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The classification-nomenclature issues in medicine: a return to natural language.

The subject is examined in accordance with recommendations made at two recent international conferences. The historical backgrounds of the International Classification of Diseases (ICD) and the Systematized Nomenclature of Medicine (SNOMED) are related to explain the current status and objectives of both as well as their structural differences. A Canadian alternative to the ICD is presented along with the reason for its non-acceptance. Finally, it is proposed that SNOMED and ICD be integrated in a practical way to obtain the benefits of a multiaxial nomenclature while retaining the equivalent ICD classes necessary for maintaining the continuity of statistical information. This approach would prepare the groundwork necessary for completely automated encoding in natural medical language for health care data banks, while providing ICD-based national and international statistics.

Artificial Intelligence↗
PubMed2025

Large Language Model and Knowledge Graph-Driven AJCC Staging of Prostate Cancer Using Pathology Reports.

Background/Objectives: To develop an automated American Joint Committee on Cancer (AJCC) staging system for radical prostatectomy pathology reports using large language model-based information extraction and knowledge graph validation. Methods: Pathology reports from 152 radical prostatectomy patients were used. Five additional parameters (Prostate-specific antigen (PSA) level, metastasis stage (M-stage), extraprostatic extension, seminal vesicle invasion, and perineural invasion) were extracted using GPT-4.1 with zero-shot prompting. A knowledge graph was constructed to model pathological relationships and implement rule-based AJCC staging with consistency validation. Information extraction performance was evaluated using a local open-source large language model (LLM) (Mistral-Small-3.2-24B-Instruct) across 16 parameters. The LLM-extracted information was integrated into the knowledge graph for automated AJCC staging classification and data consistency validation. The developed system was further validated using pathology reports from 88 radical prostatectomy patients in The Cancer Genome Atlas (TCGA) dataset. Results: Information extraction achieved an accuracy of 0.973 and an F1-score of 0.986 on the internal dataset, and 0.938 and 0.968, respectively, on external validation. AJCC staging classification showed macro-averaged F1-scores of 0.930 and 0.833 for the internal and external datasets, respectively. Knowledge graph-based validation detected data inconsistencies in 5 of 150 cases (3.3%). Conclusions: This study demonstrates the feasibility of automated AJCC staging through the integration of large language model information extraction and knowledge graph-based validation. The resulting system enables privacy-protected clinical decision support for cancer staging applications with extensibility to broader oncologic domains.

artificial intelligence↗
PubMed2004

AutoTutor: a tutor with dialogue in natural language.

AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer. It provides the student with positive, neutral, or negative feedback on the student's typed responses, pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects erroneous ideas, answers the student's questions, and summarizes answers. AutoTutor has produced learning gains of approximately .70 sigma for deep levels of comprehension.

Algorithms↗
PubMed2004

Automatic classification of dysfunctional thoughts: a feasibility test.

The identification of dysfunctional thoughts is a central effort in cognitive therapy. This paper describes the first version of a computer module that classifies dysfunctional thoughts automatically. It is part of COGNO, a system we are developing to give automatic feedback on dysfunctional thoughts. The system uses rules that were developed from language markers identified in a sample of 149 dysfunctional thoughts. The system was tested with an independent set of 112 example thoughts. The system detects the majority of dysfunctional thoughts, but works reliably only for some thought categories. Automatic thought classification may be a first step toward developing natural dialogue systems in cognitive therapy.

Artificial Intelligence↗
PubMed1992

Enabling technologies build bridge to 21st century.

The sophisticated healthcare institutions of the future will be computer-driven integrated clinical creatures. But which technologies are key to overall information access? Futurist Tim Zinn stalks tomorrow's "best of breed" I/S technologies.

Computer Communication Networks↗
PubMed1997

From natural language to formal language: when MultiTALE meets GALEN.

In the GALEN project, the syntactic-semantic tagger MultiTALE is upgraded to extract knowledge from natural language surgical procedure expressions. In this paper, we describe the methodology applied and show that out of a randomly selected sample of such expressions, 81% could be analysed correctly. The problems encountered are summarised and areas of further investigation identified.

Humans↗
PubMed1997

Using the GRAIL language for classification management.

This paper describes a novel approach in classification management where a formal model of medical semantics is being used for manipulations on existing classification systems. The paper addresses the issue of semi-automatically making specialist classifications that are compatible with the source classification. The examples in this paper are from a limited domain. At the time of the presentation results will be shown of the present modelling work within the GALEN-In-Use project. The model will then contain several thousands of medical procedures from four different classification centres.

Classification↗
PubMed1997

On different roles of natural language information in medicine.

In this paper the authors analyze the main different function types of language in medical environment in different communicative situations and descriptive tasks. These functions are categorized as knowledge transfer, documentation, directive function, expression of emotions. The computer representation of the information have to be different according to the different tasks. The paper highlights the most important differences and concludes that further research is necessary in the details.

Communication↗
PubMed1997

A second generation of terminological systems is coming.

Diverse achievements by recent computer-based terminological systems are outlining a new generation of systems (i.e. a "second generation"). We collected the relevant features of various advanced terminological systems and we systematized these features into four components of a unique framework. We review a set of systems according to our framework, and we discuss how standardization activities can support the evolution of computer-based terminological systems towards a complete set of new performances.

Humans↗
PubMed1998

Automatic extraction of linguistic knowledge from an international classification.

Automatic extraction of knowledge from large corpus of texts is an essential step toward linguistic knowledge acquisition in the medical domain. The current situation shows a lack of computer-readable large medical lexicons, with a partial exception for the English language. Moreover, multilingual lexicons with versatility for multiple languages applications are far from reach as long as only manual extraction is considered. Computer-assisted linguistic knowledge acquisition is a must. A multilingual lexicon differs from a monolingual one by the necessity to bridge the words in different languages. A kind of interlingua has to be built under the form of concepts to which the specific entries are attached. In the present approach, the authors have developed an intelligent rule-based tool in order to focus on a multilingual source of medical knowledge, like the International Classification of Disease (ICD) which contains a vocabulary of some 20,000 words, translated in numerous languages.

Disease↗
PubMed1998

Natural language generation of surgical procedures.

The GALEN-IN-USE project has developed a compositional scheme for the conceptual representation of surgical operative procedure rubrics. The complex representations which result are translated back to surface language by a tool for multilingual natural language generation. This generator can be adapted to the specific characteristics of the scheme by introducing particular definitions of concepts and relationships. We discuss how the generator uses such definitions to bridge between the modelling 'style' of the GALEN scheme and natural language.

Humans↗