README_Paper.md
In nliulab/AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator
AutoScore-Related Papers
AutoScore core paper
- Xie F, Chakraborty B, Ong MEH, Goldstein BA, Liu N. AutoScore: A machine learning-based automatic clinical score generator and its application to mortality prediction using electronic health records. JMIR Medical Informatics 2020; 8(10): e21798.
- Xie F, Ning Y, Liu M, Li S, Saffari SE, Yuan H, Volovici V, Ting DSW, Goldstein BA, Ong MEH, Vaughan R, Chakraborty B, Liu N. A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols 2023 Jun; 4(2): 102302.
AutoScore method extension
- Xie F, Ning Y, Yuan H, Goldstein BA, Ong MEH, Liu N, Chakraborty B. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics 2022; 125: 103959.
- Saffari SE, Ning Y, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong MEH, Liu N, AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes, BMC Medical Research Methodology 2022; 22: 286.
- Yuan H, Xie F, Ong MEH, Ning Y, Chee ML, Saffari SE, Abdullah HR, Goldstein BA, Chakraborty B, Liu N. AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of Biomedical Informatics 2022; 129: 104072.
- Ning Y, Li S, Ong ME, Xie F, Chakraborty B, Ting DS, Liu N. A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study. PLOS Digital Health 2022; 1(6): e0000062.
AutoScore clinical applications
A collection of clinical applications using AutoScore and its extensions can be found on this page. The list is categorized according to medical specialties and is updated regularly. However, due to the manual process of updating, we are unable to keep track of all publications.
- Acute and Emergency Medicine
- Cardiology
- Endocrinology
- Infectious Diseases
- Intensive and Critical Care
- Neurology
- Obstetrics and Gynaecology
- Ophthalmology
- Out-of-Hospital Cardiac Arrest
- Pediatrics
- Renal Medicine
- Respiratory Medicine
- Stroke
- Trauma
Acute and emergency medicine
- Xie F, et al. Development and assessment of an interpretable machine learning triage tool for estimating mortality after emergency admissions. JAMA Network Open 2021 Aug; 4(8): e2118467.
- Xie F, et al. Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions. eClinicalMedicine 2022 Mar; 45: 101315.
- Yu JY, et al. Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model. Scientific Reports 2024 Mar; 14: 6666.
- Look CS, et al. Improved interpretable machine learning emergency department triage tool addressing class imbalance. Digital Health 2024 May; 10: 1-12.
Cardiology
- Zahid S, et al. Development and validation of a machine learning risk-prediction model for 30 day readmission for heart failure following transcatheter aortic valve replacement (TAVR-HF Score). Current Problems in Cardiology 2024 Feb; 49(2): 102143.
- Zahid S, et al. PARCCS: A machine learning risk-prediction model for acute peripartum cardiovascular complications during delivery admissions. JACC: Advances 2024 Aug: 3(8): 101095.
Endocrinology
- Oppong AE, et al. Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity. iScience 2024 Mar; 27(3): 109225.
Infectious Diseases
- Lai CKC, et al. A machine learning-based risk score for prediction of infective endocarditis among patients with Staphylococcus aureus bacteraemia – The SABIER score. The Journal of Infectious Diseases 2024.
Intensive and critical care
- Shu T, et al. Development and assessment of scoring model for ICU stay and mortality prediction after emergency admissions in ischemic heart disease: a retrospective study of MIMIC-IV databases. Internal and Emergency Medicine 2023 Feb.
Neurology
- Petersen KK, et al. Predicting amyloid positivity in cognitively unimpaired older adults: A machine learning approach using the A4 data. Neurology 2022 Apr; 98(24): e2425-e2435.
- Pan Y, et al. Development and validation of the Florey Dementia Risk Score web-based tool to screen for Alzheimer's disease in primary care. eClinicalMedicine 2024 Oct; 76: 102834.
Obstetrics and gynecology
- Tan HS, et al. Developing the BreakThrough Pain Risk Score: an interpretable machine-learning-based risk score to predict breakthrough pain with labour epidural analgesia. Canadian Journal of Anesthesia 2022 Oct; 69(10): 1315-1317.
