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11 results on '"Aleissa, Faisal"'

1. Cardea: An Open Automated Machine Learning Framework for Electronic Health Records

Author

Alnegheimish, Sarah, Alrashed, Najat, Aleissa, Faisal, Althobaiti, Shahad, Liu, Dongyu, Alsaleh, Mansour, and Veeramachaneni, Kalyan

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted and verified software framework exists, forcing researchers to arduously repeat previous work. In this paper, we propose Cardea, an extensible open-source automated machine learning framework encapsulating common prediction problems in the health domain and allows users to build predictive models with their own data. This system relies on two components: Fast Healthcare Interoperability Resources (FHIR) -- a standardized data structure for electronic health systems -- and several AUTOML frameworks for automated feature engineering, model selection, and tuning. We augment these components with an adaptive data assembler and comprehensive data- and model- auditing capabilities. We demonstrate our framework via 5 prediction tasks on MIMIC-III and Kaggle datasets, which highlight Cardea's human competitiveness, flexibility in problem definition, extensive feature generation capability, adaptable automatic data assembler, and its usability.

Published
2020

2. Deploy, Connect and Execute Scientific Models

Author

Aleissa, Faisal, Alrashed, Najat, Alsuabiee, Sattam, Alfaris, Anas, Sanchez, Abel, Alabdulkareem, Ahmad, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Poniszewska-Maranda, Aneta, editor, and Enokido, Tomoya, editor

Published
2021
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3. Age density patterns in patients medical conditions: A clustering approach.

Author

Alhasoun, Fahad, Aleissa, Faisal, Alhazzani, May, Moyano, Luis, Pinhanez, Claudio, and González, Marta

Subjects
Age Factors, Algorithms, Brazil, Cluster Analysis, Disease, Epidemiologic Methods, Epidemiology, Female, Humans, International Classification of Diseases, Male, Sex Factors
Abstract

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.

Published
2018

5. Collaborative Tangible Interface (CoTI) for Complex Decision Support Systems

Author

Aldawood, Salma, Aleissa, Faisal, Almalki, Almaha, Alrashed, Tarfah, Alhindi, Tariq, Alnasser, Riyadh, Hadhrawi, Mohammad K., Alfaris, Anas, Al-Wabi, Areej, 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, and Marcus, Aaron, editor

Published
2015
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6. Interaction Design in a Tangible Collaborative Decision Support System: The City Schema DSS

Author

Aldawood, Salma, Aleissa, Faisal, Alnasser, Riyadh, Alfaris, Anas, Al-Wabil, Areej, Junqueira Barbosa, Simone Diniz, editor, Chen, Phoebe, editor, Cuzzocrea, Alfredo, editor, Du, Xiaoyong, editor, Filipe, Joaquim, editor, Kara, Orhun, editor, Kotenko, Igor, editor, Sivalingam, Krishna M., editor, Ślęzak, Dominik, editor, Washio, Takashi, editor, Yang, Xiaokang, editor, and Stephanidis, Constantine, editor

Published
2014
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10. Age density patterns in patients medical conditions: A clustering approach

Author

Fahad Alhasoun, Marta C. González, Luis Gregorio Moyano, Faisal Aleissa, Claudio S. Pinhanez, May Alhazzani, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Institute for Data, Systems, and Society, Massachusetts Institute of Technology. Program in Computation for Design and Optimization, Program in Media Arts and Sciences (Massachusetts Institute of Technology), Alhasoun, Fahad, Aleissa, Faisal Saad, Alhazzani, May, Pinhanez, Claudio S., and Gonzalez, Marta C.

Subjects
0301 basic medicine, Male, Viral Diseases, Medical Doctors, Epidemiology, Health Care Providers, purl.org/becyt/ford/1.7 [https], Health records, Geographical locations, purl.org/becyt/ford/1 [https], 0302 clinical medicine, Elderly, Chickenpox, Medicine and Health Sciences, Cluster Analysis, Disease, Medical Personnel, lcsh:QH301-705.5, COMPLEX NETWORKS, Geriatrics, Heterogeneous sample, Ecology, Age Factors, Professions, Geography, Infectious Diseases, Computational Theory and Mathematics, Modeling and Simulation, Female, HEALTHCARE, Algorithms, Brazil, Research Article, medicine.medical_specialty, Large population, Age and sex, 03 medical and health sciences, Cellular and Molecular Neuroscience, DATA ANALYSIS, Sex Factors, Age Distribution, Population Metrics, International Classification of Diseases, Diagnostic Medicine, Genetics, medicine, Humans, In patient, Cluster analysis, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Population Density, Population Biology, Biology and Life Sciences, South America, medicine.disease, Comorbidity, Health Care, 030104 developmental biology, lcsh:Biology (General), Age Groups, Population Groupings, People and places, COMORBIDITY, Epidemiologic Methods, 030217 neurology & neurosurgery, Demography
Abstract

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature., Author summary Age and sex of a patient can be directly related to susceptibilities to certain medical conditions. We present a method to generate clusters of human phenotype, based on the age of the population. This method helps extract knowledge on age and sex from the data. The age and sex correlations with disease conditions can help in a task of predicting the susceptibility of incoming patients to conditions.

Published
2017

11. Community pharmacists' knowledge of isotretinoin safety.

Author

Alrwisan AA, Alshammari TM, Tahir KW, Aleissa FM, and Aljadhey HS

Subjects
Cross-Sectional Studies, Humans, Knowledge, Saudi Arabia, Workforce, Isotretinoin adverse effects, Pharmacies, Pharmacists
Abstract

Objective: To explore the practice and knowledge of community pharmacists in Saudi Arabia regarding dispensing isotretinoin-containing products., Methods: This was a cross-sectional study conducted in 2012 that included community pharmacists from 3 cities in Saudi Arabia. A validated and piloted self-administered survey collected demographics and information on the pharmacist's knowledge regarding isotretinoin precautions, as well as his dispensing, and counseling practices., Results: One hundred and sixteen questionnaires were returned with a 72.5% response rate. Only around half of the participants (56%) knew the correct pregnancy risk classification category for oral isotretinoin. Most participants (78%) correctly identified teratogenicity as the most serious risk associated with the use of oral isotretinoin. However, only 6.2% of the pharmacists recommended using 2 methods of contraception. Almost one-fifth of the pharmacists dispensed isotretinoin without a prescription. Finally, 11% of the pharmacists did not ask whether the patient performed a pregnancy test prior to dispensing oral isotretinoin., Conclusion: Pharmacists at community pharmacies are not adequately aware of the risks for female patients using isotretinoin. Additionally, an alarming proportion of pharmacists dispense isotretinoin without a prescription. It is essential to implement risk minimization plans for certain medications to limit and prevent adverse drug reactions.

Published
2014

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