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  1. Article ; Online: A machine learning model for predicting, diagnosing, and mitigating health disparities in hospital readmission

    Shaina Raza

    Healthcare Analytics, Vol 2, Iss , Pp 100100- (2022)

    2022  

    Abstract: The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. Therefore, it is important to predict the need for diabetic patients to be hospitalized. However, using standard machine learning ... ...

    Abstract The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. Therefore, it is important to predict the need for diabetic patients to be hospitalized. However, using standard machine learning approaches to make these predictions may result in health disparities caused by biases in the data related to social determinants (such as race, age, and gender). These biases must be removed early in the data collection process, before they enter the system and are reinforced by model predictions, resulting in biases in the model’s decisions. In this paper, we propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases in the data and model predictions. This pipeline analyses the clinical data and determines whether biases exist in the data, if so, it removes those biases before making predictions. We evaluate the performance of the proposed method on a clinical dataset using accuracy and fairness measures. The findings of the results show that when we mitigate biases early during the data ingestion, we get fairer predictions.
    Keywords Predictive and diagnostic analytics ; Machine learning ; Artificial intelligence ; Hyperglycemia ; Health disparity ; Accuracy ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A COVID-19 Search Engine (CO-SE) with Transformer-based architecture

    Shaina Raza

    Healthcare Analytics, Vol 2, Iss , Pp 100068- (2022)

    2022  

    Abstract: Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and ... ...

    Abstract Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. The CO-SE has a retriever component trained on the TF–IDF vectorizer that retrieves the relevant documents from the system. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.
    Keywords CORD-19 ; COVID-19 ; Deep learning ; Transformer models ; Search Engine ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: A COVID-19 Search Engine (CO-SE) with Transformer-based architecture.

    Raza, Shaina

    Healthcare analytics (New York, N.Y.)

    2022  Volume 2, Page(s) 100068

    Abstract: Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and ... ...

    Abstract Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. The CO-SE has a retriever component trained on the TF-IDF vectorizer that retrieves the relevant documents from the system. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.
    Language English
    Publishing date 2022-06-06
    Publishing country United States
    Document type Journal Article
    ISSN 2772-4425
    ISSN (online) 2772-4425
    DOI 10.1016/j.health.2022.100068
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Navigating News Narratives

    Raza, Shaina

    A Media Bias Analysis Dataset

    2023  

    Abstract: The proliferation of biased news narratives across various media platforms has become a prominent challenge, influencing public opinion on critical topics like politics, health, and climate change. This paper introduces the "Navigating News Narratives: A ...

    Abstract The proliferation of biased news narratives across various media platforms has become a prominent challenge, influencing public opinion on critical topics like politics, health, and climate change. This paper introduces the "Navigating News Narratives: A Media Bias Analysis Dataset", a comprehensive dataset to address the urgent need for tools to detect and analyze media bias. This dataset encompasses a broad spectrum of biases, making it a unique and valuable asset in the field of media studies and artificial intelligence. The dataset is available at https://huggingface.co/datasets/newsmediabias/news-bias-full-data.
    Keywords Computer Science - Computation and Language
    Publishing date 2023-11-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Auditing ICU Readmission Rates in an Clinical Database

    Raza, Shaina

    An Analysis of Risk Factors and Clinical Outcomes

    2023  

    Abstract: This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for classification ...

    Abstract This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for classification and the fairness audit is conducted on the model predictions. The fairness audit uncovers disparities in equal opportunity, predictive parity, false positive rate parity, and false negative rate parity criteria on the MIMIC III dataset based on attributes such as gender, ethnicity, language, and insurance group. The results identify disparities in the model's performance across different groups and highlights the need for better fairness and bias mitigation strategies. The study suggests the need for collaborative efforts among researchers, policymakers, and practitioners to address bias and fairness in artificial intelligence (AI) systems.
    Keywords Computer Science - Machine Learning
    Subject code 338
    Publishing date 2023-04-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Connecting Fairness in Machine Learning with Public Health Equity

    Raza, Shaina

    2023  

    Abstract: Machine learning (ML) has become a critical tool in public health, offering the potential to improve population health, diagnosis, treatment selection, and health system efficiency. However, biases in data and model design can result in disparities for ... ...

