Hospital Length of Stay Prediction Based on Multi-modal Data Towards Trustworthy Human-AI Collaboration in Radiomics

Baniecki, Hubert; Bartlomiej, Sobieski,; Bombiński, Przemysław; Szatkowski, Patryk; Biecek, Przemysław

doi:10.1007/978-3-031-34344-5_9

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Nachit et al (2023) used it to analyze partial dependence plots of different body composition parameters extracted from computer tomography scans in a random survival forest. Additionally, we successfully applied ‘survex’ to explain model bias in predicting hospital length of stay ( Baniecki et al 2023b ).…”

Section: Implementation and Functionalitiesmentioning

confidence: 99%

survex: an R package for explaining machine learning survival models

Spytek,

Krzyziński,

Langbein

et al. 2023

Bioinformatics

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Summary Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications. Availability and implementation survex is available under the GPL3 public license at https://github.com/modeloriented/survex and on CRAN with documentation available at https://modeloriented.github.io/survex.

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Section: Implementation and Functionalitiesmentioning

confidence: 99%

survex: an R package for explaining machine learning survival models

Spytek,

Krzyziński,

Langbein

et al. 2023

Bioinformatics

View full text Add to dashboard Cite

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“…It is considered by many to be crucial for effective hospital administration, as the allocation of the necessary resources for care is among the first priorities of a hospitalization [Stone et al 2022]. Accurate LOS prediction can, thus, improve the quality of care, reduce cost overhead, and help manage administrative burden [Jain et al 2022, Baniecki et al 2023.…”

Section: Introductionmentioning

confidence: 99%

Predicting Inpatient Admissions in Brazilian Hospitals

Consoli,

Viera,

Bordini

et al. 2024

Anais Do XXIV Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2024)

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Patient length-of-stay prediction is a topic of interest for hospital administrators, as it can aid in planning and the allocation of critical resources. Ideal resource allocation can result in better care and reduced costs. Artificial Intelligence solutions have been tested for this purpose using several datasets for both foreign and Brazilian hospitals, but focusing on long-term inpatient care or Intensive Care Unit patient flow. We propose using similar solutions to predict inpatient flow from common patient entry points, such as emergency care or walk-in appointments, in an effort to better understand whether a patient will require outpatient care or inpatient admission as early as possible. Our solution was able to predict inpatient flow with as much as 88% accuracy.

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Hospital Length of Stay Prediction Based on Multi-modal Data Towards Trustworthy Human-AI Collaboration in Radiomics

Cited by 2 publications

References 15 publications

survex: an R package for explaining machine learning survival models

survex: an R package for explaining machine learning survival models

Predicting Inpatient Admissions in Brazilian Hospitals

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