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Biomedical subjects

F Steimann

Publications and source records attributed to F Steimann.

9 recordsLinked to original sources
PubMed1999

Fuzzy diagnosis.

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Diagnosis, Computer-Assisted↗
PubMed1998

Dependency parsing for medical language and concept representation.

The theory of conceptual structures serves as a common basis for natural language processing and medical concept representation. We present a PROLOG-based formalization of dependency grammar that can accommodate conceptual structures in its dependency rules. First results indicate that this formalization provides an operational basis for the implementation of medical language parsers and for the design of medical concept representation languages.

Artificial Intelligence↗
PubMed1997

Fuzzy set theory in medicine.

Indeed, the complexity of biological systems may force us to alter in radical ways our traditional approaches to the analysis of such systems. Thus, we may have to accept as unavoidable a substantial degree of fuzziness in the description of the behavior of biological systems as well as in their characterization. This fuzziness, distasteful though it may be, is the price we have to pay for the ineffectiveness of precise mathematical techniques in dealing with systems comprising a very large number of interacting elements or involving a large number of variables in their decision trees.

Artificial Intelligence↗
PubMed1997

A method to derive the time of onset of infection from serological findings.

Among the diagnostic problems requiring a retrospective assessment of the time an event occurred is that of screening for primary infection with Toxoplasma gondii acquired during pregnancy. We suggest a method to derive the possible times of onset of infection from a small sequence of serological samples by matching them against the knowledge about possible courses of infection. Special care is taken to properly address the relative change of consecutive samples, a nontrivial problem when reasoning about sparsely sampled time courses. To investigate the practicability of our approach we conducted a retrospective and a simulated prospective evaluation based on the samples of 394 pregnancies, randomly selected from our toxoplasmosis database; we could demonstrate an overall accuracy of 95.7%.

Female↗
PubMed1996

The interpretation of time-varying data with DIAMON-1.

Applying the methods of Artificial Intelligence to clinical monitoring requires some kind of signal-to-symbol conversion as a prior step. Subsequent processing of the derived symbolic information must also be sensitive to history and development, as the failure to address temporal relationships between findings invariably leads to inferior results. DIAMON-1, a framework for the design of diagnostic monitors, provides two methods for the interpretation of time-varying data: one for the detection of trends based on classes of courses, and one for the tracking of disease histories modelled through deterministic automata. Both methods make use of fuzzy set theory taking account of the elasticity of medical categories and allowing discrete disease models to mirror the patient's continuous progression through the stages of illness.

Artificial Intelligence↗
PubMed1995

A case against logic.

Based on a simple example taken from Toxoplasma serology it is shown that employment of formal logic and its symbolic descendants is not always the best choice for supporting medical diagnosis.

Acute Disease↗
PubMedDate not supplied

On the use and usefulness of fuzzy sets in medical AI.

Since its inception fuzzy set theory has been regarded as a formalism suitable to deal with the imprecision intrinsic to many medical problems. Based on a literature survey on the first 30 years, we investigate the impact fuzzy set theory has had on the work in medical AI and point out what it is most appreciated for.

Artificial Intelligence↗