Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study

Pirneskoski, Jussi; Tamminen, Joonas; Kallonen, Antti; Nurmi, Jouni; Kuisma, Markku; Olkkola, Klaus T.; Hoppu, Sanna

doi:10.1016/j.resplu.2020.100046

Cited by 13 publications

(11 citation statements)

References 17 publications

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“…Shandilya et al (107) demonstrate this with nonlinear feature extraction and fusion of multimodal capnographic and ECG signal data, resulting in a prediction of defibrillation outcomes with an AUROC of 93•8%. Pirneskoski et al (95) and Spangler et al (111)'s AI models for risk prediction of various short-term outcomes outperformed the National Early Warning Scores (NEWS) even when using the same variables, suggesting superior discrimination with nonlinear modelling. Performance was further improved when multimodal data was included (95).…”

Section: Discussionmentioning

confidence: 99%

“…Pirneskoski et al (95) and Spangler et al (111)'s AI models for risk prediction of various short-term outcomes outperformed the National Early Warning Scores (NEWS) even when using the same variables, suggesting superior discrimination with nonlinear modelling. Performance was further improved when multimodal data was included (95). Several studies used NLP to analyse multimodal EHR free-text data and speech audio samples for OHCA identification (22,23,25) or general triage (42), a task not possible with traditional methods.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Chee

Huang

et al. 2023

Preprint

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Introduction: The literature on the use of AI in prehospital emergency care (PEC) settings is scattered and diverse, making it difficult to understand the current state of the field. In this scoping review, we aim to provide a descriptive analysis of the current literature and to visualise and identify knowledge and methodological gaps using an evidence map. Methods: We conducted a scoping review from inception until 14 December 2021 on MEDLINE, Embase, Scopus, IEEE Xplore, ACM Digital Library, and Cochrane Central Register of Controlled Trials (CENTRAL). We included peer-reviewed, original studies that applied AI to prehospital data, including applications for cardiopulmonary resuscitation (CPR), automated external defibrillation (AED), out-of-hospital cardiac arrest, and emergency medical service (EMS) infrastructure like stations and ambulances. Results: The search yielded 4350 articles, of which 106 met the inclusion criteria. Most studies were retrospective (n=88, 83.0%), with only one (0.9%) randomised controlled trial. Studies were mostly internally validated (n=96, 90.6%), and only ten studies (9.4%) reported on calibration metrics. While the most studied AI applications were Triage/Prognostication (n=52, 49.1%) and CPR/AED optimisation (n=26, 24.5%), a few studies reported unique use cases of AI such as patient-trial matching for research and Internet-of-Things (IoT) wearables for continuous monitoring. Out of 49 studies that identified a comparator, 39 reported AI performance superior to either clinicians or non-AI status quo algorithms. The minority of studies utilised multimodal inputs (n=37, 34.9%), with few models using text (n=8), audio (n=5), images (n=1), or videos (n=0) as inputs. Conclusion: AI in PEC is a growing field and several promising use cases have been reported, including prognostication, demand prediction, resource optimisation, and IoT continuous monitoring systems. Prospective, externally validated studies are needed before applications can progress beyond the proof-of-concept stage to real-world clinical settings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Chee

Huang

et al. 2023

Preprint

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“…We also confirmed that hospital factors such as ED crowding, were the main factors in the prediction model. The application of ML for decision-making is gradually expanding in the medical field, including during the prehospital stage 25 , 26 . However, while the data generated during the prehospital stage are not yet fully utilized in ML research, it is significant that we developed a predictive model with integrated information based on the national dataset extracted from the standardized prehospital care system.…”

Section: Discussionmentioning

confidence: 99%

Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database

Kim

Choi

Kim

et al. 2022

Sci Rep

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In this retrospective observational study, we aimed to develop a machine-learning model using data obtained at the prehospital stage to predict in-hospital cardiac arrest in the emergency department (ED) of patients transferred via emergency medical services. The dataset was constructed by attaching the prehospital information from the National Fire Agency and hospital factors to data from the National Emergency Department Information System. Machine-learning models were developed using patient variables, with and without hospital factors. We validated model performance and used the SHapley Additive exPlanation model interpretation. In-hospital cardiac arrest occurred in 5431 of the 1,350,693 patients (0.4%). The extreme gradient boosting model showed the best performance with area under receiver operating curve of 0.9267 when incorporating the hospital factor. Oxygen supply, age, oxygen saturation, systolic blood pressure, the number of ED beds, ED occupancy, and pulse rate were the most influential variables, in that order. ED occupancy and in-hospital cardiac arrest occurrence were positively correlated, and the impact of ED occupancy appeared greater in small hospitals. The machine-learning predictive model using the integrated information acquired in the prehospital stage effectively predicted in-hospital cardiac arrest in the ED and can contribute to the efficient operation of emergency medical systems.

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“…The random forest method has the advantage of determining each input data point, but the disadvantage of being difficult to visualize [ 16 17 ]. Furthermore, the most important limitation of the random forest approach is that generalizing a dataset is difficult owing to the high probability of the accuracy or area under the receiver operating characteristic curve being different for each patient cohort [ 18 ].…”

Section: Representative Machine Learning Modelsmentioning

confidence: 99%

Use of Machine Learning in Stroke Rehabilitation: A Narrative Review

Choo

Lee

2022

Brain Neurorehabil

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A narrative review was conducted of machine learning applications and research in the field of stroke rehabilitation. The machine learning models commonly used in medical research include random forest, logistic regression, and deep neural networks. Convolutional neural networks (CNNs), a type of deep neural network, are typically used for image analysis. Machine learning has been used in stroke rehabilitation to predict recovery of motor function using a large amount of clinical data as input. Recent studies on predicting motor function have trained CNN models using magnetic resonance images as input data together with clinical data to increase the accuracy of motor function prediction models. Additionally, a model interpreting videofluoroscopic swallowing studies was developed and investigated. In the future, we anticipate that machine learning will be actively used to treat stroke patients, such as predicting the occurrence of depression and the recovery of language, cognitive, and sensory function, as well as prescribing appropriate rehabilitation treatments.

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Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study

Cited by 13 publications

References 17 publications

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database

Use of Machine Learning in Stroke Rehabilitation: A Narrative Review

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