Specifics of medical data mining for diagnosis aid: A survey

Itani, Sarah; Lecron, Fabian; Fortemps, Philippe

doi:10.1016/j.eswa.2018.09.056

Cited by 49 publications

(34 citation statements)

References 92 publications

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“…As suggested by [19], ML methods differ from standard statistical ones in some respects. On the one hand, ML is perceived as a promising alternative way of conducting exploratory data analyses which are inductive and assumption-free [20–22]. Indeed, ML methods are implemented to extract general patterns and relations from observational data [19].…”

Section: Introductionmentioning

confidence: 99%

“…Admittedly, such models provide satisfaction on prediction accuracy, but the related predictions may be hardly interpreted. Yet, for the purpose of diagnosis aid, the interpretability of a model is a quality that is imperative to reach, since it ensures that (1) the model is able to infer a patient’s state with comprehensive justifications; (2) the model may lead to a better understanding of the disorder [20, 21, 34, 35]. Actually, such a goal is in line with the recent paradigm of Theory-Guided Data Science (TGDS) [36].…”

Section: Introductionmentioning

confidence: 99%

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Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder

et al. 2019

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Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child’s wellbeing, especially in the academic, psychological and relational planes. The current evaluation of the disorder is supported by clinical assessment and written tests. A definitive diagnosis is usually made based on the DSM-V criteria. There is a lot of ongoing research on ADHD, in order to determine the neurophysiological basis of the disorder and to reach a more objective diagnosis. The advent of Machine Learning (ML) opens up promising prospects for the development of systems able to predict a diagnosis from phenotypic and neuroimaging data. This was the reason why the ADHD-200 contest was launched a few years ago. Based on the publicly available ADHD-200 collection, participants were challenged to predict ADHD with the best possible predictive accuracy. In the present work, we propose instead a ML methodology which primarily places importance on the explanatory power of a model. Such an approach is intended to achieve a fair trade-off between the needs of performance and interpretability expected from medical diagnosis aid systems. We applied our methodology on a data sample extracted from the ADHD-200 collection, through the development of decision trees which are valued for their readability. Our analysis indicates the relevance of the limbic system for the diagnosis of the disorder. Moreover, while providing explanations that make sense, the resulting decision tree performs favorably given the recent results reported in the literature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder

et al. 2019

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“…The emerging domain of explainable Artificial Intelligence (xAI) is of particular interest in this respect (21). Indeed, explainability allows clinicians to choose to trust, or not, the recommendations (22). Moreover, it is well established that ML models tend to reproduce the biases present in the training data sets, often caused by the unbalanced representation of the classification categories.…”

Section: Designing Explainable Solutionsmentioning

confidence: 99%

“…Though outstanding, the efforts for large-scale data gathering across several sites yield disparities in terms of demographics and experimental protocols (22). The homogenization of experimental protocols, and the design of appropriate validation procedures are respectively thought as ways for achieving and assessing generalizability (18,19).…”

Section: Addressing the Question Of Heterogeneity In Datamentioning

confidence: 99%

At the Crossroads Between Psychiatry and Machine Learning: Insights Into Paradigms and Challenges for Clinical Applicability

Itani

Rossignol

2020

Front. Psychiatry

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“…Thus the advantages of the data-driven approaches may be greatly reduced. Also, the trained models and diagnostic results lack interpret-ability [21,22], which is almost indispensable in real clinical diagnoses. Because the AI software users, the doctors responsible for the diagnoses, need to understand the diagnostic process before believing the results, at least for general clinical diagnoses at the present stage [20].…”

Section: Introductionmentioning

confidence: 99%

Towards Dynamic Uncertain Causality Graphs for the Intelligent Diagnosis and Treatment of Hepatitis B

Deng

Zhang

2020

Symmetry

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Hepatitis B is a widespread epidemic in the world, but so far no single drug has been shown to kill or eliminate the Hepatitis B virus and heal people with chronic Hepatitis B virus infection. Based on comprehensive investigations to relevant characteristics of Hepatitis B, a diagnostic modelling and reasoning methodology using Dynamic Uncertain Causality Graph is proposed. The symptoms, physical signs, examinations results, medical histories, etiology, pathogenesis and other factors were included in the diagnosis model. In order to reduce the difficulty of building the model, a modular modeling scheme is proposed, which provides multi-perspectives and arbitrary granularity for the expression of disease causality. The chain reasoning algorithm and weighted logic operation mechanism are introduced to ensure the correctness and effectiveness of diagnostic reasoning under incomplete and uncertain information. In addition, the causal view of the potential interactions between diseases and symptoms visually shows the reasoning process in a graphical way. In the relevant model, the model of the diagnostic process and the model of the therapeutic process are symmetrical. The results show that, even with incomplete observations, the proposed methodology achieves encouraging diagnostic accuracy and effectiveness, providing a promising assistance tool for physicians in the diagnosis of Hepatitis B.

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Specifics of medical data mining for diagnosis aid: A survey

Cited by 49 publications

References 92 publications

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder

At the Crossroads Between Psychiatry and Machine Learning: Insights Into Paradigms and Challenges for Clinical Applicability

Towards Dynamic Uncertain Causality Graphs for the Intelligent Diagnosis and Treatment of Hepatitis B

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