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PubMed2001

On conceptualization of a decision support system in health informatics.

A decision support system can be approached from two major disciplinary perspectives, those of information systems science (IS) and artificial intelligence (AI). We present in this study an extended ontology for a decision support system in health informatics, which is founded on experience from related research fields as well as being informed by our case studies. The ontology emphasises the need to cover environmental and contextual variables as an integral part of a decision support systems development methodology. With the addition of these variables, the focus in decision support systems development shifts from a task ontology towards a domain ontology. The results of this study help the system developers to take the system's context into account through the set of defined variables that are linked to the application domain. These variables explicate relevant constructs and present a vocabulary for a decision support system. However, applying the ontology requires a more thorough analysis of the domain and therefore more qualified resources for systems development. This indicates the need to focus more on education and training in health informatics.

Artificial Intelligence↗
PubMedDate not supplied

[Analysis and cognitive modelling of the analogical process in psychosis].

The disturbances of cognitive processes in psychotic patients are well known: the delusional interpretation is "the inference from a right perception into a wrong concept" (Dromard), "an wrong intuition about the meaning of what is perceived, seen or heard" (H. Ey). Analogy is the very core of any cognitive process: relating a strange thing to some object already part of the experience enables to set up differences, oppositions, connections, classes. Any semantic process (something stands for another thing) originates in analogy. It is the basis of every interpretation and world's knowledge. Its soundness is by no means reliable, but for the inner strength of the analogical network and its power to integrate new objects. It's easy to fall out of the track... A wrong analogy, better, a wrong one that would not be acknowledged as a mistake, would be enough: the gap is quite narrow between interpretations leading either to understanding or to misreading, only filled through the relation of other people providing the necessary clues. The contemporary papers about cognitive process are driving towards two main trends: 1) Neuromimetic models, and the building of neuronal networks, whose emergent properties point out the basically analogical character of representations, learning and memory. 2) Cognitive models, dealing with representations and algorithms, and leading to Artificial Intelligence Programs. We tried to build a model (both cognitive and AI) of the analogical process and its psychotic disturbances. Our model describes how simple analogical problems are solved: If (A) becomes (B), what about (C)? Making up the psychological model and its AI translation led to propound the concept of Universes as sets consisting of ONE likely or relevant link between two objects, and such intrinsic of extrinsic properties of the objects as are involved in this relation. The model uses 3 different universes: Universe U1, made up of one of the possible transformation kinks from (A) to (B) and (A)'s properties involved in this actual transformation. Universe U2, made out of the likeness link between Universe U1 and (C). Universe U3, performing in fact the validation procedure of the result. The analogical reasoning goes through the three universes, along an iterative loop again and again until a nice result is found.(ABSTRACT TRUNCATED AT 400 WORDS)

Artificial Intelligence↗
PubMed2000

GALEN: a third generation terminology tool to support a multipurpose national coding system for surgical procedures.

Generalised architecture for languages, encyclopedia and nomenclatures in medicine (GALEN) has developed a new generation of terminology tools based on a language independent model describing the semantics and allowing computer processing and multiple reuses as well as natural language understanding systems applications to facilitate the sharing and maintaining of consistent medical knowledge. During the European Union 4 Th. framework program project GALEN-IN-USE and later on within two contracts with the national health authorities we applied the modelling and the tools to the development of a new multipurpose coding system for surgical procedures named CCAM in a minority language country, France. On one hand, we contributed to a language independent knowledge repository and multilingual semantic dictionaries for multicultural Europe. On the other hand, we support the traditional process for creating a new coding system in medicine which is very much labour consuming by artificial intelligence tools using a medically oriented recursive ontology and natural language processing. We used an integrated software named CLAW (for classification workbench) to process French professional medical language rubrics produced by the national colleges of surgeons domain experts into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation, on one hand, we generate with the LNAT natural language generator controlled French natural language to support the finalization of the linguistic labels (first generation) in relation with the meanings of the conceptual system structure. On the other hand, the Claw classification manager proves to be very powerful to retrieve the initial domain experts rubrics list with different categories of concepts (second generation) within a semantic structured representation (third generation) bridge to the electronic patient record detailed terminology.

Abstracting and Indexing↗
PubMed1990

Computer-assisted adult medical diagnosis: subject review and evaluation of a new microcomputer-based system.

Three decades after the conceptual foundation was laid for computer-aided diagnosis, some of its potential has been realized. Systems based on probabilistic reasoning have been developed and applied within limited domains (e.g., acute abdominal pain) and for the general diagnosis of systemic disorders. Less progress has been made in the development and application of diagnostic systems based on "artificial intelligence", reflecting theoretical limits to this application of computers to medicine and the enormity of the task. The presently available probabilistic systems have recently been joined by a new microcomputer-based system, MEDITEL Computer-Assisted Diagnosis, Adult System. The performance of this system was evaluated with both clinical-pathologic conference cases and consecutive admissions with undiagnosed illnesses. The correct diagnosis appeared on the list generated by the system in 80 to 90% of the cases. Experience with this and other systems illustrates current issues in the evaluation of computer systems for aid in diagnosis and of computer-based medical "expert" systems in general. These issues include physician acceptance of these systems and the ethical, legal, and regulatory aspects of computer system application. We conclude that, in appropriately selected cases, the accuracy and efficiency of physician diagnosis can be enhanced with computer assistance, and the risk of overlooking the correct diagnosis can be reduced.

