Machine learning in primary care: potential to improve public health

Kang, Jungwoo; Hanif, Moghees; Mirza, Eushaa; Khan, Muhammad Asad; Malik, Muzaffar

doi:10.1080/03091902.2020.1853839

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…The process starts with historical data collection, such as instructions and direct experience, so that logical models can be built for future inference. Output accuracy depends on data size—a larger amount of data will build a better model, which in turn increases its accuracy [ 9 ].…”

Section: Artificial Intelligence Branches Explainedmentioning

confidence: 99%

“…On the basis of 18,000 ECG signals, a deep learning system can diagnose atrial fibrillation with an accuracy of 98.27% [ 53 ]. Studies have demonstrated that many general practitioners are incapable of accurately detecting and diagnosing AF based on ECGs [ 9 ]. ECGs may be useful in identifying high-risk patients, subsequently resulting in a combined approach pathway.…”

Section: Ai In Cardiologymentioning

confidence: 99%

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Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Stafie,

Sufaru,

Ghiciuc

et al. 2023

Diagnostics

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Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

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Section: Artificial Intelligence Branches Explainedmentioning

confidence: 99%

Section: Ai In Cardiologymentioning

confidence: 99%

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Stafie,

Sufaru,

Ghiciuc

et al. 2023

Diagnostics

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“…In 2013, Atencia et al (2013) proposed parameter estimation based upon NN for the estimation of HIV/AIDS. In 2020, Kang et al (2020) reviewed that how machine learning or artificial NNs can be efficiently used to provide improved healthcare. In 2014, Saberian et al (2014) developed a novel artificial NN-based forecasting system to predict the Iranian influenza epidemic.…”

Section: Related Workmentioning

confidence: 99%

A deep neural network based context-aware smart epidemic surveillance in smart cities

Gill

Sehgal

Verma

2021

LHT

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PurposeEpidemics not only affect the public health but also are a threat to a nation's growth and economy as well. Early prediction of epidemic can be beneficial to take preventive measures and to reduce the impact of epidemic in an area.Design/methodology/approachA deep neural network (DNN) based context aware smart epidemic system has been proposed to prevent and monitor epidemic spread in a geographical area. Various neural networks (NNs) have been used: LSTM, RNN, BPNN to detect the level of disease, direction of spread of disease in a geographical area and marking the high-risk areas. Multiple DNNs collect and process various data points and these DNNs are decided based on type of data points. Output of one DNN is used by another DNN to reach to final prediction.FindingsThe experimental evaluation of the proposed framework achieved the accuracy of 87% for the synthetic dataset generated for Zika epidemic in Brazil in 2016.Originality/valueThe proposed framework is designed in a way that every data point is carefully processed and contributes to the final decision. These multiple DNNs will act as a single DNN for the end user.

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“…In healthcare, AI models applied in radiology can potentially detect accurately and predict the progression of cancerous tumors [5]. Algorithms could also be useful in community-based primary health care (CBPHC) for identifying individuals, such as heart failure or diabetes outpatients, who require specific health care services [6]. As defined by the Canadian Institutes of Health Research, CBPHC encompasses a comprehensive array of services aimed at community well-being, incorporating primary prevention (including public health), health promotion, disease prevention, diagnosis, treatment, and management of chronic and episodic illnesses, rehabilitation support, and end-of-life care [7].…”

Section: Introductionmentioning

confidence: 99%

Bias Mitigation in Primary Healthcare Artificial Intelligence Models: Scoping Review (Preprint)

Sasseville,

Ouellet,

Rhéaume

et al. 2024

Preprint

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BACKGROUND Artificial intelligence (AI) predictive models in primary healthcare can potentially lead to benefits for population health. Algorithms can identify more rapidly and accurately who should receive care and health services, but they could also perpetuate or exacerbate existing biases toward diverse groups. We noticed a gap in actual knowledge about which strategies are deployed to assess and mitigate bias toward diverse groups, based on their personal or protected attributes, in primary healthcare algorithms. OBJECTIVE To describe attempts, strategies, and methods used to mitigate bias in primary healthcare artificial intelligence models. To identify which diverse groups or protected attributes have been considered. To evaluate the results on bias attenuation and AI models performance of these attempts, strategies, and methods. METHODS We conducted a scoping review informed by the Joanna Briggs Institute (JBI) review recommendations. An experienced librarian developed a search strategy in four databases (Medline (OVID), CINAHL (EBSCO), PsycInfo (OVID), and Web of Science) to identify sources published between 2017-01-01 and 2022-11-15. We imported data in Covidence and pairs of reviewers independently screened titles and abstracts, applied the selection criteria, and performed full-text screening. Any discrepancies regarding the inclusion of studies were resolved through consensus. Based on reporting standards for AI in health care, we performed data extraction - study objectives, models’ main features, diverse groups concerned, mitigation strategies deployed, and results. Using the Mixed-Methods Appraisal Tool (MMAT), we appraised the quality of studies. RESULTS After removing 585 duplicates, we screened 1018 titles and abstracts. From remaining 189 after exclusion, we excluded 172 full texts and included 17 studies. The most investigated personal or protected attributes were Race (or Ethnicity) in (12/17), and Sex (mostly identified as Gender in studies), using binary “male vs female” in (10/17) of included studies. We grouped studies according to bias mitigation attempts into the following categories: 1) existing AI models or datasets, 2) sourcing data such as Electronic Health Records, 3) developing tools with “human-in-the-loop” and 4) identifying ethical principles for informed decision-making. Mathematical and algorithmic preprocessing methods, such as changing data labeling and reweighing, along with a natural language processing method using data extraction from unstructured notes, showed the greatest potential. Other methods to enhance model fairness include group recalibration and the application of the equalized odds metric, which either exacerbated predictions errors between groups or resulted in overall models miscalibrations. CONCLUSIONS Results suggests that biases toward diverse groups can be more easily mitigated when data are open-sourced, multiple stakeholders are involved, and during the algorithm’ preprocessing stage. Further empirical studies, considering more diverse groups, such as nonbinary gender identities or Indigenous peoples in Canada, are needed to confirm and to expand this knowledge. CLINICALTRIAL OSF Registries qbph8; https://osf.io/qbph8 INTERNATIONAL REGISTERED REPORT RR2-10.2196/46684

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Machine learning in primary care: potential to improve public health

Cited by 13 publications

References 23 publications

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

A deep neural network based context-aware smart epidemic surveillance in smart cities

Bias Mitigation in Primary Healthcare Artificial Intelligence Models: Scoping Review (Preprint)

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