic interoperability of EHRs, found by searching the PubMed, IEEE Xplore, and ScienceDirect databases. Additionally, after selection of a final group of articles, an inductive content analysis was done to summarize the steps and methodologies followed in order to build CIMs described in those articles. [Results] Three hundred and seventy-eight articles were screened and thirty six were selected for full review. The articles selected for full review were analyzed to extract relevant information for the analysis and characterized according to the steps the authors had followed for clinical information modeling. [Discussion] Most of the reviewed papers lack a detailed description of the modeling methodologies used to create CIMs. A representative example is the lack of description related to the definition of terminology bindings and the publication of the generated models. However, this systematic review confirms that most clinical information modeling activities follow very similar steps for the definition of CIMs. Having a robust and shared methodology could improve their correctness, reliability, and quality. [Conclusion] Independently of implementation technologies and standards, it is possible to find common patterns in methods for developing CIMs, suggesting the viability of defining a unified good practice methodology to be used by any clinical information modeler.This research has been partially funded by the Instituto de Salud Carlos III (Platform for Innovation in Medical Technologies and Health), grant PT13/0006/0036 and the Spanish Ministry of Economy and Competitiveness, grants TIN2010-21388-C02-01 and PTQ-12-05620.Moreno-Conde, A.; Moner Cano, D.; Da Cruz, WD.; Santos, MR.; Maldonado Segura, JA.; Robles Viejo, M.; Kalra, D. (2015). 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Computer-Aided Diagnosis Software for Hypertensive Risk Determination Through Fundus Image Processing

"(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."The goal of the software proposed in this paper is to assist ophthalmologists in diagnosis and disease prevention, helping them to determine cardiovascular risk or other diseases where the vessels can be altered, as well as to monitor the pathology progression and response to different treatments. The performance of the tool has been evaluated by means of a double-blind study where its sensitivity, specificity, and reproducibility to discriminate between health fundus (without cardiovascular risk) and hypertensive patients has been calculated in contrast to an expert ophthalmologist opinion obtained through a visual inspection of the fundus image. An improvement of almost 20% has been achieved comparing the system results with the clinical visual classification.This work was supported in part by Ministerio de Economia y Competitividad of Spain, Project ACRIMA (TIN2013-46751-R) and partially by the Projects Consolider-C (SEJ2006 14301/PSIC), CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII, and the Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008157).Morales Martínez, S.; Naranjo Ornedo, V.; Navea, A.; Alcañiz Raya, ML. (2014). Computer-Aided Diagnosis Software for Hypertensive Risk Determination Through Fundus Image Processing. IEEE Journal of Biomedical and Health Informatics. 18(6):1757-1763. https://doi.org/10.1109/JBHI.2014.2337960S1757176318