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PubMed1995

MONI: an intelligent database and monitoring system for surveillance of nosocomial infections.

Recording, recognition, and prevention of nosocomial infections are the primary responsibilities of the hospital infection control unit. To perform these tasks, this unit needs information from diverse sources--the patient's symptoms and signs, microbiological and virological test results, and information regarding antibiotics and treatment come from different levels of healthcare delivery. Because of the large amount of data (e.g., about 300 microbiological requests daily) a computer system is required to store this information and to provide a means for subsequent evaluation. MONI (Monitoring of nosocomial infections) is an intelligent database and monitoring system for surveillance and detection of nosocomial infections. Data can be entered into the system manually as well as transferred automatically from external information systems. The central feature of the system is the automatic detection of and calling attention to conditions that may be a detriment to patient recovery, such as possible hospital-acquired infections, risk factors, diseases to be reported, etc. By using this system, we seek to reduce the frequency of infection and the frequency of nosocomial deaths by improving the quality of patient treatment, shortening the length of stay in a hospital, and the use of fewer and/or cheaper antibiotics. MONI provides a means to access relevant medical data (names of infectious agents, antibiotics, department names, monitoring rules, etc.) from a library. This library can be updated or otherwise modified, even during use. An infection control team using this system can customize it to suit the demands of that particular unit. Automatic data transfer from external information systems is made possible by tables that translate between different code systems. The system also offers flexibility; the program can be configured to adapt it for use in other hospitals and institutions. The core element of MONI is the monitoring module, which is implemented as a layer between data input and the database. Upon data acquisition, the system checks the input against several monitoring tools and alerts the user to matches, which may indicate an infection risk. Processing of a rule may be deferred, depending on complexity of the rule and the actual and estimated workload of the system. Examples of the monitoring guidelines are: (1) suspicion of nosocomial infection; (2) infection at a normally sterile site; (3) infection due to bacteria with unusual antibiotic sensitivity patterns; (4) lab report indicates that patient is treated with ineffective antibiotic; (5) possible choice for less expensive antibiotic; (6) infection which is required to be reported to state and/or health authorities; (7) patients receiving prophylactic antibiotics longer than medically indicated; and (8) infections of two or more patients in different wards with the same bacteria (cf. Evans 85). The MONI system was developed at one of the largest hospitals in Europe, the Vienna General Hospital (2,200 beds). This facility serves as the teaching hospital of the University of Vienna Medical School. The size of the hospital and the large amount of data made it necessary to introduce such a system into clinical routine. MONI was programmed in C and C++ with a state-of-the-art graphical user interface (Presentation Manager, Workplace Shell) for OS/2. IBM Database 2 for OS/2 (dB 2/2) was used in constructing the database. The layer between the database and the monitoring application is driven by the multitasking and interprocess communication abilities of OS/2. A pen-based support system that assists in mobile data acquisition is currently under development.

Anti-Bacterial Agents↗
PubMedDate not supplied

Development of an expert system for haemodynamic monitoring: computerized symbolization of on-line monitoring data.

The development of intelligent alarm systems for intensive care benefits from the transformation of data from a quantitative to a qualitative mode. We constructed a computerized algorithm for the symbolization of on-line monitoring data of heart rate, systemic arterial, pulmonary arterial and central venous pressures, as well as central and peripheral temperatures. We tested the ability of the algorithm to symbolize the levels of the parameters and to detect significant long-term trends in ten adult patients admitted to the intensive care unit after cardiac surgery. The estimations of an experienced clinician were taken as the 'gold standard'. The symbolization of the levels of the monitored parameters was in agreement with the clinician in 99.4% of the estimations. The algorithm detected 93.0% of the trends correctly and also estimated their reliability. The clinician considered its estimations to be accurate in 96.2% of cases. On the other hand, the clinician considered unreliable 2.4% of all the trends detected and classified as reliable by the algorithm. The computerized algorithm for the symbolization of real-time monitoring data performed efficiently enough for its further use in expert systems for intelligent monitoring.