Artificial Intelligence↗
PubMed1991

Drug product distribution systems and departmental operations.

Technologies affecting institutional pharmacy practice and the operation of pharmacy departments are reviewed, future developments are outlined, and implications of these developments for pharmacy education are proposed. Computer technology, especially as applied to areas such as artificial intelligence, online information databases, electronic bulletin boards, hospital information systems, and point-of-care systems, will have a strong impact on pharmacy practice and management in the 1990s. Other areas in which growth is likely to be active include bar-code technology, robotics, and automated drug dispensing. The applications of these technologies are described, with particular attention placed on the effects of increased automation on the drug-dispensing function. Technological advances may effect marked reductions in dispensing and medication errors; questions concerning the cost-effectiveness of these new systems remain to be answered. These advances also create new opportunities for clinical involvement by pharmacists; however, a fundamental understanding of computer systems is essential. Current practitioners can benefit from attending seminars, participating in users' groups, and keeping current with the computer literature. Many students now acquire the needed skills in computer laboratories and in the classroom. Technological advances will offer the opportunity for pharmacists to expand their clinical role.

Artificial Intelligence↗
PubMed2005

Conceptual framework of knowledge management for ethical decision-making support in neonatal intensive care.

This research is built on the belief that artificial intelligence estimations need to be integrated into clinical social context to create value for health-care decisions. In sophisticated neonatal intensive care units (NICUs), decisions to continue or discontinue aggressive treatment are an integral part of clinical practice. High-quality evidence supports clinical decision-making, and a decision-aid tool based on specific outcome information for individual NICU patients will provide significant support for parents and caregivers in making difficult "ethical" treatment decisions. In our approach, information on a newborn patient's likely outcomes is integrated with the physician's interpretation and parents' perspectives into codified knowledge. Context-sensitive content adaptation delivers personalized and customized information to a variety of users, from physicians to parents. The system provides structuralized knowledge translation and exchange between all participants in the decision, facilitating collaborative decision-making that involves parents at every stage on whether to initiate, continue, limit, or terminate intensive care for their infant.

Artificial Intelligence↗
PubMed1997

The use of knowledge-based systems in medicine in developing countries: a luxury or a necessity?

Knowledge-based systems (KBSs) in medicine have received much attention over the past two decades, mainly because of the potential benefits that can be gained from using them. They may facilitate in increasing productivity in a medical environment, support the making of diagnoses and other types of medical decisions, assist in the training of medical professionals, and can even handle some routine tasks in a medical environment. However, some critical problems in this field have also been identified. For example, research indicated that some problems can be solved partially, but not completely, with existing artificial intelligence techniques. Another problem is that many of the existing medical information systems do not support the integration of KBSs in a natural way. Furthermore, the routine use of a medical KBS is complicated by legal issues. These and other problems contribute to what we experience today: a large proportion of the medical KB applications that are developed is never actually used in practice. This justifies questions such as: Should developing countries, having limited infrastructure and research resources, invest in medical KBSs research and development, or should this field be regarded as a luxury that only belongs to developed countries?, and: Can developing countries really benefit from the use of these systems? These questions are discussed in this paper. We highlight the main problems surrounding the development and use of medical KBSs. With the focus on developing countries we discuss potential benefits that could be obtained by investing in these systems and we offer guidelines for focusing research and development of medical KBSs.

Artificial Intelligence↗
PubMedDate not supplied

Automated rule-based decision systems in forensic toxicology using expert knowledge: basic principles and practical applications.

This paper presents the basic principles and practical benefits of the application of expert systems (ES) and artificial intelligence (AI) to problem solving in forensic toxicology. We acknowledge the complexity and elegance of the theoretical substance and program algorithms of existing work in these disciplines, while simultaneously observing that many presentations of this material cloak the essential facts and concepts in unnecessary jargon and hyperbole. We attempt to remove the cloak without misrepresenting or oversimplifying the underlying structures. We first present a summary of the history, basic functions, technical fundamentals, and typical applications in three major categories of established ES/AI systems. We then assess the status of ES/AI in the forensic toxicology laboratory (FTL) with emphasis on potential applications. We conclude with an analysis of experiences with ESs in our laboratory where we have used an integrated expert system to reduce laboratory errors, detect internal inconsistencies in data, discover new substance abuse subpopulations, and reduce the frequency of sample reprocessing. We have minimized specimen processing time and instrument wear while maximizing technician efficiency and thus performing more tests for the same or reduced costs.

Artificial Intelligence↗
PubMed1992

Applications of rule-induction in the derivation of quantitative structure-activity relationships.

Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.

Algorithms↗
PubMed1994

Case-based reasoning and imaging procedure selection.

