"This site requires JavaScript to work correctly"
Header minimieren

Helana Lutfi, M.Sc.

Fakultät European Campus Rottal-Inn

Wissenschaftliche Mitarbeitende

Wissenschaftliche Mitarbeiterin

EC.B 0.16

0991/3615-8816

Sortierung:
Vortrag
  • Helana Lutfi
  • Shaban Nuredini

Can "Digital Therapeutics" Be as Good as Drugs?. Posterpräsentation.

  • Technische Hochschule Deggendorf.

Deggendorf 10.04.2019.

(2019)

Zeitschriftenartikel
  • Helana Lutfi
  • S. Glasauer
  • Thomas Spittler

The Healthcare Benefits and Impact of Artificial Intelligence Applications on Behaviour of Healthcare Users: A Structured Review of Primary Literature.

In: Journal of the International Society for Telemedicine and eHealth (vol. 8) , pg. 1-5

(2020)

DOI: 10.29086/JISfTeH.8.e10

Abstract anzeigen

Introduction: Artificial intelligence (AI) is one of the most considered topics of the current time. AI has the power to bring revolutionary improvements to the world of technology not only in the field of computer science but also in other fields like medical sciences. Objectives: This paper assumes the adoption of appropriate AI engineering principals in previous studies, and focusses on providing a structured review of the impact of AI on human society and the individual human being as a technology user. Additionally, it opens a window on how the future will look like in terms of AI and personalised medicine. Methods: The paper employed a qualitative research approach and data were collected through a structured literature review. Twenty-three peer reviewed papers were identified and analysed in relation to their relevance to the study. Results: Previous studies show a positive impact on users' behaviour is expected in supporting their healthcare needs especially in decision-making, personalised treatment and future diseases prediction, and that integrating users in studying AI impact is essential to test possible implications of the technology. Conclusion: Results indicate that without a clear understanding of why patients need AI, or how AI can support individuals with their healthcare needs, it is difficult to visualise the kinds of AI applications that have a meaningful and sustainable impact the daily lives of individuals. Therefore, there is an emerging need to understand the impact of AI technology on users' behaviour to maximise the potential benefits of AI technology.
Vortrag
  • Helana Lutfi

The Impact of Artificial Intelligence Applications on Users Behaviour in the Medical Context.

  • Technische Hochschule Deggendorf.

Deggendorf 03.12.2021.

(2021)

Zeitschriftenartikel
  • Helana Lutfi
  • Rui Li
  • Thomas Spittler
  • Sascha Kreiskott
  • Katerina Volchek

Increasing Efficiency in Virtual Teaching in an International Context: E-learning and Instructional Approaches at ECRI.

In: Bavarian Journal of Applied Sciences , pg. 211-225

(2022)

DOI: 10.25929/bjas202291

Beitrag in Sammelwerk/Tagungsband
  • Anna-Maria Kasparbauer
  • Veronika Reisner
  • C. Schenk
  • A. Glas
  • Helana Lutfi
  • Oscar Blanco
  • Thomas Spittler

Sensor Devices, the Source of Innovative Therapy and Prevention.

  • In:
  • P. Plugmann
  • S. Ehsani
  • F. Thieringer
  • Patrick Glauner

Cham, Switzerland: Springer International Publishing pg. 207-226

(2022)

Beitrag in Sammelwerk/Tagungsband
  • Thomas Spittler
  • Helana Lutfi

Innovations for Sustainable Healthcare.

  • In:
  • P. Plugmann
  • S. Ehsani
  • F. Thieringer
  • Patrick Glauner

Cham, Switzerland: Springer International Publishing pg. 343-357

(2022)

Zeitschriftenartikel
  • Helana Lutfi
  • Rui Li
  • Thomas Spittler

Proposing a Framework for Virtual Teaching at the European Campus Rottal-Inn (ECRI).

In: Bavarian Journal of Applied Sciences , pg. 559-569

(2023)

DOI: 10.25929/xx1z-bk71

Abstract anzeigen

Background: Digital education aims at minimizing interferences to education among challenging times such as during COVID-19, and empowering students to experience new tools and resources while at the same time creating a safe place for educators to have control over the teaching process. Aims: With this study, the project CREATE aimed at examining the experiences of academic students with their first weeks of online teaching post COVID-19. The specific aim was to map their experiences and acquire knowledge in order to make necessary short-term adjustments in the subsequent rounds of online teaching through proposing the structure for online teaching framework. Method: This framework is based on identifying students’ perspectives with a distributed survey towards virtual teaching in the timeframe of pre- and post COVID-19 restrictions situations. Results: The results provide a framework for accessing methods and content in the form of delivery formats needed to be included in the curriculum for specialty development of online teaching. Conclusion: The methodology and results presented in this study may prove useful to educational institutions determined to target professional development curricula for students, with the criteria and skills needed to successfully organize online teaching.
Zeitschriftenartikel
  • Helana Lutfi
  • Thomas Spittler

HRpredict: Introducing a Web-Based Application for Heart Rate Prediction and Lifestyle Recommendations.

