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Improving Machine L...
Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths
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- Heyman, Ellen Tolestam (författare)
- Lund University,Lunds universitet,Akutsjukvård,Forskargrupper vid Lunds universitet,Emergency medicine,Lund University Research Groups,Halmstad County Hospital,Department of Emergency Medicine, Halland Hospital, Region Halland, Varberg, Sweden; Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
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- Ashfaq, Awais, 1990- (författare)
- Högskolan i Halmstad,Halmstad University,CAISR Centrum för tillämpade intelligenta system (IS-lab),Halland Hospital, Region Halland, Halmstad, Sweden
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- Khoshnood, Ardavan (författare)
- Lund University,Lunds universitet,Akutsjukvård,Forskargrupper vid Lunds universitet,Emergency medicine,Lund University Research Groups,Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden; Skåne University Hospital Lund, Lund, Sweden
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- Ohlsson, Mattias (författare)
- Högskolan i Halmstad,Halmstad University,Lund University,Lunds universitet,Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS),Forskargrupper vid Lunds universitet,Artificial Intelligence in CardioThoracic Sciences (AICTS),Lund University Research Groups,CAISR Centrum för tillämpade intelligenta system (IS-lab),Department of Astronomy and Theoretical Physics, Division of Computational Biology and Biological Physics, Lund University, Lund, Sweden
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- Ekelund, Ulf (författare)
- Lund University,Lunds universitet,Akutsjukvård,Forskargrupper vid Lunds universitet,Emergency medicine,Lund University Research Groups,Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden; Skåne University Hospital Lund, Lund, Sweden
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- Holmqvist, Lina Dahlén (författare)
- Gothenburg University,Göteborgs universitet,University of Gothenburg,Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Sahlgrenska University Hospitals, Gothenburg, Sweden;,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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- Lingman, Markus, 1975 (författare)
- Gothenburg University,Göteborgs universitet,University of Gothenburg,Halmstad County Hospital,Halland Hospital, Region Halland, Halmstad Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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(creator_code:org_t)
- Philadelphia, PA : Elsevier BV, 2021
- 2021
- Engelska.
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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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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Anestesi och intensivvård (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Anesthesiology and Intensive Care (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences (hsv//eng)
Nyckelord
- machine learning
- artificial intelligence
- emergency department
- emergency medicine
- end-of-life
- palliative care
- Maskininlärning
- artificial intelligence
- akutsjukvård
- palliativ vård
- livets slutskede
- machine learning
- EMERGENCY
- TOOL
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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