Ophthalmology
- Lee S, et al. Application of an interpretable machine learning for estimating severity of Graves’ Orbitopathy based on initial finding. Journal of Clinical Medicine 2023 Apr; 12(7): 2640.
- Ng C, et al. Interpretable Machine Learning–Based Risk Score for Predicting Ten-Year Corneal Graft Survival After Penetrating Keratoplasty and Deep Anterior Lamellar Keratoplasty in Asian Eyes. Cornea 2024 Jul.
Out-of-hospital cardiac arrest
- Wong XY, et al. Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework. Resuscitation 2022 Jan; 170: 126-133.
- Liu N, et al. Development and validation of interpretable prehospital return of spontaneous circulation (P-ROSC) score for out-of-hospital cardiac arrest patients using machine learning. eClinicalMedicine 2022 Jun; 48: 101422.
Pediatrics
- Chong SL, et al. Febrile infants risk score at triage (FIRST) for the early identification of serious bacterial infections. Scientific Reports 2023 Sep; 13: 15845.
Renal medicine
- Ang Y, et al. Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department. Scientific Reports 2022 May; 12: 7111.
- Jeon J, et al. Prediction tool for renal adaptation after living kidney donation using interpretable machine learning. Frontiers in Medicine 2023 Jul; 10: 1222973.
Respiratory medicine
- Kwok SWH, et al. An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems. Respiratory Research 2023 Mar; 24: 79.
Stroke
- Ullah W, et al. Discriminative Accuracy of CHA2DS2VASc Score, and Development of Predictive Accuracy Model Using Machine Learning for Ischemic Stroke in Cardiac Amyloidosis. medRxiv 2023.
Trauma
- Yu JY, et al. Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS). The Lancet Regional Health – Western Pacific 2023.
- Cleri NA, et al. Predicting traumatic brain injury outcomes using a posterior dominant rhythm. Journal of Neurosurgery 2023.
- Karabacak M, et al. The MOST (Mortality Score for TBI): A novel prediction model beyond CRASH-Basic and IMPACT-Core for isolated traumatic brain injury. Injury 2024.
nliulab/AutoScore documentation built on Oct. 18, 2024, 4:53 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
AutoScore-Related Papers
AutoScore core paper
- Xie F, Chakraborty B, Ong MEH, Goldstein BA, Liu N. AutoScore: A machine learning-based automatic clinical score generator and its application to mortality prediction using electronic health records. JMIR Medical Informatics 2020; 8(10): e21798.
- Xie F, Ning Y, Liu M, Li S, Saffari SE, Yuan H, Volovici V, Ting DSW, Goldstein BA, Ong MEH, Vaughan R, Chakraborty B, Liu N. A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols 2023 Jun; 4(2): 102302.
AutoScore method extension
- Xie F, Ning Y, Yuan H, Goldstein BA, Ong MEH, Liu N, Chakraborty B. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics 2022; 125: 103959.
- Saffari SE, Ning Y, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong MEH, Liu N, AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes, BMC Medical Research Methodology 2022; 22: 286.
- Yuan H, Xie F, Ong MEH, Ning Y, Chee ML, Saffari SE, Abdullah HR, Goldstein BA, Chakraborty B, Liu N. AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of Biomedical Informatics 2022; 129: 104072.
- Ning Y, Li S, Ong ME, Xie F, Chakraborty B, Ting DS, Liu N. A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study. PLOS Digital Health 2022; 1(6): e0000062.
AutoScore clinical applications
A collection of clinical applications using AutoScore and its extensions can be found on this page. The list is categorized according to medical specialties and is updated regularly. However, due to the manual process of updating, we are unable to keep track of all publications.
- Acute and Emergency Medicine
- Cardiology
- Endocrinology
- Infectious Diseases
- Intensive and Critical Care
- Neurology
- Obstetrics and Gynaecology
- Ophthalmology
- Out-of-Hospital Cardiac Arrest
- Pediatrics
- Renal Medicine
- Respiratory Medicine
- Stroke
- Trauma
Acute and emergency medicine
- Xie F, et al. Development and assessment of an interpretable machine learning triage tool for estimating mortality after emergency admissions. JAMA Network Open 2021 Aug; 4(8): e2118467.
