1  Related papers

Updated on 2022-10-13

Please check updated list here.

1.1 AutoScore original 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.

1.2 AutoScore method extension

1.2.1 Extension to survival outcomes

• 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.

1.2.2 Extension to ordinal outcomes

• 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.

1.2.3 Extension to unbalanced data

• 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.

1.2.4 AutoScore-ShapleyVIC framework for robust variable ranking

• 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 Digit Health 2022; 1(6): e0000062.

1.3 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.

• Emergency medicine
• Neurology
• Out-of-hospital cardiac arrest
• Renal medicine

1.3.1 Emergency medicine

• Xie F, Ong MEH, Liew JNMH, 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, Liu N, Yan L, et al. Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions. eClinicalMedicine 2022 Mar; 45: 101315.

1.3.2 Neurology

• Petersen KK, Lipton RB, Grober E, 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.

1.3.3 Out-of-hospital cardiac arrest

• Wong XY, Ang YK, Li K, 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, Liu M, Chen X, 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.

1.3.4 Renal medicine

• Ang Y, Li S, Ong MEH, 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.