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474 results on '"SALEH, SHADI"'

1. Sijilli: A Scalable Model of Cloud-Based Electronic Health Records for Migrating Populations in Low-Resource Settings

Author

Saleh, Shadi, El Arnaout, Nour, Abdouni, Lina, Jammoul, Zeinab, Hachach, Noha, and Dasgupta, Amlan

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

The world is witnessing an alarming rate of displacement and migration, with more than 70.8 million forcibly displaced individuals, including 26 million refugees. These populations are known to have increased vulnerability and susceptibility to mental and physical health problems due to the migration journey. Access of these individuals to health services, whether during their trajectory of displacement or in refugee-hosting countries, remains limited and challenging due to multiple factors, including language and cultural barriers and unavailability of the refugees’ health records. Cloud-based electronic health records (EHRs) are considered among the top five health technologies integrated in humanitarian crisis preparedness and response during times of conflict. This viewpoint describes the design and implementation of a scalable and innovative cloud-based EHR named Sijilli, which targets refugees in low-resource settings. This paper discusses this solution compared with other similar practices, shedding light on its potential for scalability.

Published
2020
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2. Using Mobile Health to Enhance Outcomes of Noncommunicable Diseases Care in Rural Settings and Refugee Camps: Randomized Controlled Trial

Author

Saleh, Shadi, Farah, Angie, Dimassi, Hani, El Arnaout, Nour, Constantin, Joanne, Osman, Mona, El Morr, Christo, and Alameddine, Mohamad

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundRural areas and refugee camps are characterized by poor access of patients to needed noncommunicable disease (NCD)–related health services, including diabetes and hypertension. Employing low-cost innovative eHealth interventions, such as mobile health (mHealth), may help improve NCDs prevention and control among disadvantaged populations. ObjectiveThe aim of this study was to assess the effect of employing low-cost mHealth tools on the accessibility to health services and improvement of health indicators of individuals with NCDs in rural areas and refugee camps in Lebanon. MethodsThis is a randomized controlled trial study in which centers were allocated randomly into control and intervention sites. The effect of an employed mHealth intervention is assessed through selected quality indicators examined in both control and intervention groups. Sixteen primary health care centers (eight controls, eight interventions) located in rural areas and Palestinian refugee camps across Lebanon were included in this study. Data on diabetic and hypertensive patients—1433 in the intervention group and 926 in the control group—was extracted from patient files in the pre and postintervention periods. The intervention entailed weekly short message service messages, including medical information, importance of compliance, and reminders of appointments or regular physician follow-up. Internationally established care indicators were utilized in this study. Descriptive analysis of baseline characteristics of participants, bivariate analysis, logistic and linear regression were conducted using SPSS (IBM Corp). ResultsBivariate analysis of quality indicators indicated that the intervention group had a significant increase in blood pressure control (P=.03), as well as a significant decrease in the mean systolic blood pressure (P=.02), mean glycated hemoglobin (HbA1c; P

Published
2018
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3. Enhancing Supply Chain Resilience: A Machine Learning Approach for Predicting Product Availability Dates Under Disruption

Author

Camur, Mustafa Can, Ravi, Sandipp Krishnan, and Saleh, Shadi

Subjects
Computer Science - Machine Learning
Abstract

The COVID 19 pandemic and ongoing political and regional conflicts have a highly detrimental impact on the global supply chain, causing significant delays in logistics operations and international shipments. One of the most pressing concerns is the uncertainty surrounding the availability dates of products, which is critical information for companies to generate effective logistics and shipment plans. Therefore, accurately predicting availability dates plays a pivotal role in executing successful logistics operations, ultimately minimizing total transportation and inventory costs. We investigate the prediction of product availability dates for General Electric (GE) Gas Power's inbound shipments for gas and steam turbine service and manufacturing operations, utilizing both numerical and categorical features. We evaluate several regression models, including Simple Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network models. Based on real world data, our experiments demonstrate that the tree based algorithms (i.e., RF and GBM) provide the best generalization error and outperforms all other regression models tested. We anticipate that our prediction models will assist companies in managing supply chain disruptions and reducing supply chain risks on a broader scale.

