filmov
tv
Somayeh Amini & Shveta Bhasker | Unlocking the power of electronic health records
Показать описание
Presented at the 2023 Toronto Public Tech Workshop, co-hosted by the Schwartz Reisman Institute for Technology and Society and the Munk School of Global Affairs & Public Policy at the University of Toronto.
Speaker: Somayeh Amini and Shveta Bhasker (Institute of Health Policy, Management and Evaluation, University of Toronto)
Paper title: “Unlocking the power of EHRs: Harnessing unstructured data for machine learning-based outcome predictions”
Abstract:
Integrating electronic health records (EHRs) with machine learning (ML) models has become imperative in examining patient outcomes due to their vast amounts of clinical data. However, critical information regarding social and behavioral factors that affect health, such as mental health complexities, is often recorded in unstructured clinical notes, hindering its accessibility. This has resulted in an over-reliance on clinical data in current EHR-based research, leading to disparities in health outcomes. This study aims to evaluate the impact of incorporating patient- specific context from unstructured EHR data on the accuracy and stability of ML algorithms for predicting mortality. This study analyzed a sample of 1,058 patient records from the Medical Information Mart for Intensive Care III (MIMIC III) database to identify mental health disorders among adults admitted to intensive care units between 2001 and 2012. All clinical notes from each patient’s most recent ICU stay were evaluated to acquire a comprehensive understanding of their mental health issues based on unstructured data. We examined a variety of machine learning classifiers, including Logistic Regression, kernel-based Support Vector Machines, decision-tree-based Random Forest, XGBoost, ExtraTrees, and sample-based KNearest Neighbors. Results from the study confirmed the significance of incorporating patient-specific information into prediction models, leading to a notable improvement in the discriminatory power and robustness of the ML algorithms. In addition, the findings underline the importance of considering non-clinical factors related to a patient’s daily life and clinical characteristics when predicting patient outcomes. These results significantly improve ML in clinical decision support and patient outcome predictions.
About the speakers
Somayeh Amini and Shveta Bhasker are Master of Health Informatics (MHI) students at the Institute of Health Policy, Management and Evaluation, at the University of Toronto’s Dalla Lana School of Public Health. Somayeh Amini holds a PharmD degree and, as a pharmacist, has worked in different management and leadership positions in community pharmacies and pharmaceutical companies in Iran, her home country. Amini joined the MHI program to pursue her dream of harnessing technology to provide patients with better care and experience, and is passionate about deploying artificial intelligence and machine learning to help cancer patients, especially those in hospice/end-of-life care. She believes health informatics can provide the knowledge and skills to improve these patients’ care and outcomes, including survival, quality of life, and treatment costs. Shveta Bhasker has published research in infectious diseases, and has a background of professional experience in public and global health. She is interested in the intersection between health informatics and social determinants of health while incorporating an equity, diversity, and inclusion mindset.
About the Toronto Public Tech Workshop
The Toronto Public Tech Workshop enables researchers from a wide range of disciplines to present new work that explores the use of technology for public purposes. Presenters will share and discuss ideas on how to leverage new and existing technologies for public purposes, integrate policy and governance considerations, and build successful partnerships that engage with democratic institutions and public values.
About the Schwartz Reisman Institute
Located at the University of Toronto, the Schwartz Reisman Institute for Technology and Society’s mission is to deepen our knowledge of technologies, societies, and what it means to be human by integrating research across traditional boundaries and building human-centred solutions that really make a difference.
Speaker: Somayeh Amini and Shveta Bhasker (Institute of Health Policy, Management and Evaluation, University of Toronto)
Paper title: “Unlocking the power of EHRs: Harnessing unstructured data for machine learning-based outcome predictions”
Abstract:
Integrating electronic health records (EHRs) with machine learning (ML) models has become imperative in examining patient outcomes due to their vast amounts of clinical data. However, critical information regarding social and behavioral factors that affect health, such as mental health complexities, is often recorded in unstructured clinical notes, hindering its accessibility. This has resulted in an over-reliance on clinical data in current EHR-based research, leading to disparities in health outcomes. This study aims to evaluate the impact of incorporating patient- specific context from unstructured EHR data on the accuracy and stability of ML algorithms for predicting mortality. This study analyzed a sample of 1,058 patient records from the Medical Information Mart for Intensive Care III (MIMIC III) database to identify mental health disorders among adults admitted to intensive care units between 2001 and 2012. All clinical notes from each patient’s most recent ICU stay were evaluated to acquire a comprehensive understanding of their mental health issues based on unstructured data. We examined a variety of machine learning classifiers, including Logistic Regression, kernel-based Support Vector Machines, decision-tree-based Random Forest, XGBoost, ExtraTrees, and sample-based KNearest Neighbors. Results from the study confirmed the significance of incorporating patient-specific information into prediction models, leading to a notable improvement in the discriminatory power and robustness of the ML algorithms. In addition, the findings underline the importance of considering non-clinical factors related to a patient’s daily life and clinical characteristics when predicting patient outcomes. These results significantly improve ML in clinical decision support and patient outcome predictions.
About the speakers
Somayeh Amini and Shveta Bhasker are Master of Health Informatics (MHI) students at the Institute of Health Policy, Management and Evaluation, at the University of Toronto’s Dalla Lana School of Public Health. Somayeh Amini holds a PharmD degree and, as a pharmacist, has worked in different management and leadership positions in community pharmacies and pharmaceutical companies in Iran, her home country. Amini joined the MHI program to pursue her dream of harnessing technology to provide patients with better care and experience, and is passionate about deploying artificial intelligence and machine learning to help cancer patients, especially those in hospice/end-of-life care. She believes health informatics can provide the knowledge and skills to improve these patients’ care and outcomes, including survival, quality of life, and treatment costs. Shveta Bhasker has published research in infectious diseases, and has a background of professional experience in public and global health. She is interested in the intersection between health informatics and social determinants of health while incorporating an equity, diversity, and inclusion mindset.
About the Toronto Public Tech Workshop
The Toronto Public Tech Workshop enables researchers from a wide range of disciplines to present new work that explores the use of technology for public purposes. Presenters will share and discuss ideas on how to leverage new and existing technologies for public purposes, integrate policy and governance considerations, and build successful partnerships that engage with democratic institutions and public values.
About the Schwartz Reisman Institute
Located at the University of Toronto, the Schwartz Reisman Institute for Technology and Society’s mission is to deepen our knowledge of technologies, societies, and what it means to be human by integrating research across traditional boundaries and building human-centred solutions that really make a difference.