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From data to knowledge through concept-oriented terminologies: experience with the Medical Entities Dictionary.

Knowledge representation involves enumeration of conceptual symbols and arrangement of these symbols into some meaningful structure. Medical knowledge representation has traditionally focused more on the structure than the symbols. Several significant efforts are under way, at local, national, and international levels, to address the representation of the symbols though the creation of high-quality terminologies that are themselves knowledge based. This paper reviews these efforts, including the Medical Entities Dictionary (MED) in use at Columbia University and the New York Presbyterian Hospital. A decade's experience with the MED is summarized to serve as a proof-of-concept that knowledge-based terminologies can support the use of coded patient data for a variety of knowledge-based activities, including the improved understanding of patient data, the access of information sources relevant to specific patient care problems, the application of expert systems directly to the care of patients, and the discovery of new medical knowledge. The terminological knowledge in the MED has also been used successfully to support clinical application development and maintenance, including that of the MED itself. On the basis of this experience, current efforts to create standard knowledge-based terminologies appear to be justified.

Artificial Intelligence↗

[The physician and the computer. 29. Learning systems and neuron networks].

The authors discuss basic properties of the process of learning as a form of artificial intelligence. They deal with the application of adaptive and learning systems in the area of identification, regulation, manipulation and recognition. A very important perspective trend is learning in a medium of neuron networks which are actually software or hardware simulation of the functional architecture of the CNS. The authors discuss peculiar features of representation of knowledge, structure and mode of function of this neuron network. Finally the authors give examples of the solution of optimalization and classification tasks within the framework of neurocomputers.

Artificial Intelligence↗

Data bases for the assessment of medical technologies. Examples from Europe.

The assessment of medical technologies has to answer several questions ranging from safety and effectiveness to complex economical, social, and health policy issues. The type of data needed to carry out such evaluation depends on the specific questions to be answered, as well as on the stage of development of a technology. Basically two types of data may be distinguished: (a) general demographic, administrative, or financial data which has been collected not specifically for technology assessment; (b) the data collected with respect either to a specific technology or to a disease or medical problem. On the basis of a pilot inquiry in Europe and bibliographic research, the following categories of type (b) data bases have been identified: registries, clinical data bases, banks of factual and bibliographic knowledge, and expert systems. Examples of each category are discussed briefly. The following aims for further research and practical goals are proposed: criteria for the minimal data set required, improvement to the registries and clinical data banks, and development of an international clearinghouse to enhance information diffusion on both existing data bases and available reports on medical technology assessments.

Artificial Intelligence↗

Integrating case based and rule based reasoning in a decision support system: evaluation with simulated patients.

We present a Web-based knowledge management and decision support system for Type I Diabetes patients' care. The tool exploits the integration of two methodologies, Case Based Reasoning and Rule Based Reasoning, and supports physicians in the definition of therapeutic strategies. Such a work is being integrated in the EU funded T-IDDM project architecture. In this paper we report a first evaluation obtained on simulated patients.

Artificial Intelligence↗

Discovering patterns to extract protein-protein interactions from the literature: Part II.

MOTIVATION: An enormous number of protein-protein interaction relationships are buried in millions of research articles published over the years, and the number is growing. Rediscovering them automatically is a challenging bioinformatics task. Solutions to this problem also reach far beyond bioinformatics. RESULTS: We study a new approach that involves automatically discovering English expression patterns, optimizing them and using them to extract protein-protein interactions. In a sister paper, we described how to generate English expression patterns related to protein-protein interactions, and this approach alone has already achieved precision and recall rates significantly higher than those of other automatic systems. This paper continues to present our theory, focusing on how to improve the patterns. A minimum description length (MDL)-based pattern-optimization algorithm is designed to reduce and merge patterns. This has significantly increased generalization power, and hence the recall and precision rates, as confirmed by our experiments. AVAILABILITY: http://spies.cs.tsinghua.edu.cn.

Abstracting and Indexing↗

Evaluation of a probabilistic model for staging of oesophageal carcinoma.

With the help of two experts in gastrointestinal oncology from the Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, a decision-support system is being developed for patient-specific therapy selection for oesophageal carcinoma. The kernel of the system is a probabilistic model describing the characteristics of oesophageal carcinoma and the pathophysiological processes of invasion and metastasis. Using data from 185 patients, an evaluation study of the model was conducted. We found that for 86% of the patients, the model established the stage of the patient's carcinoma correctly.

Artificial Intelligence↗

Mark-up based analysis of narrative guidelines with the Stepper tool.