Published
2023

17. Highway Traffic Classification for the Perception Level of Situation Awareness

Author

Nine, Julkar, Manoharan, Shanmugapriyan, Sapkota, Manoj, Saleh, Shadi, Hardt, Wolfram, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bebis, George, editor, Yin, Zhaozheng, editor, Kim, Edward, editor, Bender, Jan, editor, Subr, Kartic, editor, Kwon, Bum Chul, editor, Zhao, Jian, editor, Kalkofen, Denis, editor, and Baciu, George, editor

Published
2020
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19. Real-world evidence for coverage determination of treatments for rare diseases

Author

Dayer, Victoria W., Drummond, Michael F., Dabbous, Omar, Toumi, Mondher, Neumann, Peter, Tunis, Sean, Teich, Nelson, Saleh, Shadi, Persson, Ulf, Schulenburg, Johann-Matthias Graf von der, Malone, Daniel C., Salimullah, Tay, Sullivan, Sean D., Dayer, Victoria W., Drummond, Michael F., Dabbous, Omar, Toumi, Mondher, Neumann, Peter, Tunis, Sean, Teich, Nelson, Saleh, Shadi, Persson, Ulf, Schulenburg, Johann-Matthias Graf von der, Malone, Daniel C., Salimullah, Tay, and Sullivan, Sean D.

Abstract

Health technology assessment (HTA) decisions for pharmaceuticals are complex and evolving. New rare disease treatments are often approved more quickly through accelerated approval schemes, creating more uncertainties about clinical evidence and budget impact at the time of market entry. The use of real-world evidence (RWE), including early coverage with evidence development, has been suggested as a means to support HTA decisions for rare disease treatments. However, the collection and use of RWE poses substantial challenges. These challenges are compounded when considered in the context of treatments for rare diseases. In this paper, we describe the methodological challenges to developing and using prospective and retrospective RWE for HTA decisions, for rare diseases in particular. We focus attention on key elements of study design and analyses, including patient selection and recruitment, appropriate adjustment for confounding and other sources of bias, outcome selection, and data quality monitoring. We conclude by offering suggestions to help address some of the most vexing challenges. The role of RWE in coverage and pricing determination will grow. It is, therefore, necessary for researchers, manufacturers, HTA agencies, and payers to ensure that rigorous and appropriate scientific principles are followed when using RWE as part of decision-making.

Published
2024

21. Learning Modalities in MENA: Evaluating the Non-Governmental Organizations Initiative.

Author

Daou, Tracy, Al Rahbany, Tatiana, Brome, Dayana, Naal, Hady, Merhi, Alaa, El Arnaout, Nour, and Saleh, Shadi

Subjects
NONGOVERNMENTAL organizations, TELECOMMUNICATION systems, LEARNING strategies, CAPACITY (Law), SEMI-structured interviews
Abstract

With increasing reliance being placed on the civil society sector to respond to rising population needs in the Middle East and North Africa (MENA) region, it is crucial to strengthen the capacity of workers in Non-Governmental Organizations (NGOs) to ensure localized, contextualized, and more effective responses. However, in the absence of systematic evaluation approaches to the capacity-building opportunities offered to NGO workers in the region, evidence on the most effective mode of delivery of these training opportunities remains scarce. We evaluated training courses implemented by the Non-Governmental Organizations Initiative at the American University of Beirut between July 2019 and November 2021 that aims to strengthen the internal capacities of NGOs in MENA, with a focus on comparing online synchronous versus in-person learning modalities. Through a mixed-methods approach, short-term and long-term data was collected from learners including knowledge assessments, course evaluations, and semi-structured interviews. A total of 383 learners participated in the training, 254 of which attended online and 129 attended in-person courses. While differences in knowledge and perceptions regarding the two modalities were relatively similar, inperson modality was preferred over online modality by virtue of it being perceived as being more engaging and more conducive for communication and networking. Details regarding the results covering strengths, challenges, and comparisons between both modalities are expanded on in the manuscript. This study highlights the overall preference of learners for in-person courses compared to online synchronous ones and high lights the need for research on identifying factors that could contribute to increased engagement in online synchronous learning along with strategies for adoption. [ABSTRACT FROM AUTHOR]

Published
2024
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23. An Extended CLEF eHealth Test Collection for Cross-Lingual Information Retrieval in the Medical Domain

Author

Saleh, Shadi, Pecina, Pavel, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Azzopardi, Leif, editor, Stein, Benno, editor, Fuhr, Norbert, editor, Mayr, Philipp, editor, Hauff, Claudia, editor, and Hiemstra, Djoerd, editor

