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- Heyman, Ellen Tolestam, et al.
(författare)
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Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths
- 2021
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Ingår i: Journal of Emergency Medicine. - Philadelphia, PA : Elsevier BV. - 0736-4679 .- 1090-1280. ; 61:6, s. 763-773
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit.OBJECTIVES: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge.METHODS: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM).RESULTS: Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models.CONCLUSION: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.
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| 2. |
- Tolestam Heyman, Ellen, et al.
(författare)
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Design of an AI Support for Diagnosis of Dyspneic Adults at Time of Triage in the Emergency Department
- 2023
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We created an AI support for diagnosis in dyspneic adults at time of triage in the emergency department.Complete data from an entire regional health care system was analyzed, to find AI-derived, unknown, important diagnostic predictors. Most important were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life difficulties and maternal care.Sensitivity for AHF, eCOPD and pneumonia was 75%, 93%, and 54%, respectively, with a specificity above 75%. Each patient visit received an individual graph with the AI´s underlying decision basis.
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| 3. |
- Tolestam Heyman, Ellen, et al.
(författare)
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How does an AI diagnose dyspnoea in ED triage without human guidance?
- 2024
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- • We aimed to capture possible insights from an AI diagnosing without human guidance.• We believe the result mainly aligns with previous knowledge. Though, vital signs and sex did not aid the AI diagnostics.
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| 5. |
- Tolestam Heyman, Ellen, 1969, et al.
(författare)
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Likelihood of admission to hospital from the emergency department is not universally associated with hospital bed occupancy at the time of admission
- 2021
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Ingår i: International Journal of Health Planning and Management. - : Wiley. - 0749-6753 .- 1099-1751. ; 36:2, s. 353-363
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Tidskriftsartikel (refereegranskat)abstract
- Background The decision to admit into the hospital from the emergency department (ED) is considered to be important and challenging. The aim was to assess whether previously published results suggesting an association between hospital bed occupancy and likelihood of hospital admission from the ED can be reproduced in a different study population. Methods A retrospective cohort study of attendances at two Swedish EDs in 2015 was performed. Admission to hospital was assessed in relation to hospital bed occupancy together with other clinically relevant variables. Hospital bed occupancy was categorized and univariate and multivariate logistic regression were performed. Results In total 89,503 patient attendances were included in the final analysis. Of those, 29.1% resulted in admission within 24 h. The mean hospital bed occupancy by the hour of the two hospitals was 87.1% (SD 7.6). In both the univariate and multivariate analysis, odds ratio for admission within 24 h from the ED did not decrease significantly with an increasing hospital bed occupancy. Conclusions A negative association between admission to hospital and occupancy level, as reported elsewhere, was not replicated. This suggests that the previously shown association might not be universal but may vary across sites due to setting specific circumstances.
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