Algorithms↗
PubMed2026

AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.

BACKGROUND: CRISPR-based genetic screening has become a central methodology in functional genomics, enabling systematic interrogation of gene function, genetic interactions and context-dependent vulnerabilities at scale. However, the rapid expansion of screening modalities-including multi-condition designs, combinatorial perturbations, in vivo applications and single-cell readouts-has exposed fundamental limitations of heuristic-driven experimental design and post hoc statistical analysis. MAIN BODY: This Review synthesizes how artificial intelligence is reshaping CRISPR screening by introducing predictive, adaptive and system-level intelligence across the experimental lifecycle. We organize recent advances into two tightly coupled modules. First, machine learning and deep learning (ML/DL) methods optimize experimental design by learning context-dependent perturbation behavior, anticipating confounding effects and enabling iterative, information-efficient screening strategies. Second, large language model-agent (LLM-agent) systems complement these advances by externalizing scientific reasoning, integrating biological knowledge at scale and coordinating analysis and decision-making in human-in-the-loop workflows. CONCLUSIONS: Together, ML/DL and LLM-agent approaches reframe CRISPR screening from a static analytical pipeline into an intelligent experimental system, with important implications for robustness, scalability and biological discovery.

Artificial Intelligence↗
PubMed1996

Cross-scale information processing in evolution, development and intelligence.

Biological systems are treated as percolation networks in which processes at all scales participate. Macroscopic inputs are transduced to microphysical events through an interleaved hierarchy of structures and processes and microphysical events are amplified to control macroscopic structures and functions. Integrity and adaptation are achieved through self-consistency dynamics operating at all levels of organization. The unmanifest structure of the vacuum plays a vital role in these dynamics.

Biological Evolution↗
PubMed1989

Decision analysis: a framework for critical care decision assistance.

The ultimate goal of medical computer systems is to help clinicians make good decisions. Such systems must be based on sound principles. Decision analysis is a 25-year-old discipline that provides the needed rigorous foundation for decision assistance. Decision analysis comprises the philosophy, procedures, and tools that can correct the flaws in existing critical care decision-making practice. Intelligent decision systems--computer-based systems that automate decision analysis--make it practical to apply decision analysis to critical care. Orchestra is a pilot intelligent decision system (now under development) that coordinates the efforts of the critical care specialist, the bedside physician, and the bedside nurse in building decision models that can provide recommendations and insight for ventilator management decisions. Decision analysis delivered by intelligent decision systems has great potential for improving critical care decision-making.

Algorithms↗
PubMed1987

Modeling and artificial intelligence approaches to enzyme systems.

Modeling is a means of formulating and testing complex hypotheses. Useful modeling is now possible with biological laboratory microcomputers with which experimenters feel comfortable. Artificial intelligence (AI) is sufficiently similar to modeling that AI techniques, now becoming usable on microcomputers, are applicable to modeling. Microcomputer and AI applications to physiological system studies with multienzyme models and with kinetic models of isolated enzymes are described. Using an IBM PC microcomputer, we have been able to fit kinetic enzyme models; to extend this process to design kinetic experiments by determining the optimal conditions; and to construct an enzyme (hexokinase) kinetics data base. We have also used a PC to do most of the constructing of complex multienzyme models, initially with small simple BASIC programs; alternative methods with standard spreadsheet or data base programs have been defined. Formulating and solving differential equations in appropriate representational languages, and sensitivity analysis, are soon likely to be feasible with PCs. Much of the modeling process can be stated in terms of AI expert systems, using sets of rules for fitting and evaluating models and designing further experiments. AI techniques also permit critiquing and evaluating the data, experiments, and hypotheses being modeled, and can be extended to supervise the calculations involved.

Artificial Intelligence↗
PubMed1992

On a common structure of intelligence in biological and technical systems.

