Modeling hospital discharge policies for patients with pneumonia-related sepsis

Kreke, Jennifer E.; Bailey, Matthew D.; Schaefer, Andrew J.; Angus, Derek C.; Roberts, Mark S.

doi:10.1080/07408170802165872

Cited by 14 publications

(13 citation statements)

References 33 publications

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“…Analytic methods for monitoring chronic conditions are also primarily focused on single chronic conditions [3]. For instance Markov Decision Processes (MDP) have been used to guide disease manage for conditions including, heart disease [4], Kidney and liver transplant [5], HIV [6], hepatitis C [7], breast cancer [8], diabetes and hyperlipidemia [9, 10], pneumonia [11, 12], and others [13, 14]. However, when the number of (disease) states increases, as happens frequently in chronic disease management, MDP methods become infeasible.…”

Section: Introductionmentioning

confidence: 99%

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions

Alaeddini¹,

Faruqui²,

Pugh³

2017

Methods Inf Med

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These findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.

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Section: Introductionmentioning

confidence: 99%

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions

Alaeddini¹,

Faruqui²,

Pugh³

2017

Methods Inf Med

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“…Kreke et al . () presented a general model to determine when to discharge a patient with a pneumonia‐related sepsis from hospital. Ceglowski et al () provided a discrete‐event simulation model incorporating core patient treatments for operation analysis.…”

Section: Operations Research Applications In Different Service Industmentioning

confidence: 99%

Operations Research (OR) in Service Industries: A Comprehensive Review

Xing

et al. 2013

Syst. Res.

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The share of gross domestic product from the service industry reflects the competitiveness of a nation; the service industry in the USA accounts for around 80% of its gross domestic product, and it has been increasing gradually. Continual innovations and advances in enabling technologies for the service industry are crucial for developed countries to sustain their leading positions in the globalized economy. To clarify future research directions of operations research (OR) in the service industry, the state of art of OR has been examined systematically, the new requirements of OR are identified for its applications in service industries in comparison with those in manufacturing industries, and the limitations of existing methodologies and tools have been discussed. This paper was intended to provide an updated review on how OR has been applied in the service sector in recent years and what directions the study of OR will be carried forward in the near future. Under a proposed research framework, recent OR-related articles were collected from 17 leading OR journals and classified into the five most active sectors, that is, transportation and warehousing, information and communication, human health and social assistance, retails and wholesales, and financial and insurance services. The conclusions on the limitations of existing studies and the demanding ORs in the service have been drawn from our summaries and observations from a comprehensive review in this field.

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“…Similarly, Turgay et al [ 28 ] used a POMDP model to support personalized mammography screening decisions in a model that used six states, three of which were fully observable. Kreke et al created a 2-state POMDP model for pneumonia-related sepsis patients that included only a Cytokine test as an investigative action and incorporated the Sequential Organ Failure Assessment (SOFA) score for the MDP state space definition [ 29 , 30 ]. In all these cases, both the state model and policy were course-grained, the parameter space limited and the training sets were a few dozen patients.…”

Section: Introductionmentioning

confidence: 99%

From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis

Τσουκαλάς¹,

Albertson²,

Tagkopoulos³

2015

JMIR Med Inform

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BackgroundA tantalizing question in medical informatics is how to construct knowledge from heterogeneous datasets, and as an extension, inform clinical decisions. The emergence of large-scale data integration in electronic health records (EHR) presents tremendous opportunities. However, our ability to efficiently extract informed decision support is limited due to the complexity of the clinical states and decision process, missing data and lack of analytical tools to advice based on statistical relationships.ObjectiveDevelopment and assessment of a data-driven method that infers the probability distribution of the current state of patients with sepsis, likely trajectories, optimal actions related to antibiotic administration, prediction of mortality and length-of-stay.MethodsWe present a data-driven, probabilistic framework for clinical decision support in sepsis-related cases. We first define states, actions, observations and rewards based on clinical practice, expert knowledge and data representations in an EHR dataset of 1492 patients. We then use Partially Observable Markov Decision Process (POMDP) model to derive the optimal policy based on individual patient trajectories and we evaluate the performance of the model-derived policies in a separate test set. Policy decisions were focused on the type of antibiotic combinations to administer. Multi-class and discriminative classifiers were used to predict mortality and length of stay.ResultsData-derived antibiotic administration policies led to a favorable patient outcome in 49% of the cases, versus 37% when the alternative policies were followed (P=1.3e-13). Sensitivity analysis on the model parameters and missing data argue for a highly robust decision support tool that withstands parameter variation and data uncertainty. When the optimal policy was followed, 387 patients (25.9%) have 90% of their transitions to better states and 503 patients (33.7%) patients had 90% of their transitions to worse states (P=4.0e-06), while in the non-policy cases, these numbers are 192 (12.9%) and 764 (51.2%) patients (P=4.6e-117), respectively. Furthermore, the percentage of transitions within a trajectory that lead to a better or better/same state are significantly higher by following the policy than for non-policy cases (605 vs 344 patients, P=8.6e-25). Mortality was predicted with an AUC of 0.7 and 0.82 accuracy in the general case and similar performance was obtained for the inference of the length-of-stay (AUC of 0.69 to 0.73 with accuracies from 0.69 to 0.82).ConclusionsA data-driven model was able to suggest favorable actions, predict mortality and length of stay with high accuracy. This work provides a solid basis for a scalable probabilistic clinical decision support framework for sepsis treatment that can be expanded to other clinically relevant states and actions, as well as a data-driven model that can be adopted in other clinical areas with sufficient training data.

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Modeling hospital discharge policies for patients with pneumonia-related sepsis

Cited by 14 publications

References 33 publications

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions

Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions

Operations Research (OR) in Service Industries: A Comprehensive Review

From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis

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