Machine Learning and Syncope Management in the ED: The Future Is Coming

Dipaola, Franca; Shiffer, Dana; Gatti, Mauro; Menè, Roberto; Solbiati, Monica; Furlan, Raffaello

doi:10.3390/medicina57040351

Cited by 7 publications

(6 citation statements)

References 97 publications

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“…Although the topic might be quite different from that of the present study, the methodology, that is the use of machine-learning techniques for automatic identification and classification of symptoms is similar to that of other studies (Dipaola et al, 2019(Dipaola et al, , 2021.…”

Section: Figurementioning

confidence: 89%

Investigating brain cortical activity in patients with post-COVID-19 brain fog

et al. 2023

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Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60–70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.

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Section: Figurementioning

confidence: 89%

Investigating brain cortical activity in patients with post-COVID-19 brain fog

et al. 2023

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“…Artificial intelligence (AI) and machine learning (ML) may help address some of these issues ( Table 1 ). Preliminary AI-based risk stratification and diagnostic methods are encouraging, 11 and include predicting short-term adverse events 12 , 13 and hospitalization, 14 , 15 diagnosing vasovagal syncope, 16 , 17 differentiating syncope from other forms of TLOC, 18 assisting in ECG interpretation, 19 interpreting ambulatory ECG monitors and implantable loop recorders, 19 and reviewing records via natural language processing (NLP). 20 However, the ultimate role for AI in syncope management remains undeveloped.…”

Section: Syncope: the Challengementioning

confidence: 99%

“…Despite these challenges, the latest syncope AI projects exploring risk stratification are encouraging. 11 Costantino et al 12 used artificial neural networks (ANNs) and prospective datasets to predict short-term (<7-10 days) adverse events after syncope and found them comparable, if not superior, to current risk stratification tools, though not via direct head-to-head comparison. Based on the same data used to develop the Canadian Syncope Risk Score, Grant et al 13 developed 4 ML models to predict short-term (<30 day) adverse outcomes after ED disposition that matched the Canadian Syncope Risk Score in performance.…”

Section: Challenges and Solutionsmentioning

confidence: 99%

Can Artificial Intelligence Enhance Syncope Management?

et al. 2023

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“…Increasing evidence suggests that the use of machine learning (ML) algorithms can improve emergency department (ED) triage, diagnosis, and risk stratification for various diseases [1]. However, the lack of external validation and reliable diagnostic standards currently limits their implementation in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department

Dipaola,

Gatti,

Menè

et al. 2023

JPM

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Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58–83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient’s initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment.

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Machine Learning and Syncope Management in the ED: The Future Is Coming

Cited by 7 publications

References 97 publications

Investigating brain cortical activity in patients with post-COVID-19 brain fog

Investigating brain cortical activity in patients with post-COVID-19 brain fog

Can Artificial Intelligence Enhance Syncope Management?

A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department

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