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At least 1,621 records · Page 90Linked to original sources
PubMed2003

A prototype natural language interface to a large complex knowledge base, the Foundational Model of Anatomy.

We describe a constrained natural language interface to a large knowledge base, the Foundational Model of Anatomy (FMA). The interface, called GAPP, handles simple or nested questions that can be parsed to the form, subject-relation-object, where subject or object is unknown. With the aid of domain-specific dictionaries the parsed sentence is converted to queries in the StruQL graph-searching query language, then sent to a server we developed, called OQAFMA, that queries the FMA and returns output as XML. Preliminary evaluation shows that GAPP has the potential to be used in the evaluation of the FMA by domain experts in anatomy.

Anatomy↗
PubMed2003

Does GEM-encoding clinical practice guidelines improve the quality of knowledge bases? A study with the rule-based formalism.

The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results.

Artificial Intelligence↗
PubMed2004

A knowledge based approach for automated signal generation in pharmacovigilance.

BACKGROUND: Pharmacovigilance experts detect new adverse drug reactions (ADR) by manually reviewing spontaneous reporting systems. Automated signal generation aims to focus the attention of experts on drug-adverse event associations which are disproportionally present in the database. Although adverse events are coded by means of controlled vocabularies such as the MedDRA dictionary, this semantic information is not taken into account for signal generation. OBJECTIVE: To improve the performance of current signal detection algorithms using knowledge based approach. METHOD: We developed a formal ontology of ADRs and built a data mining tool that uses description logic representations of MedDRA terms to group medically related case reports. RESULTS: This knowledge based approach increased the sensitivity of signal detection with no decrease in specificity. DISCUSSION: A knowledge based approach improved the performance of signal detection tools. However, the huge work-load involved in the knowledge engineering step limits the use of this approach for machine learning.

Adverse Drug Reaction Reporting Systems↗
PubMed2002

A multi-level text mining method to extract biological relationships.

Accurate and computationally efficient approaches in discovering relationships between biological objects from text documents are important for biologists to develop biological models. This paper presents a novel approach to extract relationships between multiple biological objects that are present in a text document. The approach involves object identification, reference resolution, ontology and synonym discovery, and extracting object-object relationships. Hidden Markov Models (HMMs), dictionaries, and N-Gram models are used to set the framework to tackle the complex task of extracting object-object relationships. Experiments were carried out using a corpus of one thousand Medline abstracts. Intermediate results were obtained for the object identification process, synonym discovery, and finally the relationship extraction. For a corpus of thousand abstracts, 53 relationships were extracted of which 43 were correct, giving a specificity of 81%. The approach is both adaptable and scalable to new problems as opposed to rule-based methods.

Abstracting and Indexing↗
PubMedDate not supplied

Clinical pictures of unknown origin in neurology: past, present and future usefulness of artificial intelligence.

Although, in the course of the last 50 years, the achievements in the medical field have been astonishing, at the beginning of the third millennium a number of clinical pictures are still left without a precise nosographic origin. In the past, the delay in scientific communication was the main explanation presented for the lack of understanding of clinical pictures of unknown nosographic origin. The history of medicine provides excellent examples of this dispersion of human capital, even if the history of clinical neurology presents "exceptions" (the pictures that we now call de la Tourette's syndrome and Parkinson's disease) that indicate that major clinical syndromes could be clearly detected and relatively rapidly diffused even in the 19th century. Contrary to the past, the delay in scientific communication no longer seems an obstacle to the sharing of medical knowledge. Nevertheless, the problem of the in-depth comprehension of clinical pictures of unknown nosographic origin still remains dominant, mainly because of the limited spread of ample and flexible online accessible databases of unknown nosographic origin clinical syndromes. The need for interactive electronic archives and other artificial intelligence resources in order to promote progress in clinical knowledge is discussed in this paper.

Artificial Intelligence↗
PubMed2003

Towards index-based similarity search for protein structure databases.

We propose two methods for finding similarities in protein structure databases. Our techniques extract feature vectors on triplets of SSEs (Secondary Structure Elements) of proteins. These feature vectors are then indexed using a multidimensional index structure. Our first technique considers the problem of finding proteins similar to a given query protein in a protein dataset. This technique quickly finds promising proteins using the index structure. These proteins are then aligned to the query protein using a popular pairwise alignment tool such as VAST. We also develop a novel statistical model to estimate the goodness of a match using the SSEs. Our second technique considers the problem of joining two protein datasets to find an all-to-all similarity. Experimental results show that our techniques improve the pruning time of VAST 3 to 3.5 times while keeping the sensitivity similar.

Algorithms↗
PubMed2003

Discovering compact and highly discriminative features or feature combinations of drug activities using support vector machines.

Nowadays, high throughput experimental techniques make it feasible to examine and collect massive data at the molecular level. These data, typically mapped to a very high dimensional feature space, carry rich information about functionalities of certain chemical or biological entities and can be used to infer valuable knowledge for the purposes of classification and prediction. Typically, a small number of features or feature combinations may play determinant roles in functional discrimination. The identification of such features or feature combinations is of great importance. In this paper, we study the problem of discovering compact and highly discriminative features or feature combinations from a rich feature collection. We employ the support vector machine as the classification means and aim at finding compact feature combinations. Comparing to previous methods on feature selection, which identify features solely based on their individual roles in the classification, our method is able to identify minimal feature combinations that ultimately have determinant roles in a systematic fashion. Experimental study on drug activity data shows that our method can discover descriptors that are not necessarily significant individually but are most significant collectively.

Algorithms↗
PubMed1989

Neuropsychologic impairment in children with sickle cell anemia.

In this study, the neuropsychologic functioning of 21 children with sickle cell anemia and 21 sibling controls, age range 7 through 16 years, with no history of neurologic disease, was examined. Outcome measures included tests of intelligence, constructional praxis, memory, and academic learning. On the Wechsler Intelligence Scale for Children--Revised, the sickle cell group had a mean Full Scale IQ of 77.7 (SD 12.4) compared with 94.3 (SD 11.0) for the control group. The profile of test scores was similar for the two groups, with the sickle cell group scoring significantly lower than the control group on almost all cognitive measures. Both groups showed academic achievement to be commensurate with their measured intellectual ability. These results suggest that subtle but significant and widespread neuropsychologic deficits are associated with sickle cell anemia even in the absence of neurologic complications. When and by what process this neuropsychologic impairment is caused needs to be determined.

Adolescent↗