Reusing Decisions Made with One Decision Support System to Assess a Second Decision Support System: Introducing the Notion of Complex Cases

Redjdal, Akram; Bouaud, Jacques; Guézennec, Gilles; Gligorov, Joseph; Séroussi, Brigitte

doi:10.3233/shti210251

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…After storing, indexing and delineating this knowledge, it uses computational reasoning methods to apply it to specific situations, generate and evaluate hypotheses, and then provide an evaluation for doctors 44 , 45 . There are several diagnostic criteria for cancer, such as CancerLinq 46 and a system called OncoDoc 47 . Through “reading” the literature, protocols and medical records, and learning from MSKCC test cases and experts, WFO obtains a large amount of knowledge as a preliminary knowledge base 23 , 24 .…”

Section: Application Characteristics Of the Cdssmentioning

confidence: 99%

Artificial intelligence in clinical decision support systems for oncology

Wang¹,

Chen²,

Zhang³

et al. 2023

Int. J. Med. Sci.

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Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.

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Section: Application Characteristics Of the Cdssmentioning

confidence: 99%

Artificial intelligence in clinical decision support systems for oncology

Wang¹,

Chen²,

Zhang³

et al. 2023

Int. J. Med. Sci.

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“…DESIREE [4] is a European project that aims at developing web-based services for the management of primary breast cancer by MTBs. While evaluating the guideline-based component of DESIREE, we found that for some patient cases the system did not provide recommendations or gave inappropriate recommendations [5]. These patient cases were considered as "complex cases".…”

Section: Introductionmentioning

confidence: 99%

Comparison of MetaMap, cTAKES, SIFR, and ECMT to Annotate Breast Cancer Patient Summaries

Redjdal¹,

Bouaud²,

Gligorov

et al. 2022

MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation

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Most clinical texts including breast cancer patient summaries (BCPSs) are elaborated as narrative documents difficult to process by decision support systems. Annotators have been developed to extract the relevant content of such documents, e.g., MetaMap and cTAKES, that work with the English language and perform concept mapping using UMLS, SIFR and ECMT, that work for the French language and provide concepts using various terminologies. We compared the four annotators on a sample of 25 French BCPSs, pre-processed to manage acronyms and translated in English. We observed that MetaMap extracted the largest number of UMLS concepts (15,458), followed by SIFR (3,784), ECMT (1,962), and cTAKES (1,769). Each annotator extracted specific valuable information, not proposed by the other annotators. Considered as complementary, all annotators should be used in sequence to optimize the results.

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“…DESIREE is a European project which aimed at developing web-based services for the management of primary breast cancer by MTBs. During the evaluation of the guideline-based clinical decision support system (CDSS) of DESIREE, we found that for some patient cases the system did not provide any therapeutic proposals or provided recommendations which were not followed by MTB clinicians [2]. These clinical cases were considered as complex cases, and we assume that such cases are not correctly handled by guidelines and need deeper MTB discussion.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases

Redjdal

Bouaud

Gligorov

et al. 2022

Studies in Health Technology and Informatics

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In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MTBs are now being challenged because there are a lot of cases and not enough time to discuss all the of them. During the evaluation of the guideline-based clinical decision support system (CDSS) of the DESIREE project, we found that for some clinical cases, the system did not produce recommendations. We assumed that these cases were complex clinical cases and needed deeper MTB discussions. In this work, we trained and tested several machine learning and deep learning algorithms on a labelled sample of 298 breast cancer patient summaries, to predict the complexity of a breast cancer clinical case. XGboost and multi-layer perceptron were the models with the best result, with an F1 score of 83%.

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Reusing Decisions Made with One Decision Support System to Assess a Second Decision Support System: Introducing the Notion of Complex Cases

Cited by 5 publications

References 5 publications

Artificial intelligence in clinical decision support systems for oncology

Artificial intelligence in clinical decision support systems for oncology

Comparison of MetaMap, cTAKES, SIFR, and ECMT to Annotate Breast Cancer Patient Summaries

Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases

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