Computer-aided Diagnosis of Acute Abdominal Pain

Dombal, F. T. de; Leaper, David; Staniland, J. R.; McCann, A. P.; Horrocks, Jane C.

doi:10.1136/bmj.2.5804.9

Cited by 596 publications

(193 citation statements)

References 7 publications

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“…Furthermore, existence of significant data, such as the type of seizure in the case we examined, offers us the opportunity to create a decision tree, which results in smaller, independent problems of diagnosis. For the evolution of those smaller diagnostic sub-systems, the use of different technologies is possible as Bayesian [5,21], ANN [3] or rules [4]. Since those systems are independent, each of them can be implemented through different methodologies.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial Intelligence in Medical applications have been proposed various well known methods, in order to formalize the medical knowledge, to standardize the diagnostic procedures in specific domains and to effectively store them in computer systems [5,4