Published
2019
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24. Term Selection for Query Expansion in Medical Cross-Lingual Information Retrieval

Author

Saleh, Shadi, Pecina, Pavel, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Azzopardi, Leif, editor, Stein, Benno, editor, Fuhr, Norbert, editor, Mayr, Philipp, editor, Hauff, Claudia, editor, and Hiemstra, Djoerd, editor

Published
2019
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25. Real-world evidence for coverage determination of treatments for rare diseases

Author

Dayer, Victoria W., primary, Drummond, Michael F., additional, Dabbous, Omar, additional, Toumi, Mondher, additional, Neumann, Peter, additional, Tunis, Sean, additional, Teich, Nelson, additional, Saleh, Shadi, additional, Persson, Ulf, additional, von der Schulenburg, Johann-Matthias Graf, additional, Malone, Daniel C., additional, Salimullah, Tay, additional, and Sullivan, Sean D., additional

Published
2024
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42. Reranking Hypotheses of Machine-Translated Queries for Cross-Lingual Information Retrieval

Author

Saleh, Shadi, Pecina, Pavel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fuhr, Norbert, editor, Quaresma, Paulo, editor, Gonçalves, Teresa, editor, Larsen, Birger, editor, Balog, Krisztian, editor, Macdonald, Craig, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2016
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43. SHAMUS: UFAL Search and Hyperlinking Multimedia System

Author

Galuščáková, Petra, Saleh, Shadi, Pecina, Pavel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ferro, Nicola, editor, Crestani, Fabio, editor, Moens, Marie-Francine, editor, Mothe, Josiane, editor, Silvestri, Fabrizio, editor, Di Nunzio, Giorgio Maria, editor, Hauff, Claudia, editor, and Silvello, Gianmaria, editor

Published
2016
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47. Monocular Depth Estimation with Edge-Based Constraints using Active Learning Optimization

Author

Hardt, Wolfram, Wannous, Hazem, Technische Universität Chemnitz, Saleh, Shadi, Hardt, Wolfram, Wannous, Hazem, Technische Universität Chemnitz, and Saleh, Shadi

Abstract

Depth sensing is pivotal in robotics; however, monocular depth estimation encounters significant challenges. Existing algorithms relying on large-scale labeled data and large Deep Convolutional Neural Networks (DCNNs) hinder real-world applications. We propose two lightweight architectures that achieve commendable accuracy rates of 91.2% and 90.1%, simultaneously reducing the Root Mean Square Error (RMSE) of depth to 4.815 and 5.036. Our lightweight depth model operates at 29-44 FPS on the Jetson Nano GPU, showcasing efficient performance with minimal power consumption. Moreover, we introduce a mask network designed to visualize and analyze the compact depth network, aiding in discerning informative samples for the active learning approach. This contributes to increased model accuracy and enhanced generalization capabilities. Furthermore, our methodology encompasses the introduction of an active learning framework strategically designed to enhance model performance and accuracy by efficiently utilizing limited labeled training data. This novel framework outperforms previous studies by achieving commendable results with only 18.3% utilization of the KITTI Odometry dataset. This performance reflects a skillful balance between computational efficiency and accuracy, tailored for low-cost devices while reducing data training requirements.:1. Introduction 2. Literature Review 3. AI Technologies for Edge Computing 4. Monocular Depth Estimation Methodology 5. Implementation 6. Result and Evaluation 7. Conclusion and Future Scope Appendix

Published
2023

48. Health in times of uncertainty in the eastern Mediterranean region, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Author

