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PubMed1993

Intelligence function in children with acute lymphoblastic leukemia after treatment.

In order to evaluate the effects of chemotherapy and central nervous system prophylaxis on the intelligence function of children with acute lymphoblastic leukemia (ALL), 147 patients were divided into 2 groups according to their different treatment regimens. Group A included 75 patients with ALL who were diagnosed and treated from 1981 to 1986. Group B included 72 ALL patients diagnosed and treated from 1988 to 1990. A control group included 73 healthy children who shared the same education and environmental background with the patients. Chinese revisions of Wechsler measures of intelligence quotient were used to estimate verbal IQ, performance IQ and total IQ in the 3 groups. American-produced Systate software was used to analyze the data. Results showed that IQ distribution, verbal IQ, performance IQ and total IQ of group A were obviously lower than those in the control group. Verbal IQ and total IQ of group B were also reduced. Significant differences of verbal IQ and performance IQ as well as total IQ were also found between groups A and B. Multiple regression analysis showed negative correlations between IQ and cranial irradiation dosage, times of intrathecal MTX and time after irradiation.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2004

SIVA: a hybrid knowledge-and-model-based advisory system for intensive care ventilators.

The Sheffield Intelligent Ventilator Advisor is a hybrid knowledge-and-model-based advisory system designed for intensive care ventilator management. It consists of a top-level fuzzy rule-based module to give the qualitative component of the advice, and a lower-level model-based module to give the quantitative component of the advice. It is structured to offer adaptive patient-specific decision support. It can be operated in either invasive or noninvasive modes depending on the availability of data from invasive clinical measurements. The user can choose between the full-advisory mode and the clinician-directed mode. The advice given by the top-level module has been validated against retrospective real patient data and compared with intensivists expertise and performance under simulation conditions. Closed-loop simulations were performed assuming various clinical scenarios including sudden changes in the patient parameters such as the shunt or deadspace with noise and disturbances. They have shown that the advice given was appropriate and the blood gases resulting from the closed-loop decision support were acceptable. The system was also shown to be tolerant to noise and disturbances. It is implemented in MATLAB/SIMULINK and LabVIEW.

Artificial Intelligence↗
PubMed2004

A framework and toolkit for capturing the communicable disease programmes within health systems: tuberculosis control as an illustrative example.

The frameworks and methods used for analysis, monitoring and evaluation of communicable disease control vary greatly. Although a number of manuals exist instruments for a detailed analysis of wider health system context are lacking. This is surprising given that the success of vertical programmes is often determined by the constraints of health systems. The importance of the context and the health system in determining the successful implementation of national tuberculosis programmes is well recognized by the WHO, which recommends analysis of national tuberculosis programmes within the context of health care system, health reform and the economic status of the country. However, current approaches inadequately capture intelligence on the health systems variables impacting on programme efficacy, limiting the ability of policy makers to draw lessons for wider use. A recent WHO report highlights the major systemic constraints to DOTS implementation and recommends a comprehensive and multi-sectoral approach to tuberculosis control. This obviates the need for tools that take into account health systems issues as well as focusing on a particular vertical programme but no such comprehensive tool exists. This paper outlines the conceptual basis for a model and a toolkit for rapid assessment, monitoring, and evaluation of the context, the elements of the health system and vertical communicable disease programme. It describes the framework, the potential strengths and weaknesses, approach and piloting of the toolkit and its two elements: first for 'horizontal assessment' of the health system within which the programme is embedded and second for 'vertical assessment' of the infectious disease-specific programme.

Communicable Disease Control↗
PubMed2006

Real-time face detection and lip feature extraction using field-programmable gate arrays.

This paper proposes a new technique for face detection and lip feature extraction. A real-time field-programmable gate array (FPGA) implementation of the two proposed techniques is also presented. Face detection is based on a naive Bayes classifier that classifies an edge-extracted representation of an image. Using edge representation significantly reduces the model's size to only 5184 B, which is 2417 times smaller than a comparable statistical modeling technique, while achieving an 86.6% correct detection rate under various lighting conditions. Lip feature extraction uses the contrast around the lip contour to extract the height and width of the mouth, metrics that are useful for speech filtering. The proposed FPGA system occupies only 15050 logic cells, or about six times less than a current comparable FPGA face detection system.

Artificial Intelligence↗
PubMed1995

An expert system based preventive medicine examination adviser.

Periodic preventive medicine examinations generally rely on a standardized approach. In addition, they are often performed by physicians with only limited training in preventive medicine. Evaluation of a corporate-based program led to the prototype development of an artificial intelligence (AI)-based expert system to collect information from employees and make very specific recommendations for primary practitioners. Unique features include the customizing of questions for each subject and the selection of information to be acquired, both based on answers to previous questions. Recommendations are highly person specific and fall into four categories: laboratory testing, primary physician testing, counseling, and referral. The AI approach allows for easy updating of recommendations in order to meet changes in local preventive resources and national recommendations.

