Knowledge-based Artificial Intelligence Research Articles

Human Knowledge-based Artificial Intelligence Methods for Skin Cancer Management: Accuracy and Interpretability Study

Human Knowledge-based Artificial Intelligence Methods for Skin Cancer Management: Accuracy and Interpretability Study

Developing and validating a knowledge-based AI assessment system for learning clinical core medical knowledge in otolaryngology

BackgroundClinical core medical knowledge (CCMK) learning is essential for medical trainees. Adaptive assessment systems can facilitate self-learning, but extracting experts' CCMK is challenging, especially using modern data-driven artificial intelligence (AI) approaches (e.g., deep learning). ObjectivesThis study aims to develop a multi-expert knowledge–aggregated adaptive assessment scheme (MEKAS) using knowledge-based AI approaches to facilitate the learning of CCMK in otolaryngology (CCMK-OTO) and validate its effectiveness through a one-month training program for CCMK-OTO education at a tertiary referral hospital. MethodsThe MEKAS utilized the repertory grid technique and case-based reasoning to aggregate experts' knowledge to construct a representative CCMK base, thereby enabling adaptive assessment for CCMK-OTO training. The effects of longitudinal training were compared between the experimental group (EG) and the control group (CG). Both groups received a normal training program (routine meeting, outpatient/operation room teaching, and classroom teaching), while EG received MEKAS for self-learning. The EG comprised 22 UPGY trainees (6 postgraduate [PGY] and 16 undergraduate [UGY] trainees) and 8 otolaryngology residents (ENT-R); the CG comprised 24 UPGY trainees (8 PGY and 16 UGY trainees). The training effectiveness was compared through pre- and post-test CCMK-OTO scores, and user experiences were evaluated using a technology acceptance model-based questionnaire. ResultsBoth UPGY (z = −3.976, P < 0.001) and ENT-R (z = −2.038, P = 0.042) groups in EG exhibited significant improvements in their CCMK-OTO scores, while UPGY in CG did not (z = −1.204, P = 0.228). The UPGY group in EG also demonstrated a substantial improvement compared to the UPGY group in CG (z = −4.943, P < 0.001). The EG participants were highly satisfied with the MEKAS system concerning self-learning assistance, adaptive testing, perceived satisfaction, intention to use, perceived usefulness, perceived ease of use, and perceived enjoyment, rating it between an overall average of 3.8 and 4.1 out of 5.0 on all scales. ConclusionsThe MEKAS system facilitates CCMK-OTO learning and provides an efficient knowledge aggregation scheme that can be applied to other medical subjects to efficiently build adaptive assessment systems for CCMK learning. Larger-scale validation across diverse institutions and settings is warranted further to assess MEKAS's scalability, generalizability, and long-term impact.

Effectiveness of designing a knowledge-based artificial intelligence chatbot system into a nursing training program: A quasi-experimental design

BackgroundChatbots have gained popularity in the healthcare industry due to their ability to provide prompt and accurate responses to a wide range of inquiries. This has been particularly beneficial for nurses who often require quick access to information and may have questions related to patient care. PurposeTo evaluate the effectiveness of designing a knowledge-based artificial intelligence chatbot system for a nursing training program. MethodsThe study utilized a Quasi-experimental design and collected data from a purposive sample of 73 nurses using Google Forms. The tools used in the study included 1) a structured questionnaire for nurses (a) Demographic data (b) Nurses' knowledge regarding chatbots, 2) Nurses' knowledge about artificial intelligence, 3) Nurses' perception of the application of chatbots in nursing, and 4) Nurses' opinions about the use of nursing chatbots or traditional methods of education. ResultsThere was a highly statistically significant improvement in nurses' knowledge-based chatbot systems post-intervention (p = 0.001). ConclusionIntegrating an artificial intelligence chatbot system into a nursing training program provides nurses with easy access to reliable and evidence-based knowledge. The chatbot offers immediate answers, explanations, and up-to-date resources, empowering nurses to make informed decisions, stay updated, and served as a communication platform connecting nurses through a common language and enhance their practice. RecommendationIncorporating a knowledge-based chatbot system into a nursing training program. Furthermore, further research should be conducted to explore the long-term impact of chatbot systems on nursing practices and education.

