Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes

Mullin, Sarah; Żola, Jarosław; Lee, Robert; Hu, Jinwei; MacKenzie, Brianne; Brickman, Arlen; Anaya, Gabriel; Sinha, Shyamashree; Li, Angie; Elkin, Peter L.

doi:10.1016/j.jbi.2021.103889

Cited by 23 publications

(12 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At each iteration of the algorithm, the two clusters with the lowest Ward linkage are merged into a new cluster [ 47 ]. Several studies have used the AHC [ 48 , 49 ] and k -means [ 50 , 51 ] in clinical applications, providing good clustering outcomes. We evaluate and compare the clustering results of both k -means and the AHC method in this paper.…”

Section: Methodsmentioning

confidence: 99%

Learning and visualizing chronic latent representations using electronic health records

et al. 2022

View full text Add to dashboard Cite

Background Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches. Methods We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient’s health status evolution, which is of paramount importance in the clinical setting. Results To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients. Conclusion Our results highlighted the value of ML techniques to extract clinical knowledge, supporting the identification of patients with certain chronic conditions. Furthermore, the patient’s health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.

show abstract

Section: Methodsmentioning

confidence: 99%

Learning and visualizing chronic latent representations using electronic health records

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The most common phenotypes are summarized in Figure 3 and include chronic conditions as well as adverse drug events. Social determinants of health such as marital status and homelessness were considered in 7 of the 106 articles [25][26][27][28][29][30][31], while more nuanced phenotypes such as disease severity (n = 5, 4.7%) [32][33][34][35][36] and disease subtypes (n = 19, 17.9%) [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] are emerging areas of focus. We also classified each phenotype as either binary (eg.…”

Section: Phenotypesmentioning

confidence: 99%

“…A deep autoencoder was used to discover depression and suicidal ideation patterns [47]. Discovering subphenotypes [39,43,46,[48][49][50] and clusters of disease trajectory patterns [41,45,47,53,54] are the main focus of unsupervised models. For example, non-negative tensor factorization was used to discover 14 subphenotypes of cardiovascular disease (CVD) patients and their association with conventional CVD risk scores and subsequent myocardial infarction [44].…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…In contrast to the previously discussed ML approaches, unsupervised learning is used for phenotype discovery, including identification of subphenotypes, [39,74,[120][121][122][123][124][125][126][127][128] co-occurring conditions, [69,129] and disease progression patterns. [68,[130][131][132][133][134] Among the 19 articles utilizing unsupervised learning, Latent Dirichlet Allocation (LDA) [69,124,125,127,133] and K-means were the most frequently used methods.…”

Section: Unsupervised Learningmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

Yang

Varghese²,

Stephenson

et al. 2022

Preprint

View full text Add to dashboard Cite

ObjectiveAccurate and rapid methods for phenotyping are a prerequisite to realizing the potential of electronic health records (EHRs) data for clinical and translational research. This study reviews the literature on machine learning (ML) approaches for phenotyping with respect to the phenotypes considered, the data sources and methods used, and the contributions within the wider context of EHR-based research.Materials and MethodsWe searched for relevant articles in PubMed and Web of Science published between January 1, 2018 and April 14, 2022. After screening, we collected data on 52 variables across 106 selected articles.ResultsML-based methods were developed for 156 unique phenotypes, primarily using EHR data from a single institution or health system. 72 of 106 articles leveraged unstructured data in clinical notes. In terms of methodology, supervised learning is the most prevalent ML paradigm (n = 64, 60.4%), with half of the articles employing deep learning. Semi-supervised and weakly-supervised approaches were applied to reduce the burden of obtaining gold-standard labeled data (n = 21, 19.8%), while unsupervised learning was used for phenotype discovery (n = 20, 18.9%). Federated learning has been applied to develop algorithms across multiple institutions while preserving data privacy (n = 2, 1.9%).DiscussionWhile the use of ML for phenotyping is growing, most articles applied traditional supervised ML to characterize the presence of common, chronic conditions.ConclusionContinued research in ML-based methods is warranted, with particular attention to the development of advanced methods for complex phenotypes and standards for reporting and evaluating phenotyping algorithms.

show abstract

“…Several recent publications in RMDs reflect the goals of precision medicine, which can be understood as the provision of the right treatment 32 , at the right dose 33 , to the right person, at the right time 34 , while minimizing unnecessary testing ,side effects and overuse issues, including opioid use and abuse [35][36][37] , specifically opioid use around TJR [38][39][40] , and to explore issues of inequity in classification 41 .…”

Section: Ai/ml For Precision Medicine: Using Data To Guide Therapy An...mentioning

confidence: 99%

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

Nelson

Arbeeva

2022

J Rheumatol

View full text Add to dashboard Cite

There has been rapid growth in the use of artificial intelligence analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers given the "black box" nature of most algorithms and the unfamiliarity of the terms and lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.

show abstract

Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes

Cited by 23 publications

References 45 publications

Learning and visualizing chronic latent representations using electronic health records

Learning and visualizing chronic latent representations using electronic health records

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions

Contact Info

Product

Resources

About