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- Arora, Anmol, et al.
(författare)
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The value of standards for health datasets in artificial intelligence-based applications
- 2023
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Ingår i: Nature Medicine. - : NATURE PORTFOLIO. - 1078-8956 .- 1546-170X. ; 29, s. 2929-2938
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative). A systematic review, combined with a stakeholder survey, presents an overview of current practices and recommendations for dataset curation in health, with specific focuses on data diversity and artificial intelligence-based applications.
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| 2. |
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| 3. |
- Petch, Jeremy, et al.
(författare)
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Optimizing warfarin dosing for patients with atrial fibrillation using machine learning
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R-2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.
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| 5. |
- Zekavat, Seyedeh M., et al.
(författare)
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TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease
- 2023
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Ingår i: Nature Cardiovascular Research. - : Springer Science and Business Media LLC. - 2731-0590. ; 2:2, s. 144-158
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Tidskriftsartikel (refereegranskat)abstract
- Somatic mutations in blood indicative of clonal hematopoiesis of indeterminate potential (CHIP) are associated with an increased risk of hematologic malignancy, coronary artery disease and all-cause mortality. Here we analyze the relation between CHIP status and incident peripheral artery disease (PAD) and atherosclerosis, using whole-exome sequencing and clinical data from the UK Biobank and the Mass General Brigham Biobank. CHIP associated with incident PAD and atherosclerotic disease across multiple beds, with increased risk among individuals with CHIP driven by mutation in DNA damage repair (DDR) genes, such as TP53 and PPM1D. To model the effects of DDR-induced CHIP on atherosclerosis, we used a competitive bone marrow transplantation strategy and generated atherosclerosis-prone Ldlr −/− chimeric mice carrying 20% p53-deficient hematopoietic cells. The chimeric mice were analyzed 13 weeks after grafting and showed increased aortic plaque size and accumulation of macrophages within the plaque, driven by increased proliferation of p53-deficient plaque macrophages. In summary, our findings highlight the role of CHIP as a broad driver of atherosclerosis across the entire arterial system beyond the coronary arteries and provide genetic and experimental support for a direct causal contribution of TP53-mutant CHIP to atherosclerosis.
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