Information Extraction from Clinical Practice Guidelines: A Step Towards Guidelines Adherence

Hussain, Musarrat; Lee, Sungyoung

doi:10.1007/978-3-030-19063-7_81

Cited by 4 publications

(10 citation statements)

References 9 publications

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“…Later, in Section 5, we show further improvements applying the three algorithms mentioned here to the vector representations produced by the deep learning models. In addition, we also show strong improvements on the combined classes CC, CA and A, used in [26,27].…”

Section: Evaluation Measures and Baseline Resultsmentioning

confidence: 62%

“…Similarly, the use of specific heuristic patterns has been shown to lead to a relatively high 85.54% accuracy, in identifying recommendation statements in the hypertension guideline [26]. Ensemble learning was applied [27] to the same set of three guidelines as used in this article, achieving80-84% accuracy. Part of the ensemble was a deep learning module, but it was the weakest overall performer.…”

Section: Analysis Of Medical Guidelinesmentioning

confidence: 99%

“…Hussain and Lee [27] proposed two methods to detect recommendations from clinical practice guidelines. They were defined in [26,27] as the combined classes CCA+A, i.e., condition-consequence, condition-action and action.…”

Section: Comparison With the Prior Artmentioning

confidence: 99%

“…They achieved approximately 80% accuracy for the first experiment and 84% accuracy for the second one. Although deep learning was used as a part of an ensemble learning model [27], it was the weakest overall performer.…”

Section: Comparison With the Prior Artmentioning

confidence: 99%

“…In contrast, using the transfer method described earlier, with BioBERT vectors as features for a logistic regression classifier, we achieved, as shown in Table 15, a 91 % accuracy in detecting recommendations. They are defined in [26,27] as consisting of the combined classes CCA+A, i.e., condition-consequence, condition-action and action. However, we should note that accuracy is perhaps a less informative measure than F1 in cases of imbalanced classes.…”

Section: Comparison With the Prior Artmentioning

confidence: 99%

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Identifying Condition-action Statements in Medical Guidelines: Three Studies using Machine Learning and Domain Adaptation

Hematialam

Zadrozny

2021

Preprint

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Background: Medical guidelines provide the conceptual link between a diagnosis and a recommendation. They often disagree on their recommendations. There are over thirty five thousand guidelines indexed by PubMed, which creates a need for automated methods for analysis of recommendations, i.e., recommended actions, for similar conditions. Results: This article advances the state of the art in text understanding of medical guidelines by showing the applicability of transformer-based models and transfer learning (domain adaptation) to the problem of finding condition-action and other conditional sentences. We report results of three studies using syntactic, semantic and deep learning methods, with and without transformer-based models such as BioBERT and BERT. We perform in depth evaluation on a set of three annotated medical guidelines. Our experiments show that a combination of machine learning domain adaptation and transfer can improve the ability to automatically find conditional sentences in clinical guidelines. We show substantial improvements over prior art (up to 25%), and discuss several directions of extending this work, including addressing the problem of paucity of annotated data.Conclusion: Modern deep learning methods, when applied to the text of clinical guidelines, yield substantial improvements in our ability to find sentences expressing the relations of condition-consequence, condition-action and action.

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Section: Evaluation Measures and Baseline Resultsmentioning

confidence: 62%

Section: Analysis Of Medical Guidelinesmentioning

confidence: 99%

Section: Comparison With the Prior Artmentioning

confidence: 99%

Section: Comparison With the Prior Artmentioning

confidence: 99%

Section: Comparison With the Prior Artmentioning

confidence: 99%

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Identifying Condition-action Statements in Medical Guidelines: Three Studies using Machine Learning and Domain Adaptation

Hematialam

Zadrozny

2021

Preprint

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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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Methods Used to Compare Narrative Clinical Practice Guidelines: A Scoping Review

Azarpira

Redjdal

Bouaud

et al. 2022

Studies in Health Technology and Informatics

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Guideline-based clinical decision support systems (CDSSs) need the most recent evidence for reliable performance, making the provision of regularly updated clinical practice guidelines (CPGs) a major issue. Some international guidelines are renewed in short intervals and can be used for checking the status of given national guidelines with regard to the most recent evidence. Considering the volume of medical data and the number of CPGs published, computerized comparison of clinical guidelines can be an effective method. We performed a scoping review to evaluate the methods used for comparing two CPGs. We searched for methods for extracting CPG components and for methods used for comparing CPGs at different levels of abstraction. In each case, computerized and semi-computerized methods were recognized. Expert knowledge has yet a determinant role for assessing the comparisons, this role being more prominent for the extraction of semantic rules and the resolution of inconsistencies.

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Information Extraction from Clinical Practice Guidelines: A Step Towards Guidelines Adherence

Cited by 4 publications

References 9 publications

Identifying Condition-action Statements in Medical Guidelines: Three Studies using Machine Learning and Domain Adaptation

Identifying Condition-action Statements in Medical Guidelines: Three Studies using Machine Learning and Domain Adaptation

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

Methods Used to Compare Narrative Clinical Practice Guidelines: A Scoping Review

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