Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

Yeol, Eun; Kim, Jeong Whun; Lee, Young-Min; Cho, Sung Woo; Kim, Heeyoung; Kim, Jae Kyoung

doi:10.1038/s41598-021-84003-4

Cited by 20 publications

(12 citation statements)

References 67 publications

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“…Unsupervised and supervised clustering models were used to cluster 2277 OSA patients into sic phenotypes based on their polysomnogram data. The phenotypes show different risk for the development of cardio-neuro-metabolic comorbidity, unlike the conventional single-metric apnea–hypopnea index-based phenotype [ 61 ].…”

Section: Other Use Cases For Artificial Intelligence and Sleep Medicinementioning

confidence: 99%

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Bandyopadhyay

Goldstein

2022

Sleep Breath

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Background The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. Method The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. Results Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. Conclusion Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

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Section: Other Use Cases For Artificial Intelligence and Sleep Medicinementioning

confidence: 99%

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Bandyopadhyay

Goldstein

2022

Sleep Breath

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“… 71 However, their use is imprecise, and further studies should be conducted using fasting blood glucose measurements in well-defined cohorts recruited for specific studies. While our cluster groups were derived using subjective methods, we plan to validate these phenotypes using external data-centric methods 72 with availability of longer post-COVID records for these patients in TriNetX and N3C.…”

Section: Discussionmentioning

confidence: 99%

Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering

2021

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Objective Hyperglycemia has emerged as an important clinical manifestation of coronavirus disease 2019 (COVID-19) in diabetic and non-diabetic patients. Whether these glycemic changes are specific to a subgroup of patients and persist following COVID-19 resolution remains to be elucidated. This work aimed to characterize longitudinal random blood glucose in a large cohort of non-diabetic patients diagnosed with COVID-19. Materials and Methods De-identified electronic medical records of 7,502 patients diagnosed with COVID-19 without prior diagnosis of diabetes between January 1st and November 18th, 2020 were accessed through the TriNetX Research Network. Glucose measurements, diagnostic codes, medication codes, laboratory values, vital signs, and demographics were extracted before, during, and after COVID-19 diagnosis. Unsupervised time-series clustering algorithms were trained to identify distinct clusters of glucose trajectories. Cluster associations were tested for demographic variables, COVID-19 severity, glucose-altering medications, glucose values, and new-onset diabetes diagnoses. Results Time-series clustering identified a low-complexity model with 3 clusters and a high-complexity model with 19 clusters as the best-performing models. In both models, cluster membership differed significantly by death status, COVID-19 severity, and glucose levels. Clusters membership in the 19 cluster model also differed significantly by age, sex, and new-onset diabetes mellitus. Discussion and Conclusion This work identified distinct longitudinal blood glucose changes associated with subclinical glucose dysfunction in the low-complexity model and increased new-onset-diabetes incidence in the high complexity model. Together, these findings highlight the utility of data-driven techniques to elucidate longitudinal glycemic dysfunction in patients with COVID-19 and provide clinical evidence for further evaluation of the role of COVID-19 in diabetes pathogenesis. Lay Summary Hyperglycemia is defined as elevated blood glucose measurements and are common in diabetic patients. Recent findings suggest that patients diagnosed with COVID-19 may experience elevated blood glucose levels during COVID-19 infection. Whether blood glucose levels remain elevated after COVID-19 infection remains poorly understood. This study aimed to identify how patterns of blood glucose levels before, during, and after COVID-19 change. This work analyzed blood glucose levels from 2,059 patients diagnosed with COVID-19 and used machine learning to identify different patterns of blood glucose changes. We found patterns demonstrating an overall increase, overall decrease, temporal increase, and temporary decrease in blood glucose levels. Some of these patterns were associated with COVID-19 severity and proportion of patients with new-onset diabetes. These findings demonstrate the usefulness of machine learning in understanding glucose changes following COVID-19 diagnosis and indicate that more research is needed to understand if blood glucose monitoring in COVID-19 patients should be routinely performed.

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“…Some years ago, we were the first to employ, categorical principal component analysis (CATPCA) in combination with cluster analysis in an OSAS population in order to detect comorbidity phenotypes [ 4 ]. Our scope was to identify the interplay between OSAS and its comorbidities, an approach that had served well in phenotyping in other areas and that also gained popularity after our study in the OSAS field [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Obstructive Sleep Apnea Syndrome Comorbidity Phenotypes in Primary Health Care Patients in Northern Greece

et al. 2022

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Background: Obstructive sleep apnea syndrome (OSAS) is a significant public health issue. In the general population, the prevalence varies from 10% to 50%. We aimed to phenotype comorbidities in OSAS patients referred to the primary health care (PHC) system. Methods: We enrolled 1496 patients referred to the PHC system for any respiratory- or sleep-related issue from November 2015 to September 2017. Some patients underwent polysomnography (PSG) evaluation in order to establish OSAS diagnosis. The final study population comprised 136 patients, and the Charlson comorbidity index was assessed. Categorical principal component analysis and TwoStep clustering was used to identify distinct clusters in the study population. Results: The analysis revealed three clusters: the first with moderate OSAS, obesity and a high ESS score without significant comorbidities; the second with severe OSAS, severe obesity with comorbidities and the highest ESS score; and the third with severe OSAS and obesity without comorbidities but with a high ESS score. The clusters differed in age (p < 0.005), apnea–hypopnea index, oxygen desaturation index, arousal index and respiratory and desaturation arousal index (p < 0.001). Conclusions: Predictive comorbidity models may aid the early diagnosis of patients at risk in the context of PHC and pave the way for personalized treatment.

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Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

Cited by 20 publications

References 67 publications

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering

Obstructive Sleep Apnea Syndrome Comorbidity Phenotypes in Primary Health Care Patients in Northern Greece

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