International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California / The telemetry group has begun a new program in conjunction with Goddard Space Flight Center to investigate the possibilities of using parallel processing configurations for the real-time processing of Space Station data. In order to evaluate the potential configurations, a program based on using discrete-event simulation models is being used. This modeling software allows for generic configurations to be modeled and the relevant parameters to be modified to see the effects on performance. This paper represents a description of the work we will be undertaking over the next 18 months and the environment to be used in creating the simulation models at NMSU.

No description available.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2008. / Härtill 4 uppsatser.

No description available.

Electronic patient records, containing data about the health and care of a patient, are a valuable source of information for longitudinal clinical studies. The General Practice Research Database (GPRD) has collected patient records from UK primary care practices since the late 1980s. These records contain both structured data (in the form of codes and numeric values) and free text notes. While the structured data have been used extensively in clinical studies, there are significant practical obstacles in extracting information from the free text notes. The main obstacles are data access restrictions, due to the presence of sensitive information, and the specific language of medical practitioners, which renders standard language processing tools ineffective. The aim of this research is to investigate approaches for computer analysis of free text notes. The research involved designing a primary care text corpus (the Harvey Corpus) annotated with syntactic chunks and clinically-relevant semantic entities, developing a statistical chunking model, and devising a novel method for applying machine learning for entity recognition based on chunk annotation. The tools produced would facilitate reliable information extraction from primary care patient records, needed for the development of clinically-related research. The three medical concept types targeted in this thesis could contribute to epidemiological studies by enhancing the detection of co-morbidities, and better analysing the descriptions of patient experiences and treatments. The main contributions of the research reported in this thesis are: guidelines for chunk and concept annotation of clinical text, an approach to maximising agreement between human annotators, the Harvey Corpus, a method for using a standard part-of-speech tagging model in clinical text chunking, and a novel approach to recognising clinically relevant medical concepts.

The aim of this research was to invetigate the clinical decision-making processes used in podiatry and hence to investigate if a computer expert system could be used to aid the process of clinical decision-making. This was achieved through a sequence of four empirical studies. The initial study used card sorts to investigate seven expert podiatrists’ perceptions of and attitudes toward diagnostic aids, and in particular how podiatrists viewed expert systems. The results showed that expert systems are perceived as different in kind from other diagnostic aids such as X-rays or blood tests. The second study was conducted using one expert and one novice podiatrist and used a task analysis to investigate the types of tasks and skills undertaken by a podiatrist during the diagnosis of a patient in different clinical environments. The results indicate that the work is highly schematised and involves routine tasks such as nail care and callus reduction. In clinic, podiarists perform many tasks quickly. There was little difference between the number of tasks per minute undertaken in a general clinic and the number of tasks in a specialist diabetes clinic. Considering the speed of diagnosis, it is postulated that both expert and novice podiatrists’ use of schemata, pattern matching, and tacit and implicit knowledge dominates their diagnostic activity during consultations. The third study focused on how clinical reasoning and decision-making occur during consultations with a patient. Think-aloud protocols were used to investigate the differences in the clinical reasoning process between five expert and nine novice podiatrists. The speed of diagnosis and general lack of causal assertions suggest that use of schemata and tacit knowledge dominate the diagnosis process for both experts and novices. In a general setting, the novices produced four common clinical reasoning themes. These indicate that pattern recognition is a common method of diagnosis. However, there was an increase in the number of clinical reasoning themes used by experts in a specialist setting, indicating novice—expert differences. The fourth study used laddering interviews on a mixture of twelve NHS and private podiatrists to investigate why podiatrists used certain clinical reasoning themes. A hierarchical value map was derived, showing that, at an initial response level to the laddering questions, certain values were important: the palpation of the foot, building a picture of the foot condition, and being able to use clinical reasoning frequently and immediately. The emphases on palpation and immediacy of reasoning suggest that an expert system is unlikely to serve podiatrists’ needs in clinics. This research has provided a new understanding of the clinical reasoning processes used in podiatry. A podiatrist has a very busy timeline when diagnosing a patient and predominantly uses (and values) tacit knowledge, implicit learning, and compiled skills during consultations. There is little evidence for the need or desire for an expert system in clinical podiatry practice. However, if such an expert system were to be created, then: (a) it would have to be fast and non-intrusive so it can fit into a very busy consultation timeline, (b) it would need a knowledge base that could account for diagnosis of foot and leg conditions based on pattern recognition, and (c) it might be most valuable in the form of a decision support system for professional development that included the full range of expert diagnostic themes

Diss. Uppsala : Sveriges lantbruksuniv., 2004.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2004. / Härtill 6 uppsatser.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2001. / Härtill 8 uppsatser.

Diss. (sammanfattning) Umeå : Univ., 2006. / Härtill 7 uppsatser.