The Stepper tool was developed to assist a knowledge engineer in developing a computable version of narrative guidelines. The system is document-centric: it formalises the initial text in multiple user-definable steps corresponding to interactive XML transformations. In this paper, we report on experience obtained by applying the tool on a narrative guideline document addressing unstable angina pectoris. Possible role of the tool and associated methodology in developing a guideline-based application is also discussed.

Artificial Intelligence↗

A neural network model based on the analogy with the immune system.

The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli ("questions") selectively applied to the network by a "teacher" can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep.

Artificial Intelligence↗

pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry.

SUMMARY: Research in proteomics requires powerful database-searching software to automatically identify protein sequences in a complex protein mixture via tandem mass spectrometry. In this paper, we describe a novel database-searching software system called pFind (peptide/protein Finder), which employs an effective peptide-scoring algorithm that we reported earlier. The pFind server is implemented with the C++ STL, .Net and XML technologies. As a result, high speed and good usability of the software are achieved.

Algorithms↗

Modifying the formulation or delivery mechanism to increase the activity of anthelmintic compounds.

The development of resistance to current chemical classes of broad-spectrum anthelmintics poses an undeniable threat to the long-term viability of the animal health industry. Alternative treatment strategies including vaccines, biological control and breeding of parasite-resistant animals are unlikely to be widely available in the near future and even then, they will be integrated with chemotherapy. To compound the severity of the situation there appears to be no new chemical class of anthelmintics, with unique mode of action, on the horizon. The significant cost of drug research and the development costs of a drug that is to be used in food-producing animals, together with the small market share of animal health products compared to human pharmaceutical/medical and cosmetic products, provide little incentive for anthelmintic development. The chemical actives that are currently available, are all that we are likely to have for the foreseeable future. If effective parasite treatment is to continue, existing actives must be used more efficiently. Recognising the potential for the animal's physiological behaviour to assist drug action is of significant value. Reduction of feed intake before oral anthelmintic treatment slows ruminant digesta flow, prolongs and extends the availability and therefore increases efficacy, of the benzimidazole and ivermectin compounds. This is a cost effective option that can be employed which not only increases efficacy of 'older' compounds, but will be instrumental in prolonging the useful life of the 'newer' drugs. In a related approach the co-administration of metabolic inhibitors can prolong drug clearance and extend availability and increase the action of existing anthelmintics. However, given the large costs which would be associated with this development (host toxicity, residue safety) it is probable that the value of such combinations would be more appropriate for use in the treatment of non-food producing animals. The most promising approach for improved formulation lies in innovative delivery systems using chemical or physical carriers. Solubility-defining salts, oils, solid/drug matrices, liposomes and related microparticles that reduce drug absorption/metabolism and can specifically direct large quantities of active, over an extended or pre-determined period, to the site(s) of parasitic infection. The use of lipophilic actives/vehicles which deposit in and are released from body fat is of particular value in extending drug availability. The prophylactic action of extended drug residence time, when used with effective grazing/treatment management programmes, provides opportunities for sustainable antiparasitic action. Clearly, with the paucity of new chemical classes of anthelmintics, the use of 'intelligent' but still relatively inexpensive carriers/delivery systems for existing actives will form the basis of future parasite control.

Animals↗

Applying GIFT, a Gene Interactions Finder in Text, to fly literature.

UNLABELLED: A number of freely available text mining tools have been put together to extract highly reliable Drosophila gene interaction data from text. The system has been tested with The Interactive Fly, showing low recall (27-34%), but very high precision (93-97%). AVAILABILITY: The extracted data and a web interface for submission of texts to GIFT analysis are available at http://gift.cryst.bbk.ac.uk/gift CONTACT: n.domedel_puig@cryst.bbk.ac.uk SUPPLEMENTARY INFORMATION: Additional documentation, such as the dictionaries and the reference sets, are available at the GIFT website.

Artificial Intelligence↗

Recognizing names in biomedical texts using mutual information independence model and SVM plus sigmoid.

In this paper, we present a biomedical name recognition system, called PowerBioNE. In order to deal with the special phenomena in the biomedical domain, various evidential features are proposed and integrated through a mutual information independence model (MIIM). In addition, a support vector machine (SVM) plus sigmoid is proposed to resolve the data sparseness problem in the MIIM. In this way, the data sparseness problem in MIIM-based biomedical name recognition can be resolved effectively and a biomedical name recognition system with better performance and better portability can be achieved. Finally, we present two post-processing modules to deal with the nested entity name and abbreviation phenomena in the biomedical domain to further improve the performance. Evaluation shows that our system achieves F-measures of 69.1 and 71.2 on the 23 classes of GENIA V1.1 and V3.0, respectively. In particular, our system achieves an F-measure of 77.8 on the "protein" class of GENIA V3.0. It also shows that our system outperforms the best-reported system on GENIA V1.1 and V3.0.

