LinguAPP: An m-Health Application for Teledentistry Diagnostics

An Android/iOS application for low-cost mobile devices to aid in dental diagnosis through questionnaire and photos is presented in this paper. The main purposes of our app lie in the ease of use even for nonexperienced users, in the limited hardware requirements that allow a wide diffusion, and in the possibility to modify the questionnaire for different pathologies. This tool was developed in about a month at the beginning of the COVID-19 (SARS-CoV-2) pandemic and is still in use in Italy to allow support to patients without going to the hospital, if not strictly necessary.

Monitoring Benzene, Toluene, Ethylbenzene, and Xylene (BTEX) Levels in Mixed-Use Residential-Commercial Buildings in Shiraz, Iran: Assessing the Carcinogenicity and Non-Carcinogenicity Risk of Their Inhabitants

The aim of this study is to investigate the concentration of Benzene, Toluene, Ethylbenzene, and Xylene (BTEX) compounds in the indoor air of residential-commercial complexes and to compare it with other residential buildings (control) as well as to assess the carcinogenicity and non-carcinogenicity risk of these pollutants. BTEX concentration was investigated in the indoor air of 30 ground floor restaurants, 30 upper residential units of the complexes, 20 adjacent residential units (control), and their corridors. The mean BTEX concentration measured in the upper residential units was reported higher than in the control residential units, though they were not significantly different. The lifetime cancer risk (LTCR) value calculated for benzene in the upper residential units was lower than 10−4 and higher than 10−6 across all ages, indicating a carcinogenicity risk. Furthermore, the mean hazard quotient (HQ) for all compounds was obtained lower than 1, suggesting no concern about the non-carcinogenicity risk of these compounds in the studied region. Nevertheless, considering the sources of benzene production in the indoor air as well as the carcinogenicity of these pollutants and the risk they pose in human health, application towards the reduction of the sources and concentration of benzene in the indoor air are necessary.

Is AI augmenting or substituting humans? An eye-tracking study of visual attention toward health application

In this paper, the authors focus on Artificial Intelligence as a tangible technology that is designed to sense, comprehend, act, and learn. There are two manifestations of AI in the medical service: an algorithm that analyzes and interprets the test result and a virtual assistant that communicates the result to the patient. The aim of this paper is to consider how AI can substitute a doctor in measuring human health and how the interaction with virtual assistant impacts one’s visual attention processes. Theoretically, the article refers to the following research strands: Human-Computer Interaction, technology in services, implementation of AI in the medical sector, and behavioral economy. By conducting an eye-tracking experimental study, it is demonstrated that the perception of medical diagnosis does not differ across experimental groups (human vs. AI). However, it is observed that participants exposed to AI-based assistant focused more on button allowing to contact a real doctor.

Proposed Usage of ICT and Big Data in Healthcare of Madhya Pradesh

This paper gives an insight of how information and communications technology (ICT) in combination with big data analytics can help to improve healthcare services in Madhya Pradesh, which is a state in India having approximately 75 million populations. With ongoing projects like ‘Digital India’ which will allow computerization of hospitals and digitization of healthcare data. Digital India coupled with ICT, can play an indispensable role in providing effective healthcare services through e-health application like electronic health record, e-prescription, computerized physician order entry, telemedicine, mhealth along with the network like State wide area network (SWAN) and National health information network which will allow sharing of healthcare records across the network. Data stored through e-health application is of huge size having different formats which makes it difficult to perform analytics on it. But with big data analytics we can perform analytics on large voluminous healthcare data and useful result obtained from data analytics, patients can be given better and specific treatments. It will also help doctors to exchange their knowledge and treatment practices. This paper also illustrates a case study on M.Y. hospital located in Indore, Madhya Pradesh. Keywords: ICT, E-health, Digital India, SWAN, CUG, Big Data Analytics.

Remote Health Monitoring in the Workplace for Early Detection of COVID-19 Cases during the COVID-19 Pandemic Using a Mobile Health Application: COVIDApp.

Background: COVIDApp is a platform created for management of COVID-19 in the workplace. Methods: COVIDApp was designed and implemented for the follow-up of 253 workers from seven companies in Catalonia. The assessment was based on two actions: first, the early detection and management of close contacts and potential cases of COVID-19, and second, the rapid remote activation of protocols. The main objectives of this strategy were to minimize the risk of transmission of COVID-19 infection in the work area through a new real-time communication channel and to avoid unnecessary sick leave. The parameters reported daily by workers were close contact with COVID cases and signs and/or symptoms of COVID-19. Results: Data were recorded between 1 May and 30 November 2020. A total of 765 alerts were activated by 76 workers: 127 green alarms (16.6%), 301 orange alarms (39.3%), and 337 red alarms (44.1%). Of all the red alarms activated, 274 (81.3%) were activated for symptoms potentially associated with COVID-19, and 63 (18.7%) for reporting close contact with COVID-19 cases. Only eight workers (3.1%) presented symptoms associated with COVID-19 infection. All of these workers underwent RT-PCR tests, which yielded negative results for SARS-CoV2. Three workers were considered to have had a risk contact with COVID-19 cases; only 1 (0.4%) asymptomatic worker had a positive RT-PCR test result, requiring the activation of protocols, isolation, and contact tracing. Conclusions: COVIDApp contributes to the early detection and rapid activation of protocols in the workplace, thus limiting the risk of spreading the virus and reducing the economic impact caused by COVID-19 in the productive sector. The platform shows the progression of infection in real time and can help design new strategies.

