Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

Lu, Haohui; Uddin, Shahadat

doi:10.3390/healthcare11071031

Cited by 17 publications

(10 citation statements)

References 71 publications

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“…Graph neural networks (GNNs) are fully end-to-end graph embedding and prediction models using DL methods on graph-based data [ 123 ]. They have been used for assistance with clinical diagnosis of diabetes [ 124 ], disease prediction [ 125 ], disease prediction in patients in the intensive care unit (MIMIC-III dataset) [ 126 ], and a disease-diagnosis service for online users [ 127 ].…”

Section: Graph Machine Learningmentioning

confidence: 99%

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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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Section: Graph Machine Learningmentioning

confidence: 99%

“…Their weaknesses include high complexity, scaling difficulties, and low interpretability and explainability. In an overview of Graph ML techniques for disease prediction, GNN-based models were found to have superior performance compared to traditional ML techniques [ 123 ].…”

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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“…A recent review describes in details the role of the variety of graph-based machine learning methods for disease prediction [42], including the ones based on kernel functions and support vector machine that was used in our strategy. They pointed as a future direction strategies to create an effective graph as a potential research direction, by allowing the automatic simulation of samples networks with distinct weights and attributes we move a step towards attending this research direction.…”

Section: Performance Evaluation Of the Network-based Strategymentioning

confidence: 99%

Multi-task analysis of gene expression data on cancer public datasets

Martins

2023

Preprint

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BackgroundThere is an availability of omics and often multi-omics cancer datasets on public databases such as Gene Expression Omnibus (GEO), International Cancer Genome Consortium and The Cancer Genome Atlas Program. Most of these databases provide at least the gene expression data for the samples contained in the project. Multi-omics has been an advantageous strategy to leverage personalized medicine, but few works explore strategies to extract knowledge relying only on gene expression level for decisions on tasks such as disease outcome prediction and drug response simulation. The models and information acquired on projects based only on expression data could provide decision making background for future projects that have other level of omics data such as DNA methylation or miRNAs.ResultsWe extended previous methodologies to predict disease outcome from the combination of protein interaction networks and gene expression profiling by proposing an automated pipeline to perform the graph feature encoding and further patient networks outcome classification derived from RNA-Seq. We integrated biological networks from protein interactions and gene expression profiling to assess patient specificity combining the treatment/control ratio with the patient normalized counts of the deferentially expressed genes. We also tackled the disease outcome prediction from the gene set enrichment perspective, combining gene expression with pathway gene sets information as features source for this task. We also explored the drug response outcome perspective of the cancer disease still evaluating the relationship among gene expression profiling with single sample gene set enrichment analysis (ssGSEA), proposing a workflow to perform drug response screening according to the patient enriched pathways.ConclusionWe showed the importance of the patient network modeling for the clinical task of disease outcome prediction using graph kernel matrices strategy and showed how ssGSEA improved the prediction only using transcriptomic data combined with pathway scores. We also demonstrated a detailed screening analysis showing the impact of pathway-based gene sets and normalization types for the drug response simulation. We deployed two fully automatized Screening workflows following the FAIR principles for the disease outcome prediction and drug response simulation tasks.AvailabilityThe ScreenDOP code is available athttps://github.com/yascoma/screendopwhile the DReCaS is available athttps://github.com/YasCoMa/caliscoma_pipeline/

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“…For predicting the readmission, node clasification is done to predict if the patient (represented as node) will be readmitted. Readers can refer to Lu and Uddin [2023] for comprehensive review on GNN approaches for disease prediction. Additionally, another stream of applications use GNNs along with knowledge graph to perform knowledge-driven predictions.…”

Section: Related Workmentioning

confidence: 99%

Graph-Based Patient Representation for Multimodal Clinical Data: Addressing Data Heterogeneity

Ghanvatkar,

Rajan

2023

Preprint

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Patient data comprises of different modalities like clinical notes, lab results, and radiological investigations. Predictive modeling on this patient data is challenging due to the heterogeneity among patients and modalities captured, such as no ECG recordings for a few patients. Ensuring model applicability to all admitted patients requires addressing three key factors: i) handling modalities at disparate time-scales (e.g., once in a few hours medication and 125 Hz ECG need to be handled by same model), ii) handling missing modality (e.g., ECG not recorded), and iii) modeling temporal interactions between modalities over time (e.g., the ECG fluctuation triggered some lab test order). Existing literature often doesn’t simultaneously address these requirements. Therefore, we propose a novel patient representation approach inspired by clinical workflows, representing each patient as a graph. We categorize patient data into two main components: observations and care team actions, allowing cross-modality temporal interaction when actions consider the previous observations. We define observation nodes to capture modality-specific data within the time between two actions, and action nodes capture data of the actions undertaken, with edges to capture the temporal dependencies. To address missing modalities and time-scale disparities, we define node types for different modalities and use modality-specific representation for the nodes; implying a missing modality is equivalent to a missing node type in the graph. Aligned with clinical workflows, this patient-graph representation aims to enhance the practicality of predictive systems for various healthcare tasks, from mortality risk assessment to medication recommendations, thereby improving clinical decision support.

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Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

Cited by 17 publications

References 71 publications

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

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

Multi-task analysis of gene expression data on cancer public datasets

Graph-Based Patient Representation for Multimodal Clinical Data: Addressing Data Heterogeneity

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