Web-based expert systems: benefits and challenges

Perspectives on Preparedness for Chemical, Biological, Radiological, and Nuclear Threats in the Middle East and North Africa Region: Application of Artificial Intelligence Techniques

Over the past 3 decades, the diversity of ethnic, religious, and political backgrounds worldwide, particularly in countries of the Middle East and North Africa (MENA), has led to an increase in the number of intercountry conflicts and terrorist attacks, sometimes involving chemical and biological agents. This warrants moving toward a collaborative approach to strengthening preparedness in the region. In disaster medicine, artificial intelligence techniques have been increasingly utilized to allow a thorough analysis by revealing unseen patterns. In this study, the authors used text mining and machine learning techniques to analyze open-ended feedback from multidisciplinary experts in disaster medicine regarding the MENA region's preparedness for chemical, biological, radiological, and nuclear (CBRN) risks. Open-ended feedback from 29 international experts in disaster medicine, selected based on their organizational roles and contributions to the academic field, was collected using a modified interview method between October and December 2022. Machine learning clustering algorithms, natural language processing, and sentiment analysis were used to analyze the data gathered using R language accessed through the RStudio environment. Findings revealed negative and fearful sentiments about a lack of accessibility to preparedness information, as well as positive sentiments toward CBRN preparedness concepts raised by the modified interview method. The artificial intelligence analysis techniques revealed a common consensus among experts about the importance of having accessible and effective plans and improved health sector preparedness in MENA, especially for potential chemical and biological incidents. Findings from this study can inform policymakers in the region to converge their efforts to build collaborative initiatives to strengthen CBRN preparedness capabilities in the healthcare sector.

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

Developing a web-based advisory expert system for implementing traffic calming strategies

Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented.

Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review

Background

Greater use of computerized decision support (DS) systems could address continuing safety and quality problems in healthcare, but the healthcare field has struggled to implement DS technology. This study surveys DS experience across multiple non-healthcare disciplines for new insights that are generalizable to healthcare provider decisions. In particular, it sought design principles and lessons learned from the other disciplines that could inform efforts to accelerate the adoption of clinical decision support (CDS).

Methods

Our systematic review drew broadly from non-healthcare databases in the basic sciences, social sciences, humanities, engineering, business, and defense: PsychINFO, BusinessSource Premier, Social Sciences Abstracts, Web of Science, and Defense Technical Information Center. Because our interest was in DS that could apply to clinical decisions, we selected articles that (1) provided a review, overview, discussion of lessons learned, or an evaluation of design or implementation aspects of DS within a non-healthcare discipline and (2) involved an element of human judgment at the individual level, as opposed to decisions that can be fully automated or that are made at the organizational level.

Results

Clinical decisions share some similarities with decisions made by military commanders, business managers, and other leaders: they involve assessing new situations and choosing courses of action with major consequences, under time pressure, and with incomplete information. We identified seven high-level DS system design features from the non-healthcare literature that could be applied to CDS: providing broad, system-level perspectives; customizing interfaces to specific users and roles; making the DS reasoning transparent; presenting data effectively; generating multiple scenarios covering disparate outcomes (e.g., effective; effective with side effects; ineffective); allowing for contingent adaptations; and facilitating collaboration. The article provides examples of each feature. The DS literature also emphasizes the importance of organizational culture and training in implementation success. The literature contrasts “rational-analytic” vs. “naturalistic-intuitive” decision-making styles, but the best approach is often a balanced approach that combines both styles. It is also important for DS systems to enable exploration of multiple assumptions, and incorporation of new information in response to changing circumstances.

Conclusions

Complex, high-level decision-making has common features across disciplines as seemingly disparate as defense, business, and healthcare. National efforts to advance the health information technology agenda through broader CDS adoption could benefit by applying the DS principles identified in this review.

Web-based expert systems and services

Web-based expert systems (WBESs) provide the benefits of both expert system technology and web technology. The use of web services to deliver functionalities of WBESs allows the integration of these systems in web-portals. WBESs are used in a diversity of areas like engineering, management, medicine, agriculture, education, tourism, finance etc. A study on the various features of WBESs like knowledge-representation, reasoning, languages, implementation tools, use of various web services-related processes such as discovery, selection, composition etc. can result into some interesting conclusions. Presented work tries to fulfill the same purpose. The paper presents various WBESs classified according to their use of domain. The comparisons, observations, and assessments of these systems are presented by emphasizing the above-mentioned features of WBESs. A discussion on different web services-related processes and some representative techniques for performing each has also been provided to clarify their use in the WBESs. On the basis of assessments and surveys from different perspectives, some remarkable conclusions are drawn.