A Knowledge-Based AI Framework for Mobility as a Service

Mobility as a Service (MaaS) combines various modes of transportation to present mobility services to travellers based on their transport needs. This paper proposes a knowledge-based framework based on Artificial Intelligence (AI) to integrate various mobility data types and provide travellers with customized services. The proposed framework includes a knowledge acquisition process to extract and structure data from multiple sources of information (such as mobility experts and weather data). It also adds new information to a knowledge base and improves the quality of previously acquired knowledge. We discuss how AI can help discover knowledge from various data sources and recommend sustainable and personalized mobility services with explanations. The proposed knowledge-based AI framework is implemented using a synthetic dataset as a proof of concept. Combining different information sources to generate valuable knowledge is identified as one of the challenges in this study. Finally, explanations of the proposed decisions provide a criterion for evaluating and understanding the proposed knowledge-based AI framework.

New directions for applied knowledge-based AI and machine learning

In this article, selected new directions in knowledge-based artificial intelligence (AI) and machine learning (ML) are presented: ontology development methodologies and tools, automated engineering of WordNets, innovations in semantic search, and automated machine learning (AutoML). Knowledge-based AI and ML complement each other ideally, as their strengths compensate for the weaknesses of the other discipline. This is demonstrated via selected corporate use cases: anomaly detection, efficient modeling of supply networks, circular economy, and semantic enrichment of technical information.

FPIRPQ: Accelerating regular path queries on knowledge graphs

With the growing popularity and application of knowledge-based artificial intelligence, the scale of knowledge graph data is dramatically increasing. As an essential type of query for RDF graphs, Regular Path Queries (RPQs) have attracted increasing research efforts, which explore RDF graphs in a navigational manner. Moreover, path indexes have proven successful for semi-structured data management. However, few techniques can be used effectively in practice for processing RPQ on large-scale knowledge graphs. In this paper, we propose a novel indexing solution named FPIRPQ (Frequent Path Index for Regular Path Queries) by leveraging Frequent Path Mining (FPM). Unlike the existing approaches to RPQs processing, FPIRPQ takes advantage of frequent paths, which are statistically derived from the data to accelerate RPQs. Furthermore, since there is no explicit benchmark targeted for RPQs over RDF graph yet, we design a micro-benchmark including 12 basic queries over synthetic and real-world datasets. The experimental results illustrate that FPIRPQ improves the query efficiency by up to orders of magnitude compared to the state-of-the-art RDF storage engine.

Vessel-GAN: Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks

Dynamic CT angiography derived from CT perfusion data can obviate a separate coronary CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing patient safety. However, the image quality of dynamic CT angiography is inferior to standard CT angiography images in many studies. This paper proposes an explainable generative adversarial network named vessel-GAN, which resorts to explainable knowledge-based artificial intelligence to perform image translation with increased trustworthiness. Specifically, we design a loss term to better learn the representations of blood vessels in CT angiography images. The loss term based on expert knowledge guides the generator to focus its training on the important features predicted by the discriminator. Additionally, we propose a generator architecture that effectively fuses spatio-temporal representations and further enhances temporal consistency, thereby improving the quality of the generated CT angiography images. The experiment is conducted on a dataset consisting of 232 patients with suspected coronary artery stenosis. Experimental results show that the PSNR value of vessel-GAN is 28.32 dB, SSIM value is 0.91 and MAE value is 47.36. To validate the effectiveness of the proposed synthesis method, we compare that with other image translation frameworks and GAN-based methods. Compared to other image translation methods, the proposed method vessel-GAN can generate more clearly visible blood vessels from source perfusion images. The CTA images generated by vessel-GAN are closer to the real CTA due to the use of adversarial learning. Compared with other GAN-based methods, vessel-GAN can produce sharper and more homogeneous outputs, including realistic vascular structures. The experiment demonstrates that the explainable generative adversarial network has superior performance for it can better control how models learn. Overall, the CT angiography images generated by vessel-GAN can potentially replace a separate standard CT angiography, allowing the possibility of “one-stop” cardiac examination for high-risk coronary artery disease patients who need assessment of myocardial ischemia.

On-Vehicle Danger Forecast System based on Knowledge-based Artificial Intelligence

On-Vehicle Danger Forecast System based on Knowledge-based Artificial Intelligence

Design of an Online Course on Knowledge-Based AI

In Fall 2014 we offered an online course on Knowledge-Based Artificial Intelligence (KBAI) to about 200 students as part of the Georgia Tech Online MS in CS program. By now we have offered the course to more than 1000 students. We describe the design, development and delivery of the online KBAI class in Fall 2014.

