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PubMed2006

Bio-Ontology and text: bridging the modeling gap.

MOTIVATION: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. RESULTS: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). AVAILABILITY: http://zellig.cpmc.columbia.edu/PGschema

Abstracting and Indexing↗
PubMed2006

Chester: towards a personal medication advisor.

Dialogue systems for health communication hold out the promise of providing intelligent assistance to patients through natural interfaces that require no training to use. But in order to make the development of such systems cost effective, we must be able to use generic techniques and components which are then specialized as needed to the specific health problem and patient population. In this paper, we describe Chester, a prototype intelligent assistant that interacts with its user via conversational natural spoken language to provide them with information and advice regarding their prescribed medications. Chester builds on our prior experience constructing conversational assistants in other domains. The emphasis of this paper is on the portability of our generic spoken dialogue technology, and presents a case study of the application of these techniques to the development of a dialogue system for health communication.

Artificial Intelligence↗
PubMed2005

Identifying reasoning strategies in medical decision making: a methodological guide.

Reasoning strategies are a key component in many medical tasks, including decision making, clinical problem solving, and understanding of medical texts. Identification of reasoning strategies used by clinicians may prove critical to the optimal design of decision support systems. This paper presents a formal method of cognitive-semantic analysis for the identification and characterization of reasoning strategies deployed in medical tasks and demonstrates its use through specific examples. Although semantic analysis was originally developed in the investigation of knowledge structures, it can also be applied to identify the reasoning and decision processes used by physicians and medical trainees in clinical tasks. Assumptions underlying the methods, as well as illustrations of their use in diagnostic explanation tasks, are presented. We discuss semantic analysis in the context of the current interests in developing medical ontologies and argue that a frame-based propositional analytic methodology can provide a systematic way of addressing the construction of such ontologies. Although the application of propositional analysis methods has some limitations, we show how such limitations are being addressed and present some examples of information tools that have been developed to ease, and make more systematic, the process of analysis.

Artificial Intelligence↗
PubMed2003

Terminology-driven mining of biomedical literature.

MOTIVATION: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective literature mining techniques that can help biologists to gather and make use of the knowledge encoded in text documents. Although the knowledge is organized around sets of domain-specific terms, few literature mining systems incorporate deep and dynamic terminology processing. RESULTS: In this paper, we present an overview of an integrated framework for terminology-driven mining from biomedical literature. The framework integrates the following components: automatic term recognition, term variation handling, acronym acquisition, automatic discovery of term similarities and term clustering. The term variant recognition is incorporated into terminology recognition process by taking into account orthographical, morphological, syntactic, lexico-semantic and pragmatic term variations. In particular, we address acronyms as a common way of introducing term variants in biomedical papers. Term clustering is based on the automatic discovery of term similarities. We use a hybrid similarity measure, where terms are compared by using both internal and external evidence. The measure combines lexical, syntactical and contextual similarity. Experiments on terminology recognition and clustering performed on a corpus of MEDLINE abstracts recorded the precision of 98 and 71% respectively. AVAILABILITY: software for the terminology management is available upon request.

Abbreviations as Topic↗
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↗
PubMed2000

A customisable framework for the assessment of therapies in the solution of therapy decision tasks.

In current medical research, a growing interest can be observed in the definition of a global therapy-evaluation framework which integrates considerations such as patients preferences and quality-of-life results. In this article, we propose the use of the research results in this domain as a source of knowledge in the design of support systems for therapy decision analysis, in particular with a view to application in oncology. We discuss the incorporation of these considerations in the definition of the therapy-assessment methods involved in the solution of a generic therapy decision task, described in the context of AI software development methodologies such as CommonKADS. The goal of the therapy decision task is to identify the ideal therapy, for a given patient, in accordance with a set of objectives of a diverse nature. The assessment methods applied are based either on data obtained from statistics or on the specific idiosyncrasies of each patient, as identified from their responses to a suite of psychological tests. In the analysis of the therapy decision task we emphasise the importance, from a methodological perspective, of using a rigorous approach to the modelling of domain ontologies and domain-specific data. To this aim we make extensive use of the semi-formal object oriented analysis notation UML to describe the domain level.

Artificial Intelligence↗
PubMed1999

Near-field speech intelligibility in chemical-biological warfare masks.

