Early detection of Parkinson’s disease through enriching the electronic health record using a biomedical knowledge graph

Soman, Karthik; Nelson, Charlotte; Cerono, Gabriel; Goldman, Samuel; Baranzini, Sergio E.; Brown, Ethan G.

doi:10.3389/fmed.2023.1081087

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…The PSEVs improved prediction of multiple sclerosis (MS) for 5,752 patients 3 years before diagnosis (AUC = 0.83 vs. AUC = 0.60 using only EHRs) and provided insight into the biological drivers of MS. The same SPOKE KG was used for the early detection of Parkinson's disease (86) with AUC accuracies of 0.77, 0.74, and 0.72 for 1, 3, and 5 years before diagnosis, respectively, and accuracies of 0.74, 0.70, and 0.66 in a validation cohort. These were all higher at each time point than when only EHRs were used (0.67, 0.63, and 0.56 at 1, 3, and 5 years, respectively).…”

Section: Open-source Knowledge Graphsmentioning

confidence: 99%

Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs

Woodman,

Koczwara,

Mangoni

2024

Front. Med.

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The current management of patients with multimorbidity is suboptimal, with either a single-disease approach to care or treatment guideline adaptations that result in poor adherence due to their complexity. Although this has resulted in calls for more holistic and personalized approaches to prescribing, progress toward these goals has remained slow. With the rapid advancement of machine learning (ML) methods, promising approaches now also exist to accelerate the advance of precision medicine in multimorbidity. These include analyzing disease comorbidity networks, using knowledge graphs that integrate knowledge from different medical domains, and applying network analysis and graph ML. Multimorbidity disease networks have been used to improve disease diagnosis, treatment recommendations, and patient prognosis. Knowledge graphs that combine different medical entities connected by multiple relationship types integrate data from different sources, allowing for complex interactions and creating a continuous flow of information. Network analysis and graph ML can then extract the topology and structure of networks and reveal hidden properties, including disease phenotypes, network hubs, and pathways; predict drugs for repurposing; and determine safe and more holistic treatments. In this article, we describe the basic concepts of creating bipartite and unipartite disease and patient networks and review the use of knowledge graphs, graph algorithms, graph embedding methods, and graph ML within the context of multimorbidity. Specifically, we provide an overview of the application of graph theory for studying multimorbidity, the methods employed to extract knowledge from graphs, and examples of the application of disease networks for determining the structure and pathways of multimorbidity, identifying disease phenotypes, predicting health outcomes, and selecting safe and effective treatments. In today’s modern data-hungry, ML-focused world, such network-based techniques are likely to be at the forefront of developing robust clinical decision support tools for safer and more holistic approaches to treating older patients with multimorbidity.

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Section: Open-source Knowledge Graphsmentioning

confidence: 99%

Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs

Woodman,

Koczwara,

Mangoni

2024

Front. Med.

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“…The same SPOKE KG was used for the early detection of Parkinson's disease [ 133 ]. In a random forest classifier, AUC accuracies of 0.77, 0.74, and 0.72 were obtained at one, three, and five years before diagnosis respectively, and accuracies of 0.74, 0.70 and 0.66 in a validation cohort.…”

Section: Graph Machine Learningmentioning

confidence: 99%

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Woodman,

Mangoni

2023

Aging Clin Exp Res

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The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

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“… 32 In their models, variables which could change over time were binned according to their most recent value, though there is research supporting using trajectories of patient vitals for prediction of AD. 33 Electronic medical record data augmented with knowledge graphs have also shown success in early detection of PD and MS. 34 , 35 More research is needed for the use of EMR data and ML for early detection of ALS, but the disease has been characterized in a large cohort of military Veterans’ EMR data. 36 Further, there are examples of EMR-based models for treatment and prognosis of these diseases, not all of which are included here.…”

Section: Introductionmentioning

confidence: 99%

Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records

Felix,

Johnston,

Owen

et al. 2024

DIGITAL HEALTH

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Objective This study assesses the application of interpretable machine learning modeling using electronic medical record data for the prediction of conversion to neurological disease. Methods A retrospective dataset of Cleveland Clinic patients diagnosed with Alzheimer's disease, amyotrophic lateral sclerosis, multiple sclerosis, or Parkinson's disease, and matched controls based on age, sex, race, and ethnicity was compiled. Individualized risk prediction models were created using eXtreme Gradient Boosting for each neurological disease at four timepoints in patient history. The prediction models were assessed for transparency and fairness. Results At timepoints 0-months, 12-months, 24-months, and 60-months prior to diagnosis, Alzheimer’s disease models achieved the area under the receiver operating characteristic curve on a holdout test dataset of 0.794, 0.742, 0.709, and 0.645; amyotrophic lateral sclerosis of 0.883, 0.710, 0.658, and 0.620; multiple sclerosis of 0.922, 0.877, 0.849, and 0.781; and Parkinson’s disease of 0.809, 0.738, 0.700, and 0.651, respectively. Conclusions The results demonstrate that electronic medical records contain latent information that can be used for risk stratification for neurological disorders. In particular, patient-reported outcomes, sleep assessments, falls data, additional disease diagnoses, and longitudinal changes in patient health, such as weight change, are important predictors.

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Early detection of Parkinson’s disease through enriching the electronic health record using a biomedical knowledge graph

Cited by 4 publications

References 41 publications

Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs

Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records

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