A Perspective: Use of Machine Learning Models to Predict the Risk of Multimorbidity

Delanerolle, Gayathri; Cl, Hadley; Midtrevold, M; Hme, Edvardsen; Molden, Espen; Shafiei, Marzieh; Jacobsen, Dennis Helding

doi:10.32474/lojms.2021.05.000225

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…This undercurrent of disease complexities linked to endometriosis that could lead to multimorbidity should be explored to support clinicians and healthcare organisations in future-proofing patient care [5]. In line with this, exploring machine learning as a technique in conjunction with synthetic data methods could demonstrate better predictions and offer a new solution to sample size challenges.…”

Section: Introductionmentioning

confidence: 98%

An Exploratory Study Assessing Data Synchronising Methods to Develop Machine Learning-Based Prediction Models: Application to Multimorbidity Among Endometriosis Women

Phiri

2024

AJBSR

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Section: Introductionmentioning

confidence: 98%

An Exploratory Study Assessing Data Synchronising Methods to Develop Machine Learning-Based Prediction Models: Application to Multimorbidity Among Endometriosis Women

Phiri

2024

AJBSR

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“…Hence, multimorbidity is challenging to treat, and there remains a paucity of research available to better understand the basic science behind the complex mechanisms that could enable better diagnosis and management long-term [4]. This undercurrent of disease complexities linked to endometriosis that could lead to multimorbidity should be explored to support clinicians and healthcare organisations in futureproofing patient care [5]. In line with this, exploring machine learning as a technique in conjunction with synthetic data methods could demonstrate better predictions and offer a new solution to sample size challenges.…”

Section: Introductionmentioning

confidence: 99%

Exploratory Report on Data Synchronising Methods to Develop Machine Learning-Based Prediction Models to for Multimorbidity

Delanerolle¹,

Benfield²,

Phiri³

et al. 2023

Preprint

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Endometriosis is a complex chronic condition characteristic of chronic pelvic pain, dysmenorrhea, anxiety and fatigue. This can often lead to multimorbidity which is defined by the presence of two or more long term conditions. Delayed diagnosis of endometriosis is a crucial issue that leads to poor quality of life and clinical management. There are a variety of limitations linked to conducting endometriosis research including lack of dedicated funding. Additionally, accessing existing electronic healthcare records can be challenging due to governance and regulatory restrictions. Missing data issues are another concern that has been commonly identified among real-world studies. Considering these challenges, data science technique could provide a solution by way of using synthetic datasets that could be generated using known characteristics of endometriosis to explore the possibility of predicting multimorbidity. This study aimed to develop an exploratory machine learning model that can predict multimorbidity among women with endometriosis using real-world and synthetic data. A sample size of 1012 was used from two endometriosis specialized centres in the UK. In addition, 1000 synthetic data records per centre were generated using the widely used Synthetic Data Vault’s Gaussian Copula model based on patients’ records’ characteristics. Three standard classification models, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), were used for classification. The average accuracies for all three models (LR, SVM and RF), given as “model accuracy-centre1: accuracy-centre2” were found to be: LR 64.26%:69.04%, SVM 67.35%:68.61%, and RF 58.67%:73.76% on real-world data, and LR 69.9%:72.29%, SVM 69.39%:70.13, and RF 68.88%:74.62 on synthetic data, respectively. The findings of this report show machine learning models trained on synthetic data performed better than models trained on real-world data. Our findings suggest synthetic data holds great promise for shows value to conduct clinical epidemiology and clinical trials that could devise better precision treatments and possibly reduce the burden of multimorbidity.

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“…Patients are more vocal about having therapies that are less invasive and more tolerable with minimal side effects. Therefore, precision and personalised medicine has become more appealing to the masses [2]. To develop such befitting research requires high-quality evidence, ideally in shorter timeframes, improved analytical methods and access to existing data within electronic healthcare records (EHRs).…”

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confidence: 99%

Synthetic data & the future of Women’s Health: A synergistic relationship

Delanerolle,

Phiri,

Cavalini

et al. 2023

International Journal of Medical Informatics

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A Perspective: Use of Machine Learning Models to Predict the Risk of Multimorbidity

Cited by 4 publications

References 37 publications

An Exploratory Study Assessing Data Synchronising Methods to Develop Machine Learning-Based Prediction Models: Application to Multimorbidity Among Endometriosis Women

An Exploratory Study Assessing Data Synchronising Methods to Develop Machine Learning-Based Prediction Models: Application to Multimorbidity Among Endometriosis Women

Exploratory Report on Data Synchronising Methods to Develop Machine Learning-Based Prediction Models to for Multimorbidity

Synthetic data & the future of Women’s Health: A synergistic relationship

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