Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability

Charlton, Colleen E.; Poon, Michael T C; Brennan, Paul M.; Fleuriot, Jacques

doi:10.1016/j.cmpb.2023.107482

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…Integration strategies can align ML models with existing medical knowledge and clinical practices, a critical aspect in fostering trust among physicians who rely on the efficacy of these long-standing guidelines. This alignment is also crucial in ensuring continuity of care and promoting the adoption of such models in clinical settings [ 47 ]. In the presented case study, integrations in learning and output evaluation phases attained near-complete or complete adherence to the guidelines while improving performance, thereby enhancing the model’s potential for clinical adoption.…”

Section: Discussionmentioning

confidence: 99%

“…EHRs also provide structured data, including patient demographics, laboratory tests, and medications, which can be challenging to analyse due to their high dimensionality, temporality, sparsity, irregularity and bias. To address these challenges, recent integrations have leveraged expert defined thresholds to discretise continuous variables into meaningful intervals [ 47 ], as well as previous literature [ 48 ] and existing expert models [ 49 ] to inform feature selection. Other applications have generated concise sets of meaningful summary features from expert-defined rules [ 50 ] and enriched EHR representation through the integration of knowledge graphs [ 51 ] and hierarchical code classifications [ 52 ].…”

Section: Previous Workmentioning

confidence: 99%

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Medical-informed machine learning: integrating prior knowledge into medical decision systems

Sirocchi,

Bogliolo,

Montagna

2024

BMC Med Inform Decis Mak

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Background Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML. Methods The study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models. Results The findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios. Conclusions By illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.

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Section: Discussionmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Medical-informed machine learning: integrating prior knowledge into medical decision systems

Sirocchi,

Bogliolo,

Montagna

2024

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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“…Improving the interpretability of the model is another important direction for the development of ML in the differential diagnosis of LTBI [ 291 , 292 ]. Traditionally, ML algorithms often use black-box models to balance interpretability and predictiveness [ 293 ].…”

Section: Future Directions Of ML For the Differential Diagnosis Of Ltbimentioning

confidence: 99%

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Li,

Yang,

Zhuang

et al. 2023

Military Med Res

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Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

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Machine learning models’ assessment: trust and performance

Sousa,

Paredes,

Rocha

et al. 2024

Med Biol Eng Comput

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The common black box nature of machine learning models is an obstacle to their application in health care context. Their widespread application is limited by a significant “lack of trust.” So, the main goal of this work is the development of an evaluation approach that can assess, simultaneously, trust and performance. Trust assessment is based on (i) model robustness (stability assessment), (ii) confidence (95% CI of geometric mean), and (iii) interpretability (comparison of respective features ranking with clinical evidence). Performance is assessed through geometric mean. For validation, in patients’ stratification in cardiovascular risk assessment, a Portuguese dataset (N=1544) was applied. Five different models were compared: (i) GRACE score, the most common risk assessment tool in Portugal for patients with acute coronary syndrome; (ii) logistic regression; (iii) Naïve Bayes; (iv) decision trees; and (v) rule-based approach, previously developed by this team. The obtained results confirm that the simultaneous assessment of trust and performance can be successfully implemented. The rule-based approach seems to have potential for clinical application. It provides a high level of trust in the respective operation while outperformed the GRACE model’s performance, enhancing the required physicians’ acceptance. This may increase the possibility to effectively aid the clinical decision. Graphical abstract

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Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability

Cited by 7 publications

References 40 publications

Medical-informed machine learning: integrating prior knowledge into medical decision systems

Medical-informed machine learning: integrating prior knowledge into medical decision systems

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Machine learning models’ assessment: trust and performance

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