- Xie F, et al. Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions. eClinicalMedicine 2022 Mar; 45: 101315.
- Yu JY, et al. Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model. Scientific Reports 2024 Mar; 14: 6666.
- Look CS, et al. Improved interpretable machine learning emergency department triage tool addressing class imbalance. Digital Health 2024 May; 10: 1-12.
Cardiology
- Zahid S, et al. Development and validation of a machine learning risk-prediction model for 30 day readmission for heart failure following transcatheter aortic valve replacement (TAVR-HF Score). Current Problems in Cardiology 2024 Feb; 49(2): 102143.
- Zahid S, et al. PARCCS: A machine learning risk-prediction model for acute peripartum cardiovascular complications during delivery admissions. JACC: Advances 2024 Aug: 3(8): 101095.
Endocrinology
- Oppong AE, et al. Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity. iScience 2024 Mar; 27(3): 109225.
Infectious Diseases
- Lai CKC, et al. A machine learning-based risk score for prediction of infective endocarditis among patients with Staphylococcus aureus bacteraemia – The SABIER score. The Journal of Infectious Diseases 2024.
Intensive and critical care
- Shu T, et al. Development and assessment of scoring model for ICU stay and mortality prediction after emergency admissions in ischemic heart disease: a retrospective study of MIMIC-IV databases. Internal and Emergency Medicine 2023 Feb.
Neurology
- Petersen KK, et al. Predicting amyloid positivity in cognitively unimpaired older adults: A machine learning approach using the A4 data. Neurology 2022 Apr; 98(24): e2425-e2435.
- Pan Y, et al. Development and validation of the Florey Dementia Risk Score web-based tool to screen for Alzheimer's disease in primary care. eClinicalMedicine 2024 Oct; 76: 102834.
Obstetrics and gynecology
- Tan HS, et al. Developing the BreakThrough Pain Risk Score: an interpretable machine-learning-based risk score to predict breakthrough pain with labour epidural analgesia. Canadian Journal of Anesthesia 2022 Oct; 69(10): 1315-1317.
Ophthalmology
- Lee S, et al. Application of an interpretable machine learning for estimating severity of Graves’ Orbitopathy based on initial finding. Journal of Clinical Medicine 2023 Apr; 12(7): 2640.
- Ng C, et al. Interpretable Machine Learning–Based Risk Score for Predicting Ten-Year Corneal Graft Survival After Penetrating Keratoplasty and Deep Anterior Lamellar Keratoplasty in Asian Eyes. Cornea 2024 Jul.
Out-of-hospital cardiac arrest
- Wong XY, et al. Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework. Resuscitation 2022 Jan; 170: 126-133.
- Liu N, et al. Development and validation of interpretable prehospital return of spontaneous circulation (P-ROSC) score for out-of-hospital cardiac arrest patients using machine learning. eClinicalMedicine 2022 Jun; 48: 101422.
Pediatrics
- Chong SL, et al. Febrile infants risk score at triage (FIRST) for the early identification of serious bacterial infections. Scientific Reports 2023 Sep; 13: 15845.
Renal medicine
- Ang Y, et al. Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department. Scientific Reports 2022 May; 12: 7111.
- Jeon J, et al. Prediction tool for renal adaptation after living kidney donation using interpretable machine learning. Frontiers in Medicine 2023 Jul; 10: 1222973.
Respiratory medicine
- Kwok SWH, et al. An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems. Respiratory Research 2023 Mar; 24: 79.
Stroke
- Ullah W, et al. Discriminative Accuracy of CHA2DS2VASc Score, and Development of Predictive Accuracy Model Using Machine Learning for Ischemic Stroke in Cardiac Amyloidosis. medRxiv 2023.
Trauma
- Yu JY, et al. Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS). The Lancet Regional Health – Western Pacific 2023.
- Cleri NA, et al. Predicting traumatic brain injury outcomes using a posterior dominant rhythm. Journal of Neurosurgery 2023.
- Karabacak M, et al. The MOST (Mortality Score for TBI): A novel prediction model beyond CRASH-Basic and IMPACT-Core for isolated traumatic brain injury. Injury 2024.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.