RATIONALE AND OBJECTIVES: Case-based reasoning, an artificial intelligence technique for learning and reasoning from experience, has shown great potential for use in decision support systems. The authors developed and tested a prototype case-based decision support system to explore the applicability of this technique to the selection of diagnostic imaging procedures. METHODS: A case-based system, ProtoISIS, was developed based on the Protos learning apprentice. ProtoISIS learned the domain of ultrasonography and body computed tomography by reviewing 200 consecutive cases of actual requests for imaging procedures. ProtoISIS was tested by using it to classify four sets of 25 cases of actual imaging procedure requests. RESULTS: ProtoISIS correctly classified 72% of the imaging-procedure requests. Its performance improved as it gained experience: in the last two test series, it correctly classified 84% of the cases presented. CONCLUSIONS: Case-based reasoning can be applied successfully to the selection of diagnostic imaging procedures and holds potential for use in clinical decision support aids. Further work is necessary to realize a clinically useful system.

Artificial Intelligence↗
PubMed1999

Machine learning methods applied on dental fear and behavior management problems in children.

The etiologies of dental fear and dental behavior management problems in children were investigated in a database of information on 2,257 Swedish children 4-6 and 9-11 years old. The analyses were performed using computerized inductive techniques within the field of artificial intelligence. The database held information regarding dental fear levels and behavior management problems, which were defined as outcomes, i.e. dependent variables. The attributes, i.e. independent variables, included data on dental health and dental treatments, information about parental dental fear, general anxiety, socioeconomic variables, etc. The data contained both numerical and discrete variables. The analyses were performed using an inductive analysis program (XpertRule Analyser, Attar Software Ltd, Lancashire, UK) that presents the results in a hierarchic diagram called a knowledge tree. The importance of the different attributes is represented by their position in this diagram. The results show that inductive methods are well suited for analyzing multifactorial and complex relationships in large data sets, and are thus a useful complement to multivariate statistical techniques. The knowledge trees for the two outcomes, dental fear and behavior management problems, were very different from each other, suggesting that the two phenomena are not equivalent. Dental fear was found to be more related to non-dental variables, whereas dental behavior management problems seemed connected to dental variables.

Artificial Intelligence↗
PubMed2002

Problem solving as model refinement: towards a constructivist epistemology.

In recent years the Artificial Intelligence research group at the University of New Mexico have considered several areas of problem solving in interesting and complex domains. These areas have ranged from the low level explorations of a robot tasked to explore, map, and use a new environment to the development of very sophisticated control algorithms for the optimal use of particle beam accelerators. Although the results of our research have been reflected in computer-based problem solvers, such as the robot discovering and mapping out its world, these computational tasks are in many ways similar to expert human performance in similar tasks. In fact, in many important ways our computer-based approach mimics human expert performance in such domains. This paper describes three of these task domains as well as the software algorithms that have achieved competent performances therein. We conclude this paper with some comments on how software models of a domain can elucidate aspects of intellectual performance within that context. Furthermore, we demonstrate how exploratory problem solving along with model refinement algorithms can support a constructivist epistemology.

Artificial Intelligence↗
PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗
PubMed2024

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Humans↗
PubMed1998

Unified modeling language and design of a case-based retrieval system in medical imaging.

One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users.

Artificial Intelligence↗
PubMed2004

Combining FDI and AI approaches within causal-model-based diagnosis.

This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.

Algorithms↗
PubMed2002

Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.

The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity. In this study, we used a protein biochip surface enhanced laser desorption/ionization mass spectrometry approach coupled with an artificial intelligence learning algorithm to differentiate PCA from noncancer cohorts. Surface enhanced laser desorption/ionization mass spectrometry protein profiles of serum from 167 PCA patients, 77 patients with benign prostate hyperplasia, and 82 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that used a nine-protein mass pattern that correctly classified 96% of the samples. A blinded test set, separated from the training set by a stratified random sampling before the analysis, was used to determine the sensitivity and specificity of the classification system. A sensitivity of 83%, a specificity of 97%, and a positive predictive value of 96% for the study population and 91% for the general population were obtained when comparing the PCA versus noncancer (benign prostate hyperplasia/healthy men) groups. This high-throughput proteomic classification system will provide a highly accurate and innovative approach for the early detection/diagnosis of PCA.

Aged↗
PubMed1998

The decision support system for telemedicine based on multiple expertise.

This paper discusses the application of artificial intelligence in telemedicine and some of our research results in this area. The main goal of our research is to develop methods and systems to collect, analyse, distribute and use medical diagnostics knowledge from multiple knowledge sources and areas of expertise. Use of modern communication tools enable a physician to collect and analyse information obtained from experts worldwide with the help of a decision support medical system. In this paper we discuss a multilevel representation and processing of medical data using a system which evaluates and exploits knowledge about the behaviour of statistical diagnostics methods. The presented technique is able to acquire semantically-essential information from the complex dynamics of quasi-periodical medical signals by applying recursively-ordinary statistical tools. A method and an algorithm are elaborated to select automatically the most appropriate diagnostics method for each case under consideration. We suggest the use of a voting-type technique to search for consensus among the different opinions of medical experts. Research results can be applied in the development of a telediagnostics expert medical system and medical teleconsulting support system.

Algorithms↗