Interpretable Narrative Explanation for ML Predictors with LP: A Case Study for XAI

In the era of digital revolution, individual lives are going to cross and interconnect ubiquitous online domains and offline reality based on smart technologies\u2014discovering, storing, processing, learning, analysing, and predicting from huge amounts of environment-collected data. Sub-symbolic techniques, such as deep learning, play a key role there, yet they are often built as black boxes, which are not inspectable, interpretable, explainable. New research efforts towards explainable artificial intelligence (XAI) are trying to address those issues, with the final purpose of building understandable, accountable, and trustable AI systems\u2014still, seemingly with a long way to go. Generally speaking, while we fully understand and appreciate the power of sub-symbolic approaches, we believe that symbolic approaches to machine intelligence, once properly combined with sub-symbolic ones, have a critical role to play in order to achieve key properties of XAI such as observability, interpretability, explainability, accountability, and trustability. In this paper we describe an example of integration of symbolic and sub-symbolic techniques. First, we sketch a general framework where symbolic and sub-symbolic approaches could fruitfully combine to produce intelligent behaviour in AI applications. Then, we focus in particular on the goal of building a narrative explanation for ML predictors: to this end, we exploit the logical knowledge obtained translating decision tree predictors into logical programs

Enhancing association rules algorithms for mining distributed databases. Integration of fast BitTable and multi-agent association rules mining in distributed medical databases for decision support.

Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making

Iatrogenic pathology of the urinary bladder

Intravesical immunotherapy, chemotherapy, and neoadjuvant systemic chemotherapy are among the most frequent therapeutic procedures to treat malignancies of the urinary bladder. These treatment modalities produce reactive morphologic changes in the urothelium that can mimic urothelial carcinoma in situ, urothelial dysplasia or true invasive urothelial neoplasia. Mitomycin C used after transurethral resection of bladder tumor to reduce recurrences, BCG intravesical immunotherapy to treat high risk non-muscle invasive bladder cancer and urothelial carcinoma in situ, and platinum-based systemic chemotherapy to improve post-cystectomy disease-specific survival some of the causes of therapy related atypia in urinary bladder. In addition, a number of systemic drugs in use to treat other systemic diseases, such as cyclophosphamide used to treat certain auto-immune disorders or hematologic malignancies, or the anesthetics ketamine increasingly used as illegal recreational drug, may produce similarly relevant atypical changes in the urothelium, and therefore, need to be differentiated from intraepithelial neoplasia. Immunohistochemical approach to reactive urothelium from CIS using CK20, p53, and CD44 may also be of utility in the pos-therapy scenario

Point-of-Care Ultrasound Assessment of Tropical Infectious Diseases—A Review of Applications and Perspectives

The development of good quality and affordable ultrasound machines has led to the establishment and implementation of numerous point-of-care ultrasound (POCUS) protocols in various medical disciplines. POCUS for major infectious diseases endemic in tropical regions has received less attention, despite its likely even more pronounced benefit for populations with limited access to imaging infrastructure. Focused assessment with sonography for HIV-associated TB (FASH) and echinococcosis (FASE) are the only two POCUS protocols for tropical infectious diseases, which have been formally investigated and which have been implemented in routine patient care today. This review collates the available evidence for FASH and FASE, and discusses sonographic experiences reported for urinary and intestinal schistosomiasis, lymphatic filariasis, viral hemorrhagic fevers, amebic liver abscess, and visceral leishmaniasis. Potential POCUS protocols are suggested and technical as well as training aspects in the context of resource-limited settings are reviewed. Using the focused approach for tropical infectious diseases will make ultrasound diagnosis available to patients who would otherwise have very limited or no access to medical imaging

Diseases of the Abdomen and Pelvis 2018-2021: Diagnostic Imaging - IDKD Book

Gastrointestinal disease; PET/CT; Radiology; X-ray; IDKD; Davo

Urinary Tract Infection Analysis using Machine Learning based Classification and ANN- A Study of Prediction

Urinary tract infection is the most frequently diagnosed infection among humans. A urinary tract infection (UTI) affects the areas of urinary system which includes the ureters, bladder, kidneys and urethra. The primary infected area of urinary system involves the lower tract i.e. bladder and urethra. The infection in bladder is painful as well as uncomfortable but if it spreads to kidneys, it can have severe consequences. Women are more susceptible to urinary infection in comparison to men due to their physiology. This paper aims to study and assess the impact and causes of urinary tract infection in human beings and evaluate the machine learning approach for urinary disease forecasting. The paper also proposed machine learning based methodology for the prediction of the urinary infection and estimating the outcomes of the designed procedures over real-time data and validating the same. The paper focuses to get high prediction accuracy of UTI using confusion matrix by Machine Based Classification and ANN technique. Some specific parameters have been selected with the help of Analysis of variance technique. The naive bayes classifier, J48 decision tree algorithm, and Artificial neural network have been used for the prediction of presence of urinary infection. The accuracy achieved by the proposed model is 95.5% approximately

AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models

Due to the large search space and conflicting objectives, drug design and discovery is a difficult problem for which new machine learning (ML) approaches are required. Here, the problem is to invent a method by which new, therapeutically useful, compounds can be discovered; and to simultaneously avoid compounds which will fail clinical trials or pass unwanted effects onto the end patient. By extending current technologies as well as adding new ones, more design criteria can be included, and more promising novel drugs can be discovered. This work advances the field of computational drug design by (1) developing MOEA-DT, a non-deep learning application for multi-objective molecular optimization, which generates new molecules with high performance in a variety of design criteria; and (2) developing SEMTL-BERT, a side effect prediction algorithm which leverages the latest ML techniques and datasets to accomplish its task. Experiments performed show that MOEA-DT either matches or outperforms other similar methods, and that SEMTL-BERT can enhance predictive ability

Metagenomics in diagnosis and improved targeted treatment of UTI

INTRODUCTION: The genomic revolution has transformed our understanding of urinary tract infection. There has been a paradigm shift from the dogmatic statement that urine is sterile in healthy people, as we are becoming forever more familiar with the knowledge that bacterial communities exist within the urinary tracts of healthy people. Metagenomics can investigate the broad populations of microbial communities, analysing all the DNA present within a sample, providing comprehensive data regarding the state of the microenvironment of a patient's urinary tract. This permits medical practitioners to more accurately target organisms that may be responsible for disease-a form of 'precision medicine'. METHODS AND RESULTS: This paper is derived from an extensive review and analysis of the available literature on the topic of metagenomic sequencing in urological science, using the PubMed search engine. The search yielded a total of 406 results, and manual selection of appropriate papers was subsequently performed. Only one randomised clinical trial comparing metagenomic sequencing to standard culture and sensitivity in the arena of urinary tract infection was found. CONCLUSION: Out of this process, this paper explores the limitations of traditional methods of culture and sensitivity and delves into the recent studies involving new high-throughput genomic technologies in urological basic and clinical research, demonstrating the advances made in the urinary microbiome in its entire spectrum of pathogens and the first attempts of clinical implementation in several areas of urology. Finally, this paper discusses the challenges that must be overcome for such technology to become widely used in clinical practice