Opportunities for machine learning to transform care for people with cystic fibrosis

Abroshan, Mahed; Alaa, Ahmed M.; Rayner, Oli; Schaar, Mihaela van der

doi:10.1016/j.jcf.2020.01.002

Cited by 7 publications

(5 citation statements)

References 19 publications

(23 reference statements)

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“…Artificial intelligence (AI) has several potential future applications ( 29 ). Virtual monitoring has the potential to enrich data available for machine learning using not only more granular patient generated metrics (portable spirometry, blood sugar readings, etc.…”

Section: Discussionmentioning

confidence: 99%

Virtual monitoring in CF – the importance of continuous monitoring in a multi-organ chronic condition

Vagg

Deasy

Chapman

et al. 2023

Front. Digit. Health

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Cystic Fibrosis (CF) is a chronic life-limiting condition that affects multiple organs within the body. Patients must adhere to strict medication regimens, physiotherapy, diet, and attend regular clinic appointments to manage their condition effectively. This necessary but burdensome requirement has prompted investigations into how different digital health technologies can enhance current care by providing the opportunity to virtually monitor patients. This review explores how virtual monitoring has been harnessed for assessment or performance of physiotherapy/exercise, diet/nutrition, symptom monitoring, medication adherence, and wellbeing/mental-health in people with CF. This review will also briefly discuss the potential future of CF virtual monitoring and some common barriers to its current adoption and implementation within CF. Due to the multifaceted nature of CF, it is anticipated that this review will be relevant to not only the CF community, but also those investigating and developing digital health solutions for the management of other chronic diseases.

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Section: Discussionmentioning

confidence: 99%

Virtual monitoring in CF – the importance of continuous monitoring in a multi-organ chronic condition

Vagg

Deasy

Chapman

et al. 2023

Front. Digit. Health

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“…The complex trajectory for Cystic Fibrosis patients requires close monitoring of care and high-risk decision making, such as lung transplant referral. Opportunities for individualised patient care using machine learning have recently been put forward for the case of Cystic Fibrosis (CF) patients (Abroshan et al 2020). The authors highlight risk prediction and personalised treatment recommendation, amongst others, as areas where data can be leveraged by machine learning.…”

Section: Methodsmentioning

confidence: 99%

Synthesising Electronic Health Records: Cystic Fibrosis Patient Group

Muller¹,

Xu²,

Hayes³

2022

Preprint

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Class imbalance can often degrade predictive performance of supervised learning algorithms. Balanced classes can be obtained by oversampling exact copies, with noise, or interpolation between nearest neighbours (as in traditional SMOTE methods). Oversampling using augmentation, as is typical in computer vision tasks, can be achieved with deep generative models. Deep generative models are effective data synthesisers due to their ability to capture complex underlying distributions. Synthetic data in healthcare can enhance interoperability between healthcare providers by ensuring patient privacy. Equipped with large synthetic datasets which do well to represent small patient groups, machine learning in healthcare can address the current challenges of bias and generalisability. This paper evaluates synthetic data generators ability to synthesise patient electronic health records. We test the utility of synthetic data for patient outcome classification, observing increased predictive performance when augmenting imbalanced datasets with synthetic data.

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“…36 In addition, machine learning can be used to model disease trajectories and detect signals for deteriorations in chronic diseases such as cystic fibrosis (CF). 37,38 A study among 147 participants with CF showed that data from home monitoring (weight, lung function, oximetry) and wearable (pulse rate, activity) technologies could be utilized to predict acute pulmonary exacerbations, with clustering analysis showing distinct profiles for different types of exacerbations. 37,38 This highlights the role of AI in predicting disease trajectories, anticipating future healthcare needs and allowing bespoke personalized interventions at an earlier stage.…”

Section: Diagnosis and Prognosticationmentioning

confidence: 99%

“…The model was able to accurately predict deterioration at 12 h, with slightly worse performance at 48 h. This model was reported to outperform standard early warning systems, identifying more than 40% of cardiac arrests or unplanned intensive care admissions within the preceding 48 h and provide as much as 12 h notice for more than 25% 36 . In addition, machine learning can be used to model disease trajectories and detect signals for deteriorations in chronic diseases such as cystic fibrosis (CF) 37,38 . A study among 147 participants with CF showed that data from home monitoring (weight, lung function, oximetry) and wearable (pulse rate, activity) technologies could be utilized to predict acute pulmonary exacerbations, with clustering analysis showing distinct profiles for different types of exacerbations 37,38 .…”

Section: How Can Ai Be Used In Clinical Pharmacology?mentioning

confidence: 99%

Artificial intelligence and machine learning for clinical pharmacology

Ryan,

Maclean,

Balston

et al. 2023

Brit J Clinical Pharma

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Artificial intelligence (AI) will impact many aspects of clinical pharmacology including drug discovery and development, clinical trials, personalised medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation into clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.

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Opportunities for machine learning to transform care for people with cystic fibrosis

Cited by 7 publications

References 19 publications

Virtual monitoring in CF – the importance of continuous monitoring in a multi-organ chronic condition

Virtual monitoring in CF – the importance of continuous monitoring in a multi-organ chronic condition

Synthesising Electronic Health Records: Cystic Fibrosis Patient Group

Artificial intelligence and machine learning for clinical pharmacology

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