Estimating Risk-Adjusted Hospital Performance

Weenen, Eva van; Feuerriegel, Stefan

doi:10.1109/bigdata50022.2020.9378441

Cited by 5 publications

(9 citation statements)

References 30 publications

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“…Patients in hospitals, in particular, frequently have several comorbidities (e.g., every second hospitalization contains eleven or more separate disease codes [71]). Comorbidities are also of direct relevance for clinical decision-making due to their prevalence.…”

Section: Related Work 21 Comorbiditiesmentioning

confidence: 99%

“…Other studies [e.g., 11,27,59,66,83], including readmission risk [71], employ data on comorbidities as input when generating inferences about patient outcomes [e.g., 11,27,59,66,83]. This helps researchers to account for the fact that the co-occurrence of diseases frequently leads to nonlinear relationships with patient outcomes.…”

Section: Statistical Inferences Using Comorbidity Datamentioning

confidence: 99%

“…Comorbidities are defined in medicine as the occurrence of many disorders that impact a patient's health at the same time [70]. According to recent estimates, 50 percent of hospitalizations in the United States involve individuals with eleven or more distinct disease codes [71]. Similarly, a high proportion of older people suffer from various diseases, especially chronic ones, at the same time [5,78].…”

Section: Introductionmentioning

confidence: 99%

“…This inquiry, however, is about unsupervised learning (e.g., clustering) and does not look at the diseases' longitudinal associations/interactions. Another body of research looks at the effect of comorbidities on patient health [e.g., 11,27,51,59,66] or readmission risk [71]. However, comorbidities are only employed as independent variables in these studies, not as dependent variables, or outcomes of interest.…”

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: Understanding of Comorbidities Using Modeling Techniques on EHR

Badki

2021

Preprint

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Comorbidities refer to the existence of numerous, co-occurring diseases in medicine. The course of one comorbidity is typically extremely dependent on the course of the other condition due to their co-occurrence, and therapies can have major spill-over effects. Despite the high occurrence of comorbidities among patients, there is no complete statistical framework for modelling comorbidity longitudinal dynamics. We propose a probabilistic approach for studying comorbidity dynamics in patients over time in this paper. This approach is a non-homogenous transition technique/mechanism using Hidden Markov Model called as coupled-HMM. Clinical research influenced the design of our coupled-HMM: (1) It accounts for different disease stages (acute, stable) in disease progression by providing clinically meaningful latent phases. (2) It simulates a relationship between the trajectories of comorbidities and the dynamics of capturing co-evolution. (3) The transition mechanism takes into account between-patient heterogeneity (e.g., risk factors, treatments). Based on 675 health trajectories, we assessed our proposed Coupled-HMM, which investigates the concomitant evolution of diabetes mellitus and chronic liver disease. We find that our Coupled-HMM provides a superior fit when compared to competing models without coupling. We also assess the spill-over impact, or the amount to which diabetic therapies are linked to a shift in chronic liver disease from an acute to a stable condition. Immediate application in treatment planning and clinical research becomes possible as a result of our approach in context of comorbidities.

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Section: Related Work 21 Comorbiditiesmentioning

confidence: 99%

Section: Statistical Inferences Using Comorbidity Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

WITHDRAWN: Understanding of Comorbidities Using Modeling Techniques on EHR

Badki

2021

Preprint

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“…In medicine, comorbidities refer to the simultaneous occurrence of several diseases affecting patient health [70]. Recent estimates suggest that 50 % of hospitalizations in the U.S. involve patients where eleven and more different disease codes are present [71]. Similarly, multiple diseases and, especially, chronic ones simultaneously affect the health of a large proportion of elderly patients [5,78].…”

Section: Introductionmentioning

confidence: 99%

Modeling longitudinal dynamics of comorbidities

Maag

Feuerriegel

Kraus

et al. 2021

Proceedings of the Conference on Health, Inference, and Learning

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In medicine, comorbidities refer to the presence of multiple, cooccurring diseases. Due to their co-occurring nature, the course of one comorbidity is often highly dependent on the course of the other disease and, hence, treatments can have significant spill-over effects. Despite the prevalence of comorbidities among patients, a comprehensive statistical framework for modeling the longitudinal dynamics of comorbidities is missing. In this paper, we propose a probabilistic model for analyzing comorbidity dynamics over time in patients. Specifically, we develop a coupled hidden Markov model with a personalized, non-homogeneous transition mechanism, named Comorbidity-HMM. The specification of our Comorbidity-HMM is informed by clinical research: (1) It accounts for different disease states (i. e., acute, stable) in the disease progression by introducing latent states that are of clinical meaning. (2) It models a coupling among the trajectories from comorbidities to capture co-evolution dynamics. (3) It considers between-patient heterogeneity (e. g., risk factors, treatments) in the transition mechanism. Based on our model, we define a spill-over effect that measures the indirect effect of treatments on patient trajectories through coupling (i. e., through comorbidity co-evolution). We evaluated our proposed Comorbidity-HMM based on 675 health trajectories where we investigate the joint progression of diabetes mellitus and chronic liver disease. Compared to alternative models without coupling, we find that our Comorbidity-HMM achieves a superior fit. Further, we quantify the spill-over effect, that is, to what extent diabetes treatments are associated with a change in the chronic liver disease from an acute to a stable disease state. To this end, our model is of direct relevance for both treatment planning and clinical research in the context of comorbidities. CCS CONCEPTS• Applied computing → Health informatics; • Mathematics of computing → Bayesian computation; Markov processes.

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