Search

Artificial Intelligence (AI) has shown the ability to enhance the accuracy and efficiency of physicians. ChatGPT is an AI chatbot that can interact with humans through text, over the internet. It is trained with machine learning algorithms, using large datasets. In this study, we compare the performance of using a ChatGPT API 3.5 Turbo model to a general model, in assisting urologists in obtaining accurate, valid medical information. The API was accessed through a Python script that was applied specifically for this study based on 2023 EAU guidelines in PDF format. This custom-trained model leads to providing doctors with more precise, prompt answers about specific urologic subjects, thus helping them, ultimately, providing better patient care. [ABSTRACT FROM AUTHOR]

ASCAPE Project is a study aiming to implement the advances of Artificial Intelligence (AI), to support prostate cancer survivors, regarding quality of life issues. The aim of the study is to determine characteristics of patients who accepted to join ASCAPE project. It results that participants of the study mainly originate from higher-educated societies that are better informed about the potential benefits of AI in medicine. Therefore, efforts should be focused on eliminating patients’ reluctancy by better informing them on the potential benefits of AI. [ABSTRACT FROM AUTHOR]

In this study a deep learning architecture based on a convolutional neural network has been evaluated for the classification of white light images of colorectal polyps acquired during the process of a colonoscopy, to estimate the accuracy of the optical recognition of histologic types of polyps. Convolutional neural networks (CNNs), a subclass of artificial neural networks that have gained dominance in several computer vision tasks, are gaining popularity in many medical fields, including endoscopy. The TensorFlow framework was used for implementing EfficientNetB7, which was trained with 924 images, drawn from 86 patients. 55% of the polyps were adenomas, 22% were hyperplastic, and 17% were lesions with sessile serrations. The validation loss, accuracy, and AUC ROC were 0.4845, 0.7778, and 0.8881 respectively. [ABSTRACT FROM AUTHOR]

The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17- month period, relative to the outcome, in order to build a prognostic model. We used the Google Vertex AI platform, on the one hand, to evaluate its performance in predicting ICU mortality, and on the other hand to show the ease with which even non-experts can make prognostic models. The model’s performance regarding the area under the receiver operating characteristic curve (AUC-ROC) was 0.955. The six highest-ranked predictors of mortality in the prognostic model were age, serum urea, platelets, C-reactive protein, hemoglobin, and SGOT. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel, exact border-based approach that provides an optimal solution for the hiding of sensitive frequent item sets by 1) minimally extending the original database by a synthetically generated database part--the database extension, 2) formulating the creation of the database extension as a constraint satisfaction problem, 3) mapping the constraint satisfaction problem to an equivalent binary integer programming problem, 4) exploiting underutilized synthetic transactions to proportionally increase the support of nonsensitive item sets, 5) minimally relaxing the constraint satisfaction problem to provide an approximate solution close to the optimal one when an ideal solution does not exist, and 6) using a partitioning in the universe of the items to increase the efficiency of the proposed hiding algorithm. Extending the original database for sensitive item set hiding is proved to provide optimal solutions to an extended set of hiding problems compared to previous approaches and to provide solutions of higher quality. Moreover, the application of binary integer programming enables the simultaneous hiding of the sensitive item sets and thus allows for the identification of globally optimal solutions. Index Terms--Privacy preserving data mining, knowledge hiding, association rule mining, binary integer programming.

Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area. Index Terms--Duplicate detection, data cleaning, data integration, record linkage, data deduplication, instance identification, database hardening, name matching, identity uncertainty, entity resolution, fuzzy duplicate detection, entity matching.