Using Machine Learning to Predict Hospital Disposition With Geriatric Emergency Department Innovation Intervention

Bunney, Gabrielle; Tran, Steven; Han, Sae Young; Gu, Carol; Wang, Hanyin; Luo, Yunbo; Dresden, Scott M.

doi:10.1016/j.annemergmed.2022.07.026

Cited by 8 publications

(10 citation statements)

References 26 publications

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“…The overall model’s AUROC was approximately 0.97, and the individual AUROCs for predicting admission, discharge, or expiration were also 0.94 or higher. These results surpass the findings of previous studies that predicted ED patient disposition using structured and unstructured data [ 14 , 15 , 17 , 21 ], some of which [ 15 , 17 ] incorporated laboratory data not included as part of this study.…”

Section: Discussioncontrasting

confidence: 54%

“…The crucial unstructured features include: pneumonia, fracture, failure, suspect, sepsis, mellitus, kidney, left, right, and bleeding. In the context of structured features, previous studies [ 14 , 15 , 22 , 25 ] also found that age, pulse rate, temperature, systolic blood pressure, diastolic blood pressure, and emergency severity level are all important predictors of ED Disposition.…”

Section: Discussionmentioning

confidence: 97%

“…Further, the outcomes considered in this study encompass admission, discharge, and expiration, constituting a multi-class classification problem. In prior research that focused on unstructured data, the emphasis was primarily on binary-class classification problems [ 13 , 14 , 27 , 28 ]. In clinical practice, if the goal is to predict various ED disposition outcomes, it might necessitate the use of distinct predictive models.…”

Section: Discussionmentioning

confidence: 99%

“…The features used in this study were recommended by an ED physician (> 10 years of clinical experience, possessing a Master’s degree) and determination made based on relevant literature [ 14 , 15 , 20 , 22 , 25 ] ( see Table 1 ). The features were categorized into three types: continuous, categorical, and text variables.…”

Section: Methodsmentioning

confidence: 99%

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An ensemble model for predicting dispositions of emergency department patients

Kuo,

Lin,

Chang

et al. 2024

BMC Med Inform Decis Mak

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Objective The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning’s potential to predict ED dispositions, thus leading to promising substantial benefits. This study’s objective is to create a predictive model for ED patient dispositions by employing ensemble learning. It harnesses diverse data types, including structured and unstructured information gathered during ED visits to address the evolving needs of localized healthcare systems. Methods In this cross-sectional study, 80,073 ED patient records were amassed from a major southern Taiwan hospital in 2018–2019. An ensemble model incorporated structured (demographics, vital signs) and pre-processed unstructured data (chief complaints, preliminary diagnoses) using bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF). Two random forest base-learners for structured and unstructured data were employed and then complemented by a multi-layer perceptron meta-learner. Results The ensemble model demonstrates strong predictive performance for ED dispositions, achieving an area under the receiver operating characteristic curve of 0.94. The models based on unstructured data encoded with BOW and TF-IDF yield similar performance results. Among the structured features, the top five most crucial factors are age, pulse rate, systolic blood pressure, temperature, and acuity level. In contrast, the top five most important unstructured features are pneumonia, fracture, failure, suspect, and sepsis. Conclusions Findings indicate that utilizing ensemble learning with a blend of structured and unstructured data proves to be a predictive method for determining ED dispositions.

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Section: Discussioncontrasting

confidence: 54%

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An ensemble model for predicting dispositions of emergency department patients

Kuo,

Lin,

Chang

et al. 2024

BMC Med Inform Decis Mak

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“…Other studies have explored strategies to address these challenges, such as the use of Lean principles [14][15][16], the implementation of electronic health records to enhance communication and coordination [17,18], and various other methods [19]. Recent advancements in the feld of analytical techniques, such as machine learning, have been utilized to predict patient outcomes [20][21][22][23], including hospital readmissions [24] and mortality rates [25], for emergency patients in hospital. Despite such eforts to improve emergency patient fow in hospitals, challenges persist, and more research is needed to fnd efective solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision-Making Based on Predictive Process Monitoring of Patient Treatment Processes: A Case Study of Emergency Patients

Aravazhi,

Helgheim,

Aadahl

2023

Advances in Operations Research

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This paper investigates predictive process monitoring problems in emergency treatment by combining the fields of process management and artificial intelligence. The objective is to predict the next activity and its timestamp in the treatment of emergency patients who have undergone surgery at the gastroenterology or urology surgery units in a hospital in Norway. To achieve this goal, three models were developed using different algorithms, and the best performing model was identified using various performance metrics. The results demonstrate the potential of predictive process monitoring to accurately forecast the outcome of patient treatments. By leveraging the insights gained from predictive process monitoring, hospitals can make more informed decisions. The findings of this study suggest that predictive process monitoring holds significant promise as a tool for improving the efficiency and effectiveness of emergency patient treatment processes. This research has significant implications for the field of decision sciences, particularly regarding resource allocation, reducing waiting times, and improving patient outcomes. The ability to predict the outcomes of patient treatment processes has important implications for hospitals, allowing the streamlining and acceleration of the treatment process. Overall, this study provides a promising framework for predicting patient treatment processes by using the predictive process monitoring method. This could be expanded upon in future research, ultimately leading to improved patient outcomes and better decision-making in healthcare.

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Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system

Luo,

Mao,

Sanchez‐Pinto

et al. 2024

Learning Health Systems

Self Cite

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IntroductionThe rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.MethodsWe have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk‐natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross‐pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.ResultsOur work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer‐reviewed publications. In 2022, our cross‐discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.ConclusionsOur Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution‐level learning health system.

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Using Machine Learning to Predict Hospital Disposition With Geriatric Emergency Department Innovation Intervention

Cited by 8 publications

References 26 publications

An ensemble model for predicting dispositions of emergency department patients

An ensemble model for predicting dispositions of emergency department patients

Decision-Making Based on Predictive Process Monitoring of Patient Treatment Processes: A Case Study of Emergency Patients

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system

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