Mokdad, Ali H, Forouzanfar, Mohammad Hossein, Daoud, Farah, El Bcheraoui, Charbel, Moradi-Lakeh, Maziar, Khalil, Ibrahim, Afshin, Ashkan, Tuffaha, Marwa, Charara, Raghid, Barber, Ryan M, Wagner, Joseph, Cercy, Kelly, Kravitz, Hannah, Coates, Matthew M, Robinson, Margaret, Estep, Kara, Steiner, Caitlyn, Jaber, Sara, Mokdad, Ali A, O'Rourke, Kevin F, Chew, Adrienne, Kim, Pauline, El Razek, Mohamed Magdy Abd, Abdalla, Safa, Abd-Allah, Foad, Abraham, Jerry P, Abu-Raddad, Laith J, Abu-Rmeileh, Niveen M E, Al-Nehmi, Abdulwahab A, Akanda, Ali S, Al Ahmadi, Hanan, Al Khabouri, Mazin J, Al Lami, Faris H, Al Rayess, Zulfa A, Alasfoor, Deena, AlBuhairan, Fadia S, Aldhahri, Saleh F, Alghnam, Suliman, Alhabib, Samia, Al-Hamad, Nawal, Ali, Raghib, Ali, Syed Danish, Alkhateeb, Mohammad, AlMazroa, Mohammad A, Alomari, Mahmoud A, Al-Raddadi, Rajaa, Alsharif, Ubai, Al-Sheyab, Nihaya, Alsowaidi, Shirina, Al-Thani, Mohamed, Altirkawi, Khalid A, Amare, Azmeraw T, Amini, Heresh, Ammar, Walid, Anwari, Palwasha, Asayesh, Hamid, Asghar, Rana, Assabri, Ali M, Assadi, Reza, Bacha, Umar, Badawi, Alaa, Bakfalouni, Talal, Basulaiman, Mohammed O, Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Bhakta, Amit R, Bhutta, Zulfiqar A, Bin Abdulhak, Aref A, Boufous, Soufiane, Bourne, Rupert R A, Danawi, Hadi, Das, Jai, Deribew, Amare, Ding, Eric L, Durrani, Adnan M, Elshrek, Yousef, Ibrahim, Mohamed E, Eshrati, Babak, Esteghamati, Alireza, Faghmous, Imad A D, Farzadfar, Farshad, Feigl, Andrea B, Fereshtehnejad, Seyed-Mohammad, Filip, Irina, Fischer, Florian, Gankpé, Fortuné G, Ginawi, Ibrahim, Gishu, Melkamu Dedefo, Gupta, Rahul, Habash, Rami M, Hafezi-Nejad, Nima, Hamadeh, Randah R, Hamdouni, Hayet, Hamidi, Samer, Harb, Hilda L, Hassanvand, Mohammad Sadegh, Hedayati, Mohammad T, Heydarpour, Pouria, Hsairi, Mohamed, Husseini, Abdullatif, Jahanmehr, Nader, Jha, Vivekanand, Jonas, Jost B, Karam, Nadim E, Kasaeian, Amir, Kassa, Nega Assefa, Kaul, Anil, Khader, Yousef, Khalifa, Shams Eldin A, Khan, Ejaz A, Khan, Gulfaraz, Khoja, Tawfik, Khosravi, Ardeshir, Kinfu, Yohannes, Defo, Barthelemy Kuate, Balaji, Arjun Lakshmana, Lunevicius, Raimundas, Obermeyer, Carla Makhlouf, Malekzadeh, Reza, Mansourian, Morteza, Marcenes, Wagner, Farid, Habibolah Masoudi, Mehari, Alem, Mehio-Sibai, Abla, Memish, Ziad A, Mensah, George A, Mohammad, Karzan A, Nahas, Ziad, Nasher, Jamal T, Nawaz, Haseeb, Nejjari, Chakib, Nisar, Muhammad Imran, Omer, Saad B, Parsaeian, Mahboubeh, Peprah, Emmanuel K, Pervaiz, Aslam, Pourmalek, Farshad, Qato, Dima M, Qorbani, Mostafa, Radfar, Amir, Rafay, Anwar, Rahimi, Kazem, Rahimi-Movaghar, Vafa, Rahman, Sajjad Ur, Rai, Rajesh K, Rana, Saleem M, Rao, Sowmya R, Refaat, Amany H, Resnikoff, Serge, Roshandel, Gholamreza, Saade, Georges, Saeedi, Mohammad Y, Sahraian, Mohammad Ali, Saleh, Shadi, Sanchez-Riera, Lidia, Satpathy, Maheswar, Sepanlou, Sadaf G, Setegn, Tesfaye, Shaheen, Amira, Shahraz, Saeid, Sheikhbahaei, Sara, Shishani, Kawkab, Sliwa, Karen, Tavakkoli, Mohammad, Terkawi, Abdullah S, Uthman, Olalekan A, Westerman, Ronny, Younis, Mustafa Z, El Sayed Zaki, Maysaa, Zannad, Faiez, Roth, Gregory A, Wang, Haidong, Naghavi, Mohsen, Vos, Theo, Al Rabeeah, Abdullah A, Lopez, Alan D, and Murray, Christopher J L

Published
2016
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