Digital Health Applications in the Context of Dementia: A Questionnaire Study to develop an Assessment Tool about the Likelihood of Use among Physicians (Preprint)

BACKGROUND Age-related diseases such as dementia are playing an increasingly important role with regard to global population development. Thus, prevention, diagnostics and interventions require more accessibility, which can be realized through digital health applications. With the "app on prescription" Germany made history by being the first country worldwide to offer physicians the possibility to prescribe and reimburse digital health applications starting by the end of the year 2020. OBJECTIVE Considering the lack of knowledge about correlations with the likelihood of use among physicians, the aim of this study is to address the question of what makes the use of an digital health application by physicians more likely. METHODS We developed and validated a novel measurement tool - the Digital-Health-Compliance-Questionnaire (DHCQ) - to assess the role of four proposed factors on the likelihood of using a health application. Therefore, a survey was conducted online that evaluated the likelihood of using a digital application for screening of Alzheimers’ dementia called DemPredict. Within this survey, five latent dimensions (acceptance, attitude towards technology, technology experience, payment for time of use and effort of collection), the dependent variable "likelihood of use" and answers to exploratory questions were recorded and tested within directed correlations. The study was completed by 331 physicians from Germany, of whom a total of 301 physicians fulfilled the study criteria (e.g., being in regular contact to dementia patients). This data was analysed using a range of statistical methods to validate the DHCQs’ dimensions. RESULTS The DHCQ revealed good test theoretical measures: it showed excellent fit indices (TLI = .98, CFI = .982, SRMR = .073, RMSEA = .037), good internal consistency (Cronbachs-alpha = .83) and showed signs of moderate to large correlations between the DHCQ-dimensions and the dependent variable. The correlations between {“acceptance”|“attitude towards technology”| “technology experience”|“payment for time of use”} and "likelihood of use" ranged from r = 0.29 to r = 0.79 as well as between “effort of collection” and "likelihood of use" at r = -0.80. In addition, we found high levels of skepticism regarding data protection and the age of the subjects was found to be negatively related to their technical experience as well as their attitude towards technology. CONCLUSIONS In the context of the results, increased communication between the medical and technology sectors and significantly more awareness rising are recommended in order to make the use of digital health applications more attractive for physicians because it can be adjusted to their everyday needs. Further research could explore the connection between areas such as adherence on patient side and its impact on the likelihood of use by the physician.

Sentimental Tweets Classification of Symptomatic COVID 19

The approach I described is straightforward, related to COVID-19 SARS based tweets and the symptoms, that people tweet about. Also, social media mining for health application reports was shared in many different tasks of 2021. The motto at the back of this observe is to analyses tweets of COVID-19 based symptoms. By performing BERT model and text classification with XLNET with which uses to classify text and purpose of the texts (i.e.) tweets. So that I can get a deep understanding of the texts. When developing the system, I used two models the XLNet and DistilBERT for the text sorting task, but the outcome was XLNET out-performs the given approach to the best accuracy achieved. Now I discover a whole lot vital for as it should be categorizing tweets as encompassing self-said COVID-19 indications. Whether or not a tweets associated with COVID-19 is a non-public report or an information point out to the virus. Which gives test accuracy to an F1 score of 96%.

Effect of Need, Easy, and Trust on the Intensity of Using the Mobile Health Application to Conduct a Laboratoty Examination

This study aims to analyze the influence of need, convenience and trust on the intensity of using the mobile health application to perform laboratory examinations on Pramita Lab Surabaya patients. This research is important because the intensity of the use of the mobile health application is influenced by the needs, convenience and trust of patients in the mobile health application. The sample in this study were all 108patients using the mobile health application at Pramita Lab Surabaya. The research design used is quantitative research with a descriptive approach with an emphasis on theory testing through measurement of research variables through distributing research questionnaires. The distribution of research questionnaires was carried out using probability sampling techniques. The analysis technique in this study is to use multiple linear regression analysis with the help of SPSS 20.0 software. The results showed that the variables of need, convenience and trust had a positive and significant effect simultaneously on the intensity of using the mobile health application in patients at Pramita Lab Surabaya. The result also show that there is a positive and significant influence on the need, convenience and trust on the intensity of using the mobile health application by patients at Pramita Lab Surabaya.

ACCU3RATE: A mobile health application rating scale based on user reviews

Background Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.