Abstracting and Indexing↗

Using MEDLINE as a knowledge source for disambiguating abbreviations and acronyms in full-text biomedical journal articles.

Biomedical abbreviations and acronyms are widely used in biomedical literature. Since many of them represent important content in biomedical literature, information retrieval and extraction benefits from identifying the meanings of those terms. On the other hand, many abbreviations and acronyms are ambiguous, it would be important to map them to their full forms, which ultimately represent the meanings of the abbreviations. In this study, we present a semi-supervised method that applies MEDLINE as a knowledge source for disambiguating abbreviations and acronyms in full-text biomedical journal articles. We first automatically generated from the MEDLINE abstracts a dictionary of abbreviation-full pairs based on a rule-based system that maps abbreviations to full forms when full forms are defined in the abstracts. We then trained on the MEDLINE abstracts and predicted the full forms of abbreviations in full-text journal articles by applying supervised machine-learning algorithms in a semi-supervised fashion. We report up to 92% prediction precision and up to 91% coverage.

Artificial Intelligence↗

[The development of database system of electrocardiographic information].

An ECG information database system was established with 1297 ECG information records and clinical informations. The data and the analysis software in this database can be shared with the international physiological signal databases such as MIT-BIH Arrhythmia. The establishment of this database will facilitate the clinical interpretation, diagnosis and prediction of cardiovascular diseases, especially the cardiac sudden death (SCD).

Artificial Intelligence↗

Instantiating and monitoring treatment protocols.

This paper presents a system for protocol-based treatment planning, plan execution, and execution monitoring. The approach, named SPIN, is developed as a component of the Guardian system. Guardian is an experimental architecture for intelligent patient monitoring and control. The paper describes and illustrates SPIN in a clinical scenario.

Clinical Protocols↗

Toward an interim standard for patient-centered knowledge-access.

Most care-giver "knowledge" needs arise at the point of care and are "patient-centered." Many of these knowledge needs can be met using existing on-line knowledge sources, but the process is too time-consuming, currently, for even the computer-proficient. We are developing a set of public domain standards aimed at bringing potentially relevant knowledge to the point of care in a straight-forward and timely fashion. The standards will a) make use of selected items from a Computer-based Patient Record (CPR), e.g., a diagnosis and measure of severity, b) anticipate certain care-giver knowledge needs, e.g., "therapy," "protocols," "complications," and c) try to satisfy those needs from available knowledge sources, e.g., knowledge-bases, citation databases, practice guidelines, and on-line textbooks. The standards will use templates, i.e., fill-in-the-blank structures, to anticipate knowledge needs and UMLS Metathesaurus enhancements to represent the content of knowledge sources. Together, the standards will form the specification for a "Knowledge-Server" (KS) designed to be accessed from any CPR system. Plans are in place to test an interim version of this specification in the context of medical oncology. We are accumulating anecdotal evidence that a KS operating in conjunction with a CPR is much more compelling to users than either a CPR or a KS operating alone.

Artificial Intelligence↗

Knowledge-based system for structured examination, diagnosis and therapy in treatment of traumatised teeth.

Dental trauma in children and adolescents is a common problem, and the prevalence of these injuries has increased in the last 10-20 years. A dental injury should always be considered an emergency and, thus, be treated immediately to relieve pain, facilitate reduction of displaced teeth, reconstruct lost hard tissue, and improve prognosis. Rational therapy depends upon a correct diagnosis, which can be achieved with the aid of various examination techniques. It must be understood that an incomplete examination can lead to inaccurate diagnosis and less successful treatment. Good knowledge of traumatology and models of treatments can also reduce stress and anxiety for both the patient and the dental team. Knowledge-based Systems (KBS) are a practical implementation of Artificial Intelligence. In complex domains which humans find difficult to understand, KBS can assist in making decisions and can also add knowledge. The aim of this paper is to describe the structure of a knowledge-based system for structured examination, diagnosis and therapy for traumatised primary and permanent teeth. A commercially available program was used as developmental tool for the programming (XpertRule, Attar, London, UK). The paper presents a model for a computerised decision support system for traumatology.

Adolescent↗

Medical data abstractionism: fitting an EMR to radically evolving medical information systems.

Growing and maintaining a simple and flexible EMR (Electronic Medical Record) becomes a complicated task in light of diverse and distributed legacy data representation, advancing technologies, changes in medical practice and procedure, and changes in data regulation. Utilizing several abstraction mechanisms can simplify application development and maintenance, and provide flexibility for data evolution and migration. Newer applications built on these abstractions can be the beneficiary of slower obsolescence and lower maintenance costs.

Abstracting and Indexing↗