It is common knowledge among field personnel that poor speech intelligibility can occur when chemical-biological warfare (CBW) masks are worn: indeed, many users resort to hand signals for person-to-person communicative purposes. This study was conducted in an effort to generate basic information about the problem; its focus was on the assessment of, and comparisons among, the communicative efficiency of seven different CBW units. Near-field word intelligibility was assessed by use of rhyming minimal contrast tests; user and acoustic restrictions were studied by means of diadochokinetic tests and system frequency response. The near-field word intelligibility of six American-designed masks varied somewhat, but overall it was reasonably good; however, a Russian unit did not perform well. Second, three of the U.S. masks were found to produce less physiological restraint than the others, and the Soviet mask produced the greatest physiological restraint. Finally, a few of the CBW masks also exhibited very low levels of acoustic distortion. Accordingly, it was concluded that two of the several configurations studied exhibited superior features. Other factors being equal, they can be recommended for field use and as a basis for the development of future generations of CBW masks. However, it also should be noted that although these devices provided reasonably good speech intelligibility when the listener was close to the talker, they do not appear to do so even at minimal distances.

Adult↗
PubMed2006

Substring selection for biomedical document classification.

MOTIVATION: Attribute selection is a critical step in development of document classification systems. As a standard practice, words are stemmed and the most informative ones are used as attributes in classification. Owing to high complexity of biomedical terminology, general-purpose stemming algorithms are often conservative and could also remove informative stems. This can lead to accuracy reduction, especially when the number of labeled documents is small. To address this issue, we propose an algorithm that omits stemming and, instead, uses the most discriminative substrings as attributes. RESULTS: The approach was tested on five annotated sets of abstracts from iProLINK that report on the experimental evidence about five types of protein post-translational modifications. The experiments showed that Naive Bayes and support vector machine classifiers perform consistently better [with area under the ROC curve (AUC) accuracy in range 0.92-0.97] when using the proposed attribute selection than when using attributes obtained by the Porter stemmer algorithm (AUC in 0.86-0.93 range). The proposed approach is particularly useful when labeled datasets are small.

Abstracting and Indexing↗
PubMed2004

HELEN, a modular framework for representing and implementing clinical practice guidelines.

OBJECTIVES: In order to implement clinical practice guidelines for the Department of Neonatology of the Heidelberg University Medical Center we developed a modular framework consisting of tools for authoring, browsing and executing encoded clinical practice guidelines (CPGs). METHODS: Based upon a comprehensive analysis of literature, we set up requirements for guideline representation systems. Additionally, we analyzed further aspects such as the critical appraisal and known bridges and barriers for implementing CPGs. Thereafter we went through an evolutionary spiral model to develop a comprehensive ontology. Within this model each cycle focuses on a certain topic of management and implementation of CPGs. RESULTS: In order to bring the resulting ontology into practice we developed a framework consisting of a tool for authoring, a server for web-based browsing, and an engine for the execution of certain elements of CPGs. Based upon this framework we encoded and implemented several CPGs in varying medical domains. CONCLUSIONS: This paper shall present a practical framework for both authors and implementers of CPGs. We have shown the fruitful combination of different knowledge representations such as narrative text and algorithm for implementing CPGs. Finally, we introduced a possible approach for the explicit adaptation of CPGs in order to provide institution-specific recommendations and to support sharing with other medical institutions.

Academic Medical Centers↗
PubMed2004

Ruleminer: a knowledge system for supporting high-throughput protein function annotations.

In this paper, we present RuleMiner, a knowledge system to facilitate a seamless integration of multi-sequence analysis tools and define profile-based rules for supporting high-throughput protein function annotations. This system consists of three essential components, Protein Function Groups (PFGs), PFG profiles and rules. The PFGs, established from an integrated analysis of current knowledge of protein functions from Swiss-Prot database and protein family-based sequence classifications, cover all possible cellular functions available in the database. The PFG profiles illustrate detailed protein features in the PFGs as in sequence conservations, the occurrences of sequence-based motifs, domains and species distributions. The rules, extracted from the PFG profiles, describe the clear relationships between these PFGs and all possible features. As a result, the RuleMiner is able to provide an enhanced capability for protein function analysis, such as results from the integrated sequence analysis tools for given proteins can be comparatively analyzed due to the clear feature-PFG relationships. Also, much needed guidance is readily available for such analysis. If the rules describe one-to-one (unique) relationships between the protein features and the PFGs, then these features can be utilized as unique functional identifiers and cellular functions of unknown proteins can be reliably determined. Otherwise, additional information has to be provided.