Artificial Intelligence↗
PubMed1983

Use of conditional rule structure to automate clinical decision support: a comparison of artificial intelligence and deterministic programming techniques.

A rule-based computer system was developed to perform clinical decision-making support within a medical information system, oncology practice, and clinical research. This rule-based system, which has been programmed using deterministic rules, possesses features of generalizability, modularity of structure, convenience in rule acquisition, explanability, and utility for patient care and teaching, features which have been identified as advantages of artificial intelligence (AI) rule-based systems. Formal rules are primarily represented as conditional statements; common conditions and actions are stored in system dictionaries so that they can be recalled at any time to form new decision rules. Important similarities and differences exist in the structure of this system and clinical computer systems utilizing artificial intelligence (AI) production rule techniques. The non-AI rule-based system possesses advantages in cost and ease of implementation. The degree to which significant medical decision problems can be solved by this technique remains uncertain as does whether the more complex AI methodologies will be required.

Computers↗
PubMed1997

Lightweight fuzzy processes in clinical computing.

In spite of advances in computing hardware, many hospitals still have a hard time finding extra capacity in their production clinical information system to run artificial intelligence (AI) modules, for example: to support real-time drug-drug or drug-lab interactions; to track infection trends; to monitor compliance with case specific clinical guidelines; or to monitor/ control biomedical devices like an intelligent ventilator. Historically, adding AI functionality was not a major design concern when a typical clinical system is originally specified. AI technology is usually retrofitted 'on top of the old system' or 'run off line' in tandem with the old system to ensure that the routine work load would still get done (with as little impact from the AI side as possible). To compound the burden on system performance, most institutions have witnessed a long and increasing trend for intramural and extramural reporting, (e.g. the collection of data for a quality-control report in microbiology, or a meta-analysis of a suite of coronary artery bypass grafts techniques, etc.) and these place an ever-growing burden on typical the computer system's performance. We discuss a promising approach to adding extra AI processing power to a heavily-used system based on the notion 'lightweight fuzzy processing (LFP)', that is, fuzzy modules designed from the outset to impose a small computational load. A formal model for a useful subclass of fuzzy systems is defined below and is used as a framework for the automated generation of LFPs. By seeking to reduce the arithmetic complexity of the model (a hand-crafted process) and the data complexity of the model (an automated process), we show how LFPs can be generated for three sample datasets of clinical relevance.

Biopsy, Needle↗
PubMed2003

Decision support systems to identify different species of malarial parasites.

In this project, medical expert systems were designed to aid technicians, students and scientists in identifying various species of malarial parasite from blood smears. The rule based system was found to intelligently ask pertinent questions to accurately and efficiently identify the species of malarial parasite. The probabilistic system provided quantitative feedback as to the likelihood of a diagnosis of a species of malaria. Medical expert systems can potentially streamline the process of malarial species identification, and can aid in training new technicians and scientists in this important skill.

Animals↗
PubMed1994

Artificial intelligence within the chemical laboratory.

Various techniques within the area of artificial intelligence such as expert systems and neural networks may play a role during the problem-solving processes within the clinical biochemical laboratory. Neural network analysis provides a non-algorithmic approach to information processing, which results in the ability of the computer to form associations and to recognize patterns or classes among data. It belongs to the machine learning techniques which also include probabilistic techniques such as discriminant function analysis and logistic regression and information theoretical techniques. These techniques may be used to extract knowledge from example patients to optimize decision limits and identify clinically important laboratory quantities. An expert system may be defined as a computer program that can give advice in a well-defined area of expertise and is able to explain its reasoning. Declarative knowledge consists of statements about logical or empirical relationships between things. Expert systems typically separate declarative knowledge residing in a knowledge base from the inference engine: an algorithm that dynamically directs and controls the system when it searches its knowledge base. A tool is an expert system without a knowledge base. The developer of an expert system uses a tool by entering knowledge into the system. Many, if not the majority of problems encountered at the laboratory level are procedural. A problem is procedural if it is possible to write up a step-by-step description of the expert's work or if it can be represented by a decision tree. To solve problems of this type only small expert system tools and/or conventional programming are required.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗
PubMed1999

Computational modeling of an early evolutionary stage of the nervous system.