    Abstract Machine learning (ML) has become a critical tool in public health, offering the potential to improve population health, diagnosis, treatment selection, and health system efficiency. However, biases in data and model design can result in disparities for certain protected groups and amplify existing inequalities in healthcare. To address this challenge, this study summarizes seminal literature on ML fairness and presents a framework for identifying and mitigating biases in the data and model. The framework provides guidance on incorporating fairness into different stages of the typical ML pipeline, such as data processing, model design, deployment, and evaluation. To illustrate the impact of biases in data on ML models, we present examples that demonstrate how systematic biases can be amplified through model predictions. These case studies suggest how the framework can be used to prevent these biases and highlight the need for fair and equitable ML models in public health. This work aims to inform and guide the use of ML in public health towards a more ethical and equitable outcome for all populations.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
    Subject code 006
    Publishing date 2023-04-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission

    Raza, Shaina

    2022  

    Abstract: The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. Therefore, it is important to predict the need for diabetic patients to be hospitalized. However, using standard machine learning ... ...

    Abstract The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. Therefore, it is important to predict the need for diabetic patients to be hospitalized. However, using standard machine learning approaches to make these predictions may result in health disparities caused by biases in the data related to social determinants (such as race, age, and gender). These biases must be removed early in the data collection process, before they enter the system and are reinforced by model predictions, resulting in biases in the model's decisions. In this paper, we propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases in the data and model predictions. This pipeline analyses the clinical data and determines whether biases exist in the data, if so, it removes those biases before making predictions. We evaluate the performance of the proposed method on a clinical dataset using accuracy and fairness measures. The findings of the results show that when we mitigate biases early during the data ingestion, we get fairer predictions.

    Comment: report
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
    Subject code 006
    Publishing date 2022-06-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  8. Book ; Online: A COVID-19 Search Engine (CO-SE) with Transformer-based Architecture

    Raza, Shaina

    2022  

    Abstract: Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and ... ...

    Abstract Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. The CO-SE has a retriever component trained on the TF-IDF vectorizer that retrieves the relevant documents from the system. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.

    Comment: Accepted in HealthCare Analytics
    Keywords Computer Science - Information Retrieval
    Subject code 006
    Publishing date 2022-06-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  9. Article ; Online: Discovering Social Determinants of Health from Case Reports using Natural Language Processing: Algorithmic Development and Validation

    Raza, Shaina

    medRxiv

    Abstract: Background: Social determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available via electronic health records, clinical reports, and social media, usually in free texts format, ... ...

    Abstract Background: Social determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available via electronic health records, clinical reports, and social media, usually in free texts format, which poses a significant challenge and necessitates the use of natural language processing (NLP) techniques to extract key information. Objective: The objective of this research is to advance the automatic extraction of SDOH from clinical texts. Setting and Data: The case reports of COVID-19 patients from the published literature are curated to create a corpus. A portion of the data is annotated by experts to create gold labels, and active learning is used for corpus re-annotation. Methods: A named entity recognition (NER) framework is developed and tested to extract SDOH along with a few prominent clinical entities (diseases, treatments, diagnosis) from the free texts. The proposed model consists of three deep neural networks-A Transformer-based model, a BiLSTM model and a CRF module. Results: The proposed NER implementation achieves an accuracy (F1-score) of 92.98% on our test set and generalizes well on benchmark data. A careful analysis of case examples demonstrates the superiority of the proposed approach in correctly classifying the named entities. Conclusions: NLP can be used to extract key information, such as SDOH from free texts. A more accurate understanding of SDOH is needed to further improve healthcare outcomes.
    Keywords covid19
    Language English
    Publishing date 2022-12-05
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2022.11.30.22282946
    Database COVID19

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  10. Article ; Online: Constructing a disease database and using natural language processing to capture and standardize free text clinical information.

    Raza, Shaina / Schwartz, Brian

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 8591

    Abstract: The ability to extract critical information about an infectious disease in a timely manner is critical for population health research. The lack of procedures for mining large amounts of health data is a major impediment. The goal of this research is to ... ...

    Abstract The ability to extract critical information about an infectious disease in a timely manner is critical for population health research. The lack of procedures for mining large amounts of health data is a major impediment. The goal of this research is to use natural language processing (NLP) to extract key information (clinical factors, social determinants of health) from free text. The proposed framework describes database construction, NLP modules for locating clinical and non-clinical (social determinants) information, and a detailed evaluation protocol for evaluating results and demonstrating the effectiveness of the proposed framework. The use of COVID-19 case reports is demonstrated for data construction and pandemic surveillance. The proposed approach outperforms benchmark methods in F1-score by about 1-3%. A thorough examination reveals the disease's presence as well as the frequency of symptoms in patients. The findings suggest that prior knowledge gained through transfer learning can be useful when researching infectious diseases with similar presentations in order to accurately predict patient outcomes.
    MeSH term(s) Humans ; Natural Language Processing ; COVID-19/epidemiology ; Electronic Health Records ; Records ; Pandemics
    Language English
    Publishing date 2023-05-26
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-35482-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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