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8 results on '"Althobaiti, Shahad"'

1. Longitudinal Complex Dynamics of Labour Markets Reveal Increasing Polarisation

Author

Althobaiti, Shahad, Alabdulkareem, Ahmad, Shen, Judy Hanwen, Rahwan, Iyad, Frank, Morgan, Moro, Esteban, and Rutherford, Alex

Subjects
Computer Science - Computers and Society
Abstract

In this paper we conduct a longitudinal analysis of the structure of labour markets in the US over 7 decades of technological, economic and policy change. We make use of network science, natural language processing and machine learning to uncover structural changes in the labour market over time. We find a steady rate of both disappearance of jobs and a shift in the required work tasks, despite much technological and economic change over this time period. Machine learning is used to classify jobs as being predominantly cognitive or physical based on the textual description of the workplace tasks. We also measure increasing polarisation between these two classes of jobs, linked by the similarity of tasks, over time that could constrain workers wishing to move to different jobs.

Published
2022

2. 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

3. Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings.

Author

LANDAU, Aviv Y., BLANCHARD, Ashley, KULKARNI, Paritosh, ALTHOBAITI, Shahad, IDNAY, Betina, PATTON, Desmond U., CATO, Kenrick, and TOPAZ, Maxim

Abstract

Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often complex. Our study developed a machine learning model using structured and unstructured electronic health record (EHR) data to predict when children in EDs might need intervention from child protective services. We used a case-control study design, analyzing data from a pediatric ED. Clinical notes were processed with natural language processing (NLP) techniques to identify suspected cases and matched in a 1:9 ratio to ensure dataset balance. The features from these notes were combined with structured EHR data to construct a model using the XGBoost algorithm. The model achieved a precision of 0.95, recall of 0.88, and F1-score of 0.92, with improvements seen from integrating NLP-derived data. Key indicators for abuse included hospital admissions, extended ED stays, and specific clinical orders. The model's accuracy and the utility of NLP suggest the potential for EDs to better identify at-risk children. Future work should validate the model further and explore additional features while considering ethical implications to aid healthcare providers in safeguarding children. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Prevalence of Using Medicinal and Edible Plants During the Covid-19 Pandemic in Taif-Saudi Arabia.

Author

Almathkori, Raghad Suliman, Alotaibi, Rema Nasser, Alhumaidi, Munirah Sultan, Alotibi, Shreifa Thaar, Althobaiti, Shahad Ayidh, and Farag, Salwa Farouk

Subjects
SARS-CoV-2, COVID-19 pandemic, EDIBLE plants, COVID-19, MEDICINAL plants, POMEGRANATE
Abstract

In early January 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the agent responsible for coronavirus disease 2019 (COVID-19). Recently, a high percentage of patients show a high interest in natural medicines. This is mainly due to the general feeling that natural medicines are safer than synthetic drugs. This study is designed to measure the prevalence of medical and edible plants during the (COVID-19) pandemic in Taif-Saudi Arabia. A cross-sectional study was conducted. Data was collected online using a Google forms survey. The online questionnaire was distributed to participants aged 18 years or older, living in Taif city. The data were analyzed by using IBM-SPSS version 25. In this study, we collected data from 325 participants where 76.9% of the participants were females. Considering the use of plants, 25.7% of the participants reported using Cinnamon while 20% of the participants reported using Star anise. Among the users of plants, family and friends were the main advisors of using these plants at 44.62% followed by social media at 23.69%. Moreover, 62.4 % of the participants reported using plants to protect against COVID-19 while 37.5 % reported using plants in relieving the symptoms of COVID-19. There is a significant increase in the trend of using herbs and natural products among Saudi populations, due to the COVID-19 pandemic. Cinnamon, Star Anise, and pomegranate were the main reported plants to be used by the participants. [ABSTRACT FROM AUTHOR]

Published
2023
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6. Housing Prices and the Skills Composition of Neighborhoods

Author

Massachusetts Institute of Technology. Media Laboratory, Althobaiti, Shahad, Alghumayjan, Saud, Frank, Morgan R., Moro Egido, Esteban, Alabdulkareem, Ahmad, Pentland, Alexander (Sandy), Massachusetts Institute of Technology. Media Laboratory, Althobaiti, Shahad, Alghumayjan, Saud, Frank, Morgan R., Moro Egido, Esteban, Alabdulkareem, Ahmad, and Pentland, Alexander (Sandy)

Abstract

In the United States (US), low-income workers are being pushed away from city centers where the cost of living is high. The effects of such changes on labor mobility and housing price have been explored in the literature. However, few studies have focused on the occupations and specific skills that identify the most susceptible workers. For example, it has become increasingly challenging to fill the service sector jobs in the San Francisco (SF) Bay Area because appropriately skilled workers cannot afford the growing cost of living within commuting distance. With this example in mind, how does a neighborhood's skill composition change as a result of higher housing prices? Are there certain skill sets that are being pushed to the geographical periphery of a city despite their essentialness to the city's economy? Our study focuses on the impact of housing prices with a granular view of skills compositions to answer the following question: Has the density of cognitive skill workers been increasing in a gentrified area? We hypothesize that, over time, low-skilled workers are pushed away from downtown or areas where high-skill establishments thrive. Our preliminary results show that high-level cognitive skills are getting closer to the city center indicating adaptation to the increase of median housing prices as opposed to low-level physical skills that got further away. We examined tracts that the literature indicates as gentrified areas and found a pattern in which there is a temporal increase in median housing prices and the number of business establishments coupled with an increase in the percentage of skilled cognitive workers.

Published
2021

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