Electronic health records for the diagnosis of rare diseases

Garcelon, Nicolas; Burgun, Anita; Salomon, Rémi; Neuraz, Antoine

doi:10.1016/j.kint.2019.11.037

Cited by 52 publications

(34 citation statements)

References 94 publications

(95 reference statements)

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“…In rare disease research, this is of particular interest due to the scarcity and the spread of the data among the different centers [ 36 ]. Various approaches have been applied in the area of rare diseases, especially in looking for genetic associations [ 37 ] and making correlations between genotype and phenotype [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Andrade‐Campos

Frutos

Cebolla

et al. 2020

Orphanet J Rare Dis

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Background Since enzyme replacement therapy for Gaucher disease (MIM#230800) has become available, both awareness of and the natural history of the disease have changed. However, there remain unmet needs such as the identification of patients at risk of developing bone crisis during therapy and late complications such as cancer or parkinsonism. The Spanish Gaucher Disease Registry has worked since 1993 to compile demographic, clinical, genetic, analytical, imaging and follow-up data from more than 400 patients. The aims of this study were to discover correlations between patients’ characteristics at diagnosis and to identify risk features for the development of late complications; for this a machine learning approach involving correlation networks and decision trees analyses was applied. Results A total of 358 patients, 340 type 1 Gaucher disease and 18 type 3 cases were selected. 18% were splenectomyzed and 39% had advanced bone disease. 81% of cases carried heterozygous genotype. 47% of them were diagnosed before the year 2000. Mean age at diagnosis and therapy were 28 and 31.5 years old (y.o.) respectively. 4% developed monoclonal gammopathy undetermined significance or Parkinson Disease, 6% cancer, and 10% died before this study. Previous splenectomy correlates with the development of skeletal complications and severe bone disease (p = 0.005); serum levels of IgA, delayed age at start therapy (> 9.5 y.o. since diagnosis) also correlates with severe bone disease at diagnosis and with the incidence of bone crisis during therapy. High IgG (> 1750 mg/dL) levels and age over 60 y.o. at diagnosis were found to be related with the development of cancer. When modelling the decision tree, patients with a delayed diagnosis and therapy were the most severe and with higher risk of complications. Conclusions Our work confirms previous observations, highlights the importance of early diagnosis and therapy and identifies new risk features such as high IgA and IgG levels for long-term complications.

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Section: Discussionmentioning

confidence: 99%

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Andrade‐Campos

Frutos

Cebolla

et al. 2020

Orphanet J Rare Dis

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“…Deep learning techniques are increasingly being applied to biomedical data, from image processing to genomic data analysis [75]. Such methods might outperform pathologists' fibrosis scores from histological renal biopsy images [76]. Well-known techniques include the convolutional neural network (CNN), fully connected neural network, generative adversarial network (GAN), deep reinforcement learning, and recurrent neural network (RNN) [17,77], shown in Figure 1B.…”

Section: Using Electronic Health Record Data In Nephrologymentioning

confidence: 99%

“…AI-based clinical decision support systems (CDSS) can be implemented employing the expert system strategy, data-driven approach, or an ensemble approach by coupling both. An expert system consolidates a knowledge base containing a set of rules for specific clinical scenarios, and the initial rule set may be acquired from domain experts or learned from data through machine learning algorithms [72,[78][79][80] AI has recently been adopted for the prediction, diagnosis, and treatment of kidney diseases [76,[81][82][83][84][85], as shown in Table 2. For example, a prediction model based on the combination of a machine learning algorithm and survival analysis has recently developed and can stratify risk for kidney disease progression among patients with IgA Nephropathy [86].…”

Section: Using Electronic Health Record Data In Nephrologymentioning

confidence: 99%

“…For AKI research, Tomasev et al [83] recently used deep-learning methods to make a continuous prediction of AKI by developing a RNN model on the sequential health record data of >700,000 veterans, allowing physicians to practise with adequate data and sufficient time. In addition, regarding utilization of ANN and CNN methods, Kolachalama et al [76] recently provided a perspicacity into the association of pathological fibrosis identified from histologic images with clinical phenotypes for patients with CKD, helping the diagnostics and prognostics of these phenotypes. Subsequently, there has been an increasing number of AI studies, with great emphasis on the usage of nephrology and transplantation [85,[87][88][89].…”

Section: Using Electronic Health Record Data In Nephrologymentioning

confidence: 99%

“…AI has recently been adopted for the prediction, diagnosis, and treatment of kidney diseases [76,[81][82][83][84][85], as shown in Table 2. For example, a prediction model based on the combination of a machine learning algorithm and survival analysis has recently developed and can stratify risk for kidney disease progression among patients with IgA Nephropathy [86].…”

Section: Using Electronic Health Record Data In Nephrologymentioning

confidence: 99%

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Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

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Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

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Towards values‐based healthcare for inherited metabolic disorders: An overview of current practices for persons with liver glycogen storage disease and fatty acid oxidation disorders

Venema

Peeks

Rossi

et al. 2022

J of Inher Metab Disea

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Value-based healthcare (VBHC) intends to achieve better outcomes for patients, to improve quality of patient care, with reduced costs. Four dimensions define a model of intimately related value-pillars: personal value, allocative value, technical value, and societal value. VBHC is mostly applied in common diseases, and there are fundamental challenges in applying VBHC strategies to low volume, high complex healthcare situations, such as rare diseases, including inherited metabolic disorders. This article summarizes current practices at various academical domains (i.e., research, healthcare, education, and training) that (aim to) increase values at various value-pillars for persons with liver glycogen storage diseases or fatty acid oxidation disorders and their families. Future perspectives may include facilitating virtual networks to function as integrated practice units, improving measurement of outcomes, and creating information technology platforms to overcome the ethical, legal, societal, and technical challenges of data sharing for healthcare and research purposes.

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Electronic health records for the diagnosis of rare diseases

Cited by 52 publications

References 94 publications

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Towards values‐based healthcare for inherited metabolic disorders: An overview of current practices for persons with liver glycogen storage disease and fatty acid oxidation disorders

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