This is a survey of the general structure of an electronic computer-implemented, operation-oriented system, designed by the authors, which uses artificial intelligence mechanisms and is intended for the control of technical objects that function both in predictable and random environments. The system is based on processing knowledge, which is stored in a hierarchically arranged Knowledge Bank, and program mechanisms for adapting to and interacting with the External and Internal Worlds. The system has distributed program mechanisms, which are 'designed' with a constant structure. It is independent of the purpose and environment of the system operation and the specific features of the controlled object. None of the program mechanisms are concentrated in any program module. They are distributed in many modules, and therefore there is no single module responsible for the execution of a particular external function. The system is structured into separate program modules by internal procedures. The conceptual organisation of the Knowledge Base presupposes that the framework is structured according to functional, semantic and tier indications, i.e. the structured description of knowledge, by the system, of the external environment and its possible behaviour in it. The possibility of multiple use of the same elements of the lower tiers of the Knowledge Base by higher-tier elements makes the proposed Knowledge Base very efficient. If the cerebrum is considered at a structural level, there appears to be an amazing similarity between the structure and the mechanisms of the above system and the structure of the cerebral cortex, as suggested previously by Edelman and Mountcastle. Each mechanism of the cerebral cortex structure has a structural analogue in the described system.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗
PubMed1996

An expert diagnostic system based on neural networks and image analysis techniques in the field of automated cytogenetics.

In this study, we introduce an expert system for intelligent chromosome recognition and classification based on artificial neural networks (ANN) and features obtained by automated image analysis techniques. A microscope equipped with a CCTV camera, integrated with an IBM-PC compatible computer environment including a frame grabber, is used for image data acquisition. Features of the chromosomes are obtained directly from the digital chromosome images. Two new algorithms for automated object detection and object skeletonizing constitute the basis of the feature extraction phase which constructs the components of the input vector to the ANN part of the system. This first version of our intelligent diagnostic system uses a trained unsupervised neural network structure and an original rule-based classification algorithm to find a karyotyped form of randomly distributed chromosomes over a complete metaphase. We investigate the effects of network parameters on the classification performance and discuss the adaptability and flexibility of the neural system in order to reach a structure giving an output including information about both structural and numerical abnormalities. Moreover, the classification performances of neural and rule-based system are compared for each class of chromosome.

Algorithms↗
PubMed1989

Self-organisation and living systems: Is DNA an 'artificial intelligence'?

There seems little doubt that the maintenance and development of living systems is crucially dependent on an internal organisation of monumental complexity--particularly in higher living species. It is suggested that current thinking--particularly relating to the role of DNA in the total process cannot explain the underlying mechanisms and that a radical rethinking will be necessary. To this end it is proposed that DNA has a unique molecular electronic structure enabling it to operate as a computer analogue system for the highly efficient storage of information and as a type of artificial intelligence through which the information is translated and implemented to organise and control all aspects of the construction and activity of living systems.

Artificial Intelligence↗
PubMed1987

Validation of the pediatric speech intelligibility test in children with central nervous system lesions.

The pediatric speech intelligibility (PSI) test was administered to 21 children with a variety of documented central nervous system (CNS) lesions. Ages ranged from 3 to 8 years. PSI test results demonstrated both high sensitivity and high specificity. Results were consistently (1) abnormal in children with lesions in areas of the brain important for auditory function (CNS auditory disorders) and (2) normal in children with lesions in areas anatomically remote from auditory nuclei and pathways (nonauditory CNS disorders).

Auditory Threshold↗
PubMed1992

An intelligent computer-assisted instruction system designed for rural health workers in developing countries.

This paper describes an intelligent computer-assisted instruction system that was designed for rural health workers in developing countries. This system, called Consult-EAO, includes an expert module and a coaching module. The expert module, which is derived from the knowledge-based decision support system Tropicaid, covers most of medical practice in developing countries. It allows for the creation of outpatient simulations without the help of a teacher. The student may practice his knowledge by solving problems with these simulations. The system gives some initial facts and controls the simulation during the session by guiding the student toward the most efficient decisions. All student answers are analyzed and, if necessary, criticized. The messages are adapted to the situation due to the pedagogical rules of the coaching module. This system runs on PC-compatible computer.

Computer Simulation↗
PubMed1992

Diagnosis of periodontitis by physical measurement: interpretation from episodic disease hypothesis.

Physical measurements including the evaluation of probing depth, bleeding on probing, tooth mobility, and inflammation form the basis for most periodontal diagnostics in use today. The interpretation of these observations and the methods available for their measurement, however, have begun to change significantly. The episodic disease activity concept has done much to implement these changes. Observation of episodic attachment loss has been correlated with parallel radiographic changes, alteration in levels of probable pathogens, and changes in inflammatory mediator levels. The failure of pocket depth, suppuration, and bleeding on probing to predict episodic attachment loss has been given plausible explanations and enhanced meanings. Although attachment loss by a continuous process cannot be excluded in some disease conditions, the hypothesis of periodontal disease progression by episodic activity supplements and expands understanding of the disease process. Interest in periodontal diagnostics has accelerated in the last decade. As a parallel development, the technology of small computers has decreased in cost and increased in sophistication. The combination of these factors has created an environment for the development of intelligent diagnostic systems. Four commercially available systems and two systems under development are described. The systems, which measure pocket depth, pocket depth or attachment level, tooth mobility, and pocket temperature, all utilize computer processing of measurements. The result is to provide a simplified and more meaningful presentation of diagnostic information. As intelligent diagnostic systems prove themselves, some of these instruments are likely to become common to dental practice. The promise of more accurate identification of areas of the mouth that are diseased can increase both the efficiency and effectiveness of periodontal therapy.

Humans↗
PubMed1989

A knowledge-based information system for monitoring drug levels.

The expert system shell SMR has been enhanced to include information system routines for designing data screens and providing facilities for data entry, storage, retrieval, queries and descriptive statistics. The data for inference making is abstracted from the data base record and inserted into a data array to which the knowledge base is applied to derive the appropriate advice and comments. The enhanced system has been used to develop an intelligent information system for monitoring serum drug levels which includes evaluation of temporal changes and production of specialized printed reports. The module for digoxin has been fully developed and validated. To demonstrate the extension to other drugs a module for phenytoin was constructed with only a rudimentary knowledge base. Data from the request forms together with the S-digoxin results are entered into the data base by the department secretary. The day's results are then reviewed by the clinical pharmacologist. For each case, previous results may be displayed and are taken into account by the system in the decision process. The knowledge base is applied to the data to formulate an evaluative comment on the report returned to the requestor. The report includes a semi-graphic presentation of the current and previous results and either the system's interpretation or one entered by the pharmacologist if he does not agree with it. The pharmacologist's comment is also recorded in the data base for future retrieval, analysis and possible updating of the knowledge base. The system is now undergoing testing and evaluation under routine operations in the clinical pharmacology service. It is a prototype for other applications in both laboratory and clinical medicine currently under development at Uppsala University Hospital. This system may thus provide a vehicle for a more intensive penetration of knowledge-based systems in practical medical applications.

Data Interpretation, Statistical↗
PubMed1980

Adding information and intelligence to a family practice data system.

A critical test of any data system is its relevance; more simply, reports must present what users want. At MCV, the current system is a direct response to expressed user demands (corroborated by results of a survey of British general practitioners). That is, resident and physicians are interested in workload rates, such as visits/patient, and a delineation of diagnoses by frequency. Applications of these reports are organizational, comparative, and educational. The ultimate goal of data systems in family practice is the production of intelligence about health and health affairs: clearly, this is valuable in patient care, research, and education. Methods outlined above will contribute to achieving this end by adding information and intelligence to data systems.

Data Collection↗