Biomedical subjects

M B do Amaral

Publications and source records attributed to M B do Amaral.

5 recordsLinked to original sources

PubMed2000

NLP techniques associated with the OpenGALEN ontology for semi-automatic textual extraction of medical knowledge: abstracting and mapping equivalent linguistic and logical constructs.

This research project presents methodological and theoretical issues related to the inter-relationship between linguistic and conceptual semantics, analysing the results obtained by the application of a NLP parser to a set of radiology reports. Our objective is to define a technique for associating linguistic methods with domain specific ontologies for semi-automatic extraction of intermediate representation (IR) information formats and medical ontological knowledge from clinical texts. We have applied the Edinburgh LTG natural language parser to 2810 clinical narratives describing radiology procedures. In a second step, we have used medical expertise and ontology formalism for identification of semantic structures and abstraction of IR schemas related to the processed texts. These IR schemas are an association of linguistic and conceptual knowledge, based on their semantic contents. This methodology aims to contribute to the elaboration of models relating linguistic and logical constructs based on empirical data analysis. Advance in this field might lead to the development of computational techniques for automatic enrichment of medical ontologies from real clinical environments, using descriptive knowledge implicit in large text corpora sources.

Electronic Data Processing↗

PubMed1998

Renewing information infrastructure at Hospital das Clínicas.

In this paper we describe the process of renewing the Informatics infrastructure of Sao Paulo University Medical School Hospital, a very complex environment. Our proposal consists of a change in the paradigm of Informatics and the use of Information Technology in the hospital. That change aims at making information available to the hospital, its managers, health care workers and patients. The paradigm change is reflected in every aspect of the new infrastructure: human resources, methods, and organizational structure, as we intend to demonstrate in this paper. This process is expected to be concluded by the end of this year, yielding benefits regarding costs, efficiency, and better patient care.

Brazil↗

PubMed1995

A psychiatric diagnostic system integrating probabilistic and categorical reasoning.

We describe a diagnostic support system for clinical psychiatry and its evaluation results. The system has two inter-related components: a rule-based reasoning part associated with uncertainty, and a deterministic part, that uses heuristics to perform categorical reasoning. The system includes the 30 groups of psychiatric diagnoses which are classified under the categories 290 to 319 of the DSM-III-R and the ICD-9. There are, in fact, 1508 rules relating 208 clinical findings with 257 diagnoses. The reasoning strategy is based on selecting and differentiating diagnostic categories in a hierarchical classification tree. The system is intended to be used for education of medical students, and to help non-specialist clinicians, residents in psychiatry, or experts with few years of experience in decision making. We tested the diagnostic performance of the system using case reports extracted from a specialized journal. In 52.8% of the cases, the correct diagnosis was ranked as the first hypothesis using only the rule-based part. In combination with the deterministic strategy, the correct diagnosis could be made for 73.6% of the analyzed cases.

Artificial Intelligence↗

PubMedDate not supplied

Automated diagnostic indexing by natural language processing.

Developing tools for natural language understanding by computers represents an important and intense field of research. This paper describes a system developed for interpreting medical natural language in the domain of symptoms and diagnoses from complete discharge summaries and locating the correspondent category into the International Classification of Diseases, through indexing by the Systematized Nomenclature of Medicine. The indexing program makes use of the MEID dictionary and some auxiliary semantic databases for identifying adjectival forms, synonyms, hypernyms and other semantic relations while searching for the longest consistent match into SNOMED. A further subdivision of the SNOMED structure was also proposed in order to find the hierarchically superior representative of a conceptual class when this association is not assigned by the related SNOMED code number. The system can be used by any language that possesses a translation of SNOMED and ICD. The knowledge base was built using a conversion file that maps the terms of the nomenclature into the classification, which can be improved by learning from users.

Abstracting and Indexing↗

PubMedDate not supplied

Structuring medical information into a language-independent database.

We describe one approach for natural language processing and database representation of medical information. The method is based in the semantic analysis of the statements and in the identification of patterns. The processed information is indexed and structured into a frame format containing semantic slots into the database. We tested our method analysing sentences describing symptoms extracted from case reports presented in the volume 328 of the New England Journal of Medicine. The results are: 73.41% of the sentences were formatted; 81.05% of the analysed words were identified; and 95.33% of the medical terms were indexed. We conclude that this semantic approach is not only efficient for processing natural language texts, but it can also be used for the organization of medical information using a language-independent format. This interlingua that is set into the database can be applied to semantic data retrieval; serving as a basis to organize a problem-orientated medical record; displaying simultaneously the DB information into two or more languages; information interchanging among different human languages and computers; and automatic translation, among others.

Abstracting and Indexing↗