Algorithms↗
PubMed1995

Knowledge-acquisition tools for medical knowledge-based systems.

Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

Algorithms↗
PubMed1993

An electronic medical record that helps care for patients with HIV infection.

We have built a clinical workstation to help doctors and nurses care for patients with HIV infection. This knowledge-based medical record system provides medication alerts, reminders about primary care, and on-line information to support the care of patients with HIV infection. We are conducting a controlled clinical trial of this computer system in a single practice setting, which consists of 18 staff physicians, 13 nurses, and 113 residents, who cooperatively practice in four teams. Two teams of physicians are assigned to an intervention group and two teams to a control group. This paper reports preliminary results from the first year of study, January 15, 1992, through January 14, 1993. During this period 274 patients with HIV infection were followed by the general medical practice--130 in a control group and 144 in an intervention group. Physicians in the intervention group more rapidly and more completely followed primary care guidelines than did physicians in the control group. Patients in the intervention group had 2476 ambulatory or emergency visits (17.2 visits per patient) compared with 1882 visits (14.5 visits per patient) for the control patients (p < 0.01). There were 101 hospitalizations for 51 patients in the intervention group (an admission rate of 0.7) compared with 104 admissions for 54 patients in the control group (an admission rate of 0.8) (p = NS). There were 8 deaths in the intervention group (5.6%) compared with 13 (10%) in the control group (p = NS).(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗
PubMed2004

Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction.

MOTIVATION: Wnt signaling is a very active area of research with highly relevant publications appearing at a rate of more than one per day. Building and maintaining databases describing signal transduction networks is a time-consuming and demanding task that requires careful literature analysis and extensive domain-specific knowledge. For instance, more than 50 factors involved in Wnt signal transduction have been identified as of late 2003. In this work we describe a natural language processing (NLP) system that is able to identify references to biological interaction networks in free text and automatically assembles a protein association and interaction map. RESULTS: A 'gold standard' set of names and assertions was derived by manual scanning of the Wnt genes website (http://www.stanford.edu/~rnusse/wntwindow.html) including 53 interactions involved in Wnt signaling. This system was used to analyze a corpus of peer-reviewed articles related to Wnt signaling including 3369 Pubmed and 1230 full text papers. Names for key Wnt-pathway associated proteins and biological entities are identified using a chi-squared analysis of noun phrases over-represented in the Wnt literature as compared to the general signal transduction literature. Interestingly, we identified several instances where generic terms were used on the website when more specific terms occur in the literature, and one typographic error on the Wnt canonical pathway. Using the named entity list and performing an exhaustive assertion extraction of the corpus, 34 of the 53 interactions in the 'gold standard' Wnt signaling set were successfully identified (64% recall). In addition, the automated extraction found several interactions involving key Wnt-related molecules which were missing or different from those in the canonical diagram, and these were confirmed by manual review of the text. These results suggest that a combination of NLP techniques for information extraction can form a useful first-pass tool for assisting human annotation and maintenance of signal pathway databases. AVAILABILITY: The pipeline software components are freely available on request to the authors. CONTACT: dstates@umich.edu SUPPLEMENTARY INFORMATION: http://stateslab.bioinformatics.med.umich.edu/software.html.

Animals↗
PubMed1997

Decision support system to assist mechanical ventilation in the adult respiratory distress syndrome.

This paper presents a knowledge-based decision support system to assist mechanical ventilation in patients with the Adult Respiratory Distress Syndrome (DSSARDS). The knowledge base uses clinical algorithms developed from interviews and seminars with experts. The system contains 140 rules, applies backward chaining and was built on an IBM-PC compatible microcomputer. Clinical and physiological data and ventilator settings were used for suggestions of ventilatory support mode (VSMODE) and settings (MVSET) and for hemodynamic evaluation and therapy (HEMO). Success rates (s) and kappa coefficient (k) were used to measure agreement between DSSARDS and physicians at 4 decision steps related to: beginning of mechanical ventilation (FIRSTSET), VSMODE, MVSET and HEMO, DSSARDS prototype was evaluated in a development phase with 6 patients aged 48.6 +/- 15.9 years. Agreement results for 142 decision steps were: FIRSTSET k = 0.90, s = 0.93; VSMODE k = 0.76, s = 0.92; HEMO k = 0.58, s = 0.70, MVSET k = 0.86, s = 0.92 (p < 0.05 for all k). Improvements in the knowledge base were performed mainly in HEMO and VSMODE modules. The subsequent test phase studied 5 patients aged 54.8 +/- 11.0 years in a total of 900 decision steps. Results were: FIRSTSET k = 0.93, s = 0.95; VSMODE k = 0.93, s = 0.96; HEMO k = 0.97, s = 0.99, MVSET k = 0.96, s = 0.97 (p < 0.05 for all k). The results indicate significant agreement between DSSARDS and physicians for all decision steps. This suggests that DSSARDS may be used as a support for decision making and a training tool for mechanical ventilation in patients with the adult respiratory distress syndrome.

