Using Association Rules in Antimicrobial Resistance in Stone Disease Patients

Manolitsis, Ioannis; Feretzakis, Georgios; Tzelves, Lazaros; Kalles, Dimitris; Loupelis, Evangelos; Katsimperis, Stamatios; Kosmidis, Thanos; Αναστασίου, Αθανάσιος; Koutsouris, Dimitrios; Kofopoulou, Stavroula; Verykios, Vassilios S.; Skolarikos, Andreas; Varkarakis, Ioannis

doi:10.3233/shti220765

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Nevertheless, ChatGPT is capable of learning from proper inputs, as demonstrated by Manolitsis et al . [20]. In their study with a custom-training program for ChatGPT, they found that by training the models with very specific content on the individuals, the credibility of the information received is increased.…”

Section: Enhancing Efficiencymentioning

confidence: 99%

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

Nedbal,

Naik,

Castellani

et al. 2023

Current Opinion in Urology

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Purpose of review ChatGPT has emerged as a potentially useful tool for healthcare. Its role in urology is in its infancy and has much potential for research, clinical practice and for patient assistance. With this narrative review, we want to draw a picture of what is known about ChatGPT's integration in urology, alongside future promises and challenges. Recent findings The use of ChatGPT can ease the administrative work, helping urologists with note-taking and clinical documentation such as discharge summaries and clinical notes. It can improve patient engagement through increasing awareness and facilitating communication, as it has especially been investigated for uro-oncological diseases. Its ability to understand human emotions makes ChatGPT an empathic and thoughtful interactive tool or source for urological patients and their relatives. Currently, its role in clinical diagnosis and treatment decisions is uncertain, as concerns have been raised about misinterpretation, hallucination and out-of-date information. Moreover, a mandatory regulatory process for ChatGPT in urology is yet to be established. Summary ChatGPT has the potential to contribute to precision medicine and tailored practice by its quick, structured responses. However, this will depend on how well information can be obtained by seeking appropriate responses and asking the pertinent questions. The key lies in being able to validate the responses, regulating the information shared and avoiding misuse of the same to protect the data and patient privacy. Its successful integration into mainstream urology needs educational bodies to provide guidelines or best practice recommendations for the same.

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Section: Enhancing Efficiencymentioning

confidence: 99%

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

Nedbal,

Naik,

Castellani

et al. 2023

Current Opinion in Urology

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show abstract

“…Association Rules associate a specific conclusion with a set of conditions. In comparison to standard decision tree algorithms such as the C5.0 and C&R trees, the associations can occur between any of the variables [53][54][55].…”

Section: Association Analysesmentioning

confidence: 99%

Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

Carreras,

Yukie Kikuti,

Miyaoka

et al. 2024

BioMedInformatics

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Diffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed, including cell-of-origin molecular classification; Hans’ classification and derivates; and the Schmitz, Chapuy, Lacy, Reddy, and Sha models. This study introduced different machine learning techniques and their classification. Later, several machine learning techniques and artificial neural networks were used to predict the DLBCL subtypes with high accuracy (100–95%), including Germinal center B-cell like (GCB), Activated B-cell like (ABC), Molecular high-grade (MHG), and Unclassified (UNC), in the context of the data released by the REMoDL-B trial. In order of accuracy (MHG vs. others), the techniques were XGBoost tree (100%); random trees (99.9%); random forest (99.5%); and C5, Bayesian network, SVM, logistic regression, KNN algorithm, neural networks, LSVM, discriminant analysis, CHAID, C&R tree, tree-AS, Quest, and XGBoost linear (99.4–91.1%). The inputs (predictors) were all the genes of the array and a set of 28 genes related to DLBCL-Burkitt differential expression. In summary, artificial intelligence (AI) is a useful tool for predictive analytics using gene expression data.

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“…Practitioners could input patient comorbidities and receive monitoring support aligned with patient specific evidence-based local and national guidelines 20 . Moreover, users can understand the evidence and rationale behind each prompt, enriching their learning with each interaction.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

The “Flapbot”: A Global Perspective on the Validity and Usability of a Flap Monitoring Chatbot

Ejaz,

Ali,

Berner

et al. 2024

J Reconstr Microsurg

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Background: The Flapbot chatbot assists in free flap monitoring, emphasising accessibility, user-friendliness, and global reliability. This study assesses Flapbot's worldwide validity and usability and uses qualitative analysis to identify areas for future enhancement. Methods: Flapbot, built on Google's Dialogflow, was evaluated by international plastic surgeons. Invitations were sent to the International Lower Limb Reconstruction Collaborative (INTELLECT), International Confederation of Plastic Surgery Societies (ICOPLAST), and the International Microsurgery Club. Out of the 42 surgeons who agreed to participate 21 tested the Flapbot and completed an online survey on its validity and usability. The survey had 13 validity items and 10 usability items. Data analysis involved computing the Individual Content Validity Index (I-CVI) and Scale-wide Content Validity Index (S-CVI) for validity, and the System Usability Score (SUS) for usability. Thematic analysis distilled free text responses to identify key themes. Results: Nine of thirteen items had an I-CVI over 0.78, denoting significant relevance. The S-CVI score stood at 0.82, indicating high relevance. The SUS score was 68, representing average usability. Themes highlighted issues with the current model, development suggestions, and surgeons' concerns regarding growing reliance on digital tools in healthcare. Conclusion: Flapbot is a promising digital aid for free flap monitoring. While it showcases notable validity and usability, improvements in functionality, usability, and accessibility are needed for broader global use.

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Using Association Rules in Antimicrobial Resistance in Stone Disease Patients

Cited by 4 publications

References 10 publications

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

The “Flapbot”: A Global Perspective on the Validity and Usability of a Flap Monitoring Chatbot

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