Decomposition of Surface EMG Signals

This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to > 97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically < or = 5 MUs per contraction. Both the accuracy and the yield should increase as the algorithms are developed further. With this technique it is possible to investigate the behavior of MUs in muscles that are not easily studied by needle sensors. We found that the inverse relationship between the recruitment threshold and the firing rate previously reported for muscles innervated by spinal nerves is also present in the orbicularis oculi and the platysma, which are innervated by cranial nerves. However, these two muscles were found to have greater and more widespread values of firing rates than those of large limb muscles.

Adaptive Behavior in Autonomous Agents

This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that, in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on lifelike qualities in both agents and environments.

Development of a health promotion system for the elderly: Committee of Health Evaluation for Elderly Persons Council of Japan AMHTS Institutions.

A new health promotion approach for elderly persons is required which maintains not only their physical health but also their quality of life. We are developing a health promotion system which makes use of questionnaires dealing with physical conditions and quality of life, and makes health reports. Health evaluation is carried out in three steps. First, detailed information about the physical health of each client is collected. Second, quality of life is evaluated according to five health indicators. Last, health recommendations are generated. An artificial intelligence (AI) program produces detailed questions to collect necessary information for the evaluation of a client's health. The information related to quality of life is converted into five health indicators and presented as a radar-chart in documents and displays. The knowledge-based AI program automatically generates the health recommendation documents. This information is available for physicians and nurses for health counseling.

Three-dimensional visualization of mission planning and control for the NPS autonomous underwater vehicle

The Naval Postgraduate School (NPS) is constructing a small autonomous underwater vehicle (AUV) with an onboard mission control computer. The mission controller software for this vehicle is a knowledge-based artificial intelligence (AI) system requiring thorough analysis and testing before the AUV is operational. The manner in which rapid prototyping of this software has been demonstrated by developing a controller code on a LISP machine and using an Ethernet link with a graphics workstation to simulate the controller's environment is discussed. The development of a testing simulator using a knowledge engineering environment (KEE) expert system shell that examines AUV controller subsystems and vehicle models before integrating them with the full AUV for its test environment missions is discussed. This AUV simulator utilizes an interactive mission planning control console and is fully autonomous once initial parameters are selected.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Application of the EXPERT consultation system to accelerated laboratory testing and interpretation.

The EXPERT consultation system-building tool, a knowledge-based artificial intelligence program developed at Rutgers University, has been applied to the development of a laboratory consultation system facilitating sequential laboratory testing and interpretation. Depending on the results of a basic panel of laboratory tests, the system requests that specific secondary tests be performed. Input of these secondary findings can result in requests for tertiary testing, to complete the database necessary for interpretation. Interpretation of all results is based upon final inferences from the collected findings through a series of rules, a hierarchical network that yields an efficient production system not easily obtained through conventional programming. The rules included in this model are based upon initial results for total protein, calcium, glucose, total bilirubin, alkaline phosphatase, lactate dehydrogenase, aspartate aminotransferase, thyroxin, hemoglobin, mean corpuscular volume, and the concentrations of four drugs. Pertinent clinical history items included are jaundice, diabetes, thyroid disease, medications, and ethanol. Implementing this system in a laboratory-based accelerated testing program involving outpatients maximized the effective use of laboratory resources, eliminated useless testing, and provided the patient with low-cost laboratory information.

Functionally Accurate, Cooperative Distributed Systems

A new approach for structuring distributed processing systems, called functionally accurate, cooperative (FA/C), is proposed. The approach differs from conventional ones in its emphasis on handling distribution-caused uncertainty and errors as an integral part of the network problem-solving process. In this approach nodes cooperatively problem-solve by exchanging partial tentative results (at various levels of abstraction) within the context of common goals. The approach is especially suited to applications in which the data necessary to achieve a solution cannot be partitioned in such a way that a node can complete a task without seeing the intermediate state of task processing at other nodes. Much of the inspiration for the FA/C approach comes from the mechanisms used in knowledge-based artificial intelligence (Al) systems for resolving uncertainty caused by noisy input data and the use of approximate knowledge. The appropriateness of the FA/C approach is explored in three application domains: distributed interpretation, distributed network traffic-light control, and distributed planning. Additionally, the relationship between the approach and the structure of management organizations is developed. Finally, a number of current research directions necessary to more fully develop the FA/C approach are outlined. These research directions include distributed search, the integration of implicit and explicit forms of control, and distributed planning and organizational self-design.