Bmc Med Inform Decis
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Bmc Med Inform Decis · Oct 2020
A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare.
There is an increasing interest in clinical prediction tools that can achieve high prediction accuracy and provide explanations of the factors leading to increased risk of adverse outcomes. However, approaches to explaining complex machine learning (ML) models are rarely informed by end-user needs and user evaluations of model interpretability are lacking in the healthcare domain. We used extended revisions of previously-published theoretical frameworks to propose a framework for the design of user-centered displays of explanations. This new framework served as the basis for qualitative inquiries and design review sessions with critical care nurses and physicians that informed the design of a user-centered explanation display for an ML-based prediction tool. ⋯ We proposed a framework for the design of user-centered displays of explanations for ML models. We used the proposed framework to motivate the design of a user-centered display of an explanation for predictions from a PICU in-hospital mortality risk model. Positive feedback from focus group participants provides preliminary support for the use of model-agnostic, instance-level explanations of feature influence as an approach to understand ML model predictions in healthcare and advances the discussion on how to effectively communicate ML model information to healthcare providers.
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Bmc Med Inform Decis · Oct 2020
Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.
Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sepsis in ICUs. ⋯ The machine learning-based models developed in this study had good prediction performance. Amongst them, the GBM model showed the best performance in predicting the risk of in-hospital death. It has the potential to assist physicians in the ICU to perform appropriate clinical interventions for critically ill sepsis patients and thus may help improve the prognoses of sepsis patients in the ICU.
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Bmc Med Inform Decis · Sep 2020
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?
Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors. ⋯ A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.
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With the development of information technology, an increasing number of healthcare professionals are using smartphones and mobile medical applications (apps) in their clinical practice. The objective of this study was to survey the use of smartphone-based medical apps among dentists in China and determine dentists' perceptions of such apps. ⋯ Medical apps were perceived to have a positive impact on clinical practice, education and patient care in dentistry by providing relevant medical information. However, there will still be much room for improvement in the future.
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Bmc Med Inform Decis · Aug 2020
ReviewShared decision making in surgery: a scoping review of patient and surgeon preferences.
Many suggest that shared decision-making (SDM) is the most effective approach to clinical counseling. It is unclear if this applies to surgical decision-making-especially regarding urgent, highly-morbid operations. In this scoping review, we identify articles that address patient and surgeon preferences toward SDM in surgery. ⋯ There has been limited evaluation of patient and surgeon preferences toward SDM in surgical decision-making. Generally, patients and surgeons expressed preference toward SDM. None of the articles evaluated decision-making preferences in an emergent setting, so assessment of the impact of acuity on decision-making preferences is limited. Extension of research to complex, emergent clinical settings is needed.