Adult↗
PubMed1999

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

GALEN has developed a new generation of terminology tools based on a language independent concept reference model using a compositional formalism allowing computer processing and multiple reuses. During the 4th framework program project Galen-In-Use we applied the modelling and the tools to the development of a new multipurpose coding system for surgical procedures (CCAM) in France. On one hand we contributed to a language independent knowledge repository 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 to process French professional medical language rubrics produced by the national colleges of surgeons into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation on one hand we generate controlled French natural language to support the finalization of the linguistic labels in relation with the meanings of the conceptual system structure. On the other hand the classification manager of third generation proves to be very powerful to retrieve the initial professional rubrics with different categories of concepts within a semantic network.

Abstracting and Indexing↗
PubMed1987

Personal reflections on the nature of intelligence in humans and machines.

Computers, the human mind, and social systems have common problems of inadequate memory and insufficient data manipulation speed. In each of these domains, information compression techniques have evolved to reduce storage and processing needs. Among the techniques for information compression, coding of information in procedures stands out as exceptionally powerful. Procedural information coding also gives rise to behavior that may be defined as intelligent. It is found in the human mind, in machines and in social systems. Its use in human thought is aided by language development which promotes regular review of abstract procedures. A practical consequence of better understanding of procedural information coding is the possibility of training people to exhibit greater mental capacity, a controversial possibility. This paper explores the impact of data processing resource limitations, data compression and procedural thinking in men and machines.

Artificial Intelligence↗
PubMed2005

Nursing constraint models for electronic health records: a vision for domain knowledge governance.

Various forms of electronic health records (EHRs) are currently being introduced in several countries. Nurses are primary stakeholders and need to ensure that their information and knowledge needs are being met by such systems information sharing between health care providers to enable them to improve the quality and efficiency of health care service delivery for all subjects of care. The latest international EHR standards have adopted the openEHR approach of two-level modelling. The first level is a stable information model determining structure, while the second level consists of constraint models or 'archetypes' that reflect the specifications or clinician rules for how clinical information needs to be represented to enable unambiguous data sharing. The current state of play in terms of international health informatics standards development activities is providing the nursing profession with a unique opportunity and challenge. Much work has been undertaken internationally in the area of nursing terminologies and evidence-based practice. This paper argues that to make the most of these emerging technologies and EHRs we must now concentrate on developing a process to identify, document, implement, manage and govern our nursing domain knowledge as well as contribute to the development of relevant international standards. It is argued that one comprehensive nursing terminology, such as the ICNP or SNOMED CT is simply too complex and too difficult to maintain. As the openEHR archetype approach does not rely heavily on big standardised terminologies, it offers more flexibility during standardisation of clinical concepts and it ensures open, future-proof electronic health records. We conclude that it is highly desirable for the nursing profession to adopt this openEHR approach as a means of documenting and governing the nursing profession's domain knowledge. It is essential for the nursing profession to develop its domain knowledge constraint models (archetypes) collaboratively in an international context.

Artificial Intelligence↗
PubMed1991

Intelligent management of epidemiologic data.

In the lifecycle of epidemiologic data three steps can be identified: production, interpretation and exploitation for decision. Computerized support can be precious, if not indispensable, at any of the three levels, therefore several epidemiologic data management systems were developed. In this paper we focus on intelligent management of epidemiologic data, where intelligence is needed in order to analyze trends or to compare observed with reference value and possibly detect abnormalities. After having outlined the problems involved in such a task, we show the features of ADAMS, a system realized to manage aggregated data and implemented in a personal computer environment.

Artificial Intelligence↗