In: European Journal of Medical and Health Sciences (vol. 6) , pg. 58-61

(2024)

DOI: 10.24018/ejmed.2024.6.1.2009

Abstract anzeigen

Background: Monitoring heart health requires early detection of deviations in HR, which makes it easier to detect and address heart irregularities at an early stage. Health remote systems when combined with artificial intelligence (AI) can assist in better health outcomes through early detection of heart problems. Aims: Our main goal is to create a website application (Web-App) for web browser access, aiming to utilize a Random Forest (RF) machine learning (ML) model trained to predict the average heart rate (HR) over 10 days for different periods, and to enable lifestyle and activity recommendations. Methods: The Web-App is created using Laravel, an open-source Personal Home Page (PHP) web framework that follows the model-view-controller (MVC) architectural pattern. Results: This research resulted in a web-based ML model that can be used to predict future heart rates over a 10-day period which are utilized to establish average HR values, considering baseline and three distinct periods: morning, noon, and evening across the 10-day duration. Through this Web-App lifestyle, habit, activity, and 10-day reassessment recommendations are also provided. Conclusion: The Web-App was designed to be accessed and used through a web browser, to provide lifestyle recommendations based on predicted HR readings. To determine the impact of users adhering to recommendations, further research is required.
Zeitschriftenartikel
  • Helana Lutfi
  • Thomas Spittler
  • Hassan Ibrahim

Using synthetic data and machine learning to predict heart rate and enable lifestyle recommendations.

In: Journal of Medical Artificial Intelligence (vol. 7)

(2024)

DOI: 10.21037/jmai-24-35

Abstract anzeigen

Background: Heart rate (HR) is an essential indicator for cardiovascular (CV) health and is known to be influenced by unhealthy lifestyle habits like smoking, alcohol and caffeinated drinks consumption. However, to date the potential degree of impact of multiple lifestyle factors on HR is unknown. The main goal in this study is to train a machine learning (ML) model to capture future HR over a 10-day period and subsequent generation of lifestyle recommendations based on predicted HR. Methods: The proposed system consists of generating synthetic data, building HR random forest ML predictive model, and evaluating the performance of the model versus real participants’ data. Results: We applied the system to 25 male participants. The study results validate the system’s ability to demonstrate effectiveness in predicting HR variations in response to lifestyle factors, including smoking, alcohol, and caffeinated drinks during different times of the day. Accurate predictions were observed for baseline HR readings, compared to the poorest performance for the prediction model at noon as reflected in the mean absolute error closer to zero and high R2 score of 0.953, capturing around 95.3% of the variance in the actual data. Conclusions: The study presents the foundation for further studies to investigate the applicability of other ML techniques for predicting HR using synthetic data generation. Additionally, the model can be further explored to improve its accuracy by including additional behavioural factors that influence the HR variability.
Zeitschriftenartikel
  • L. Gerold
  • Helana Lutfi
  • Thomas Spittler

User’s Perceived Attitudes and Acceptance Towards Wearable Devices in Healthcare.

In: European Journal of Medical and Health Sciences (EJMED) (vol. 6) , pg. 10-16

(2024)

DOI: 10.24018/ejmed.2024.6.1.1990

Abstract anzeigen

With the coronavirus (COVID-19) pandemic uncovering several structural problems within the German healthcare system, especially within the inpatient sector, rapid improvements were needed to strengthen the preventive industry of the healthcare system. To adequately cover prevention as well as aftercare needs, some telemedical solutions, such as wearables can strongly contribute to the preventive sector. Therefore, this research aims to understand users’ perceived attitudes and acceptance towards wearable devices in healthcare. Following the Technology Acceptance Model, the essential factors that influence user acceptance were assessed using an online survey involving 154 participants, students of the Deggendorf Institute of Technology. The results of this survey indicate that among the students’ technology acceptance is generally high, participants had a favourable attitude towards digital health technologies, a high perception of usefulness, and a heightened perception of ease of use. Only a minor of the participants have stated that they have certain concerns, mainly regarding data protection. This study however gives very little insight into what elderly people, people in the active workforce, or those suffering from chronic illness think of wearables and digital health as a whole. So further research including this demographic of people is suggested.