1
|
Huguet N, Chen J, Parikh RB, Marino M, Flocke SA, Likumahuwa-Ackman S, Bekelman J, DeVoe JE. Applying Machine Learning Techniques to Implementation Science. Online J Public Health Inform 2024; 16:e50201. [PMID: 38648094 DOI: 10.2196/50201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/15/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.
Collapse
Affiliation(s)
- Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Jinying Chen
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Data Science Core, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- iDAPT Implementation Science Center for Cancer Control, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ravi B Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Susan A Flocke
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Sonja Likumahuwa-Ackman
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Justin Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, United States
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| |
Collapse
|
2
|
Murthy D, Ouellette RR, Anand T, Radhakrishnan S, Mohan NC, Lee J, Kong G. Using Computer Vision to Detect E-cigarette Content in TikTok Videos. Nicotine Tob Res 2024; 26:S36-S42. [PMID: 38366342 PMCID: PMC10873490 DOI: 10.1093/ntr/ntad184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 09/15/2023] [Accepted: 09/27/2023] [Indexed: 02/18/2024]
Abstract
INTRODUCTION Previous research has identified abundant e-cigarette content on social media using primarily text-based approaches. However, frequently used social media platforms among youth, such as TikTok, contain primarily visual content, requiring the ability to detect e-cigarette-related content across large sets of videos and images. This study aims to use a computer vision technique to detect e-cigarette-related objects in TikTok videos. AIMS AND METHODS We searched 13 hashtags related to vaping on TikTok (eg, #vape) in November 2022 and obtained 826 still images extracted from a random selection of 254 posts. We annotated images for the presence of vaping devices, hands, and/or vapor clouds. We developed a YOLOv7-based computer vision model to detect these objects using 85% of extracted images (N = 705) for training and 15% (N = 121) for testing. RESULTS Our model's recall value was 0.77 for all three classes: vape devices, hands, and vapor. Our model correctly classified vape devices 92.9% of the time, with an average F1 score of 0.81. CONCLUSIONS The findings highlight the importance of having accurate and efficient methods to identify e-cigarette content on popular video-based social media platforms like TikTok. Our findings indicate that automated computer vision methods can successfully detect a range of e-cigarette-related content, including devices and vapor clouds, across images from TikTok posts. These approaches can be used to guide research and regulatory efforts. IMPLICATIONS Object detection, a computer vision machine learning model, can accurately and efficiently identify e-cigarette content on a primarily visual-based social media platform by identifying the presence of vaping devices and evidence of e-cigarette use (eg, hands and vapor clouds). The methods used in this study can inform computational surveillance systems for detecting e-cigarette content on video- and image-based social media platforms to inform and enforce regulations of e-cigarette content on social media.
Collapse
Affiliation(s)
- Dhiraj Murthy
- Moody College of Communication, University of Texas at Austin, Austin, TX, USA
| | | | - Tanvi Anand
- Cockrell School of Engineering, University of Texas at Austin, Austin, TX, USA
| | - Srijith Radhakrishnan
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal, Karnataka, India
| | - Nikhil C Mohan
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal, Karnataka, India
| | - Juhan Lee
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Grace Kong
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| |
Collapse
|
3
|
Ramezani M, Takian A, Bakhtiari A, Rabiee HR, Ghazanfari S, Mostafavi H. The application of artificial intelligence in health policy: a scoping review. BMC Health Serv Res 2023; 23:1416. [PMID: 38102620 PMCID: PMC10722786 DOI: 10.1186/s12913-023-10462-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking. METHODS We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords "artificial intelligence" and "policymaking." We used Walt and Gilson's policy triangle framework for charting the data. RESULTS The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts. CONCLUSION Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI's potential to foster the meaningful use of evidence-based policymaking.
Collapse
Affiliation(s)
- Maryam Ramezani
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Health Equity Research Center (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Takian
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
- Department of Global Health and Public Policy, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
- Health Equity Research Center (HERC), Tehran University of Medical Sciences, Tehran, Iran.
| | - Ahad Bakhtiari
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Health Equity Research Center (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid R Rabiee
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Sadegh Ghazanfari
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Hakimeh Mostafavi
- Health Equity Research Center (HERC), Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|