Multi-Objective Fine-Tuning of Clinical Scoring Tables: Adapting to Variations in Demography and Data

Kei Sen Fong, Mehul Motani
Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:744-780, 2025.

Abstract

Clinical scoring tables (e.g., CURB-65 for pneumonia severity and mortality estimation) are widely used for estimating outcomes in healthcare, but their applicability is limited by i) demographic variations, ii) incomplete data availability of clinical variables, or iii) the need to incorporate data of new cohort-relevant clinical variables. We introduce a novel constrained multi-objective evolutionary machine learning (ML) optimization framework, SET (Scoring-table Evolutionary Tuning), that fine-tunes established clinical scoring tables to enhance performance while maintaining familiarity. SET works by iteratively making small constrained changes to the original table to improve performance across multiple metrics, while maintaining a similar structure, ensuring that minimal adjustments are made. This is in contrast to ML-based proposals that replace scoring tables with entirely new models or tables, which may encounter barriers to clinical adoption. Extensive evaluations across 8 established scoring tables and cohorts demonstrate that SET allows existing clinically-trusted scoring tables to adapt to variations in demography, enhancing performance. We also show that in situations with incomplete data availability of key clinical variables, SET can still augment scoring tables and perform competitively. Additionally, SET can also augment existing tables to incorporate new cohort-relevant features.

Cite this Paper

BibTeX
@InProceedings{pmlr-v287-fong25a, title = {Multi-Objective Fine-Tuning of Clinical Scoring Tables: Adapting to Variations in Demography and Data}, author = {Fong, Kei Sen and Motani, Mehul}, booktitle = {Proceedings of the sixth Conference on Health, Inference, and Learning}, pages = {744--780}, year = {2025}, editor = {Xu, Xuhai Orson and Choi, Edward and Singhal, Pankhuri and Gerych, Walter and Tang, Shengpu and Agrawal, Monica and Subbaswamy, Adarsh and Sizikova, Elena and Dunn, Jessilyn and Daneshjou, Roxana and Sarker, Tasmie and McDermott, Matthew and Chen, Irene}, volume = {287}, series = {Proceedings of Machine Learning Research}, month = {25--27 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v287/main/assets/fong25a/fong25a.pdf}, url = {https://proceedings.mlr.press/v287/fong25a.html}, abstract = {Clinical scoring tables (e.g., CURB-65 for pneumonia severity and mortality estimation) are widely used for estimating outcomes in healthcare, but their applicability is limited by i) demographic variations, ii) incomplete data availability of clinical variables, or iii) the need to incorporate data of new cohort-relevant clinical variables. We introduce a novel constrained multi-objective evolutionary machine learning (ML) optimization framework, SET (Scoring-table Evolutionary Tuning), that fine-tunes established clinical scoring tables to enhance performance while maintaining familiarity. SET works by iteratively making small constrained changes to the original table to improve performance across multiple metrics, while maintaining a similar structure, ensuring that minimal adjustments are made. This is in contrast to ML-based proposals that replace scoring tables with entirely new models or tables, which may encounter barriers to clinical adoption. Extensive evaluations across 8 established scoring tables and cohorts demonstrate that SET allows existing clinically-trusted scoring tables to adapt to variations in demography, enhancing performance. We also show that in situations with incomplete data availability of key clinical variables, SET can still augment scoring tables and perform competitively. Additionally, SET can also augment existing tables to incorporate new cohort-relevant features.} }
Endnote
%0 Conference Paper %T Multi-Objective Fine-Tuning of Clinical Scoring Tables: Adapting to Variations in Demography and Data %A Kei Sen Fong %A Mehul Motani %B Proceedings of the sixth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2025 %E Xuhai Orson Xu %E Edward Choi %E Pankhuri Singhal %E Walter Gerych %E Shengpu Tang %E Monica Agrawal %E Adarsh Subbaswamy %E Elena Sizikova %E Jessilyn Dunn %E Roxana Daneshjou %E Tasmie Sarker %E Matthew McDermott %E Irene Chen %F pmlr-v287-fong25a %I PMLR %P 744--780 %U https://proceedings.mlr.press/v287/fong25a.html %V 287 %X Clinical scoring tables (e.g., CURB-65 for pneumonia severity and mortality estimation) are widely used for estimating outcomes in healthcare, but their applicability is limited by i) demographic variations, ii) incomplete data availability of clinical variables, or iii) the need to incorporate data of new cohort-relevant clinical variables. We introduce a novel constrained multi-objective evolutionary machine learning (ML) optimization framework, SET (Scoring-table Evolutionary Tuning), that fine-tunes established clinical scoring tables to enhance performance while maintaining familiarity. SET works by iteratively making small constrained changes to the original table to improve performance across multiple metrics, while maintaining a similar structure, ensuring that minimal adjustments are made. This is in contrast to ML-based proposals that replace scoring tables with entirely new models or tables, which may encounter barriers to clinical adoption. Extensive evaluations across 8 established scoring tables and cohorts demonstrate that SET allows existing clinically-trusted scoring tables to adapt to variations in demography, enhancing performance. We also show that in situations with incomplete data availability of key clinical variables, SET can still augment scoring tables and perform competitively. Additionally, SET can also augment existing tables to incorporate new cohort-relevant features.
APA
Fong, K.S. & Motani, M.. (2025). Multi-Objective Fine-Tuning of Clinical Scoring Tables: Adapting to Variations in Demography and Data. Proceedings of the sixth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 287:744-780 Available from https://proceedings.mlr.press/v287/fong25a.html.

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