The object of this work is to create a computational model that examines the early evolution of the nervous system in relation to adaptive behavior. The main questions are: how did the nervous system and the most primitive forms of intelligence came into being, how a system can be organized during evolution that is able to ensure the adaptive behavior of a being, what are the basic rules of construction that are sufficient to create a workable nervous system without specifying the details of the construction. The biological bases of the model are the phyla Cnidaria and Porifera as they stand at the beginning of the genesis of nervous organization. We found in our model that in a network of homogenous epithelial-like cells, which is considered the starting point of the genesis of the nervous system, the changes that have positive influence on the behavior are those that make the spreading of the electric potential more efficient. It can cause the increase of the effectiveness of the behavior by itself without creating new specific cell-types. There are some alternatives to increasing the effectiveness of spreading of stimuli, for example increasing the value of biophysical parameters of the cells, or increasing the density of nerve cells and the number of synapses. If during the evolution a sort of cell comes into being that is able to conduct electrical stimuli--even in a rudimentary way--it can increase the adaptivity of behavior by itself without the need for specific information of how to organize the construction of this system.

Adaptation, Physiological↗
PubMed1991

Artificial intelligence-assisted occupational lung disease diagnosis.

An artificial intelligence expert-based system for facilitating the clinical recognition of occupational and environmental factors in lung disease has been developed in a pilot fashion. It utilizes a knowledge representation scheme to capture relevant clinical knowledge into structures about specific objects (jobs, diseases, etc) and pairwise relations between objects. Quantifiers describe both the closeness of association and risk, as well as the degree of belief in the validity of a fact. An independent inference engine utilizes the knowledge, combining likelihoods and uncertainties to achieve estimates of likelihood factors for specific paths from work to illness. The system creates a series of "paths," linking work activities to disease outcomes. One path links a single period of work to a single possible disease outcome. In a preliminary trial, the number of "paths" from job to possible disease averaged 18 per subject in a general population and averaged 25 per subject in an asthmatic population. Artificial intelligence methods hold promise in the future to facilitate diagnosis in pulmonary and occupational medicine.

Artificial Intelligence↗
PubMed1989

Construction of the diagnostic encyclopedia workstation. Computerizing pathology for the pathologist.

The combination of a personal computer and a laser-vision disc player is an adroit tool for storing and retrieving textual information in combination with pictorial information. This paper describes such a system (a "diagnostic encyclopedia workstation"), which provides information to the pathologist engaged in daily diagnostic practice. The system contains a considerable amount of descriptive textual information that might be useful in making diagnoses in histopathology, along with many illustrations; the text is presented in a natural language (English). The descriptive information is divided into 18 categories, including such topics as histology, macroscopy, immunopathology and clinical data; each topic has a separate display in the system. Also present are two types of "decision rules" for making diagnoses (confirmative criteria for a diagnosis under consideration and exclusive criteria for many other diagnoses) and a classifying structure. The present version of this system contains information on about 100 diagnoses in ovarian pathology, illustrated with about 3,000 color slides from about 140 cases. The information and pictures are immediately available. Further characteristics of this system are its flexibility, accessibility and user friendliness; it has been structured so that components of artificial intelligence may be added later.

Artificial Intelligence↗
PubMed1993

An intelligent interactive simulator of clinical reasoning in general surgery.

We introduce an interactive computer environment for teaching in general surgery and for diagnostic assistance. The environment consists of a knowledge-based system coupled with an intelligent interface that allows users to acquire conceptual knowledge and clinical reasoning techniques. Knowledge is represented internally within a probabilistic framework and externally through a interface inspired by Concept Graphics. Given a set of symptoms, the internal knowledge framework computes the most probable set of diseases as well as best alternatives. The interface displays CGs illustrating the results and prompting essential facts of a medical situation or a process. The system is then ready to receive additional information or to suggest further investigation. Based on the new information, the system will narrow the solutions with increased belief coefficients.

Artificial Intelligence↗
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↗
PubMed1988

Artificial intelligence: a computerized decision aid for trauma.

A computerized decision support system has been developed to advise ATLS-trained surgeons on the initial definitive management of patients with penetrating injuries of the abdomen immediately following resuscitation and stabilization. The program was developed as an "expert system," using the techniques of artificial intelligence. It is able to suggest: the need for further examination; additional tests; diagnoses; and treatments. In this study, the advice offered by the expert system was compared to that of physicians-in-training. Five actual patient care situations were presented to the system and to 13 medical students and surgical residents: four MS-III, three PGY-I, three PGY-III, and three PGY-V. The suggestions of each of the 13 trainees, the advice of the expert system, and the actual management were blinded. Five surgeons versed in trauma and otherwise not involved in the project judged whether each of the 15 purported management plans was acceptable and ranked them in order of preference. Only the actual care and the advice from the system were judged acceptable for all five problems. The rankings of the expert system were better than those of any individual trainee. The differences were statistically significant for two of the three chief residents, five of nine residents overall, and all four students. This preliminary validation of a prototype expert system is encouraging for the prospect of a computerized decision support system that can help surgeons make initial definitive management plans for patients with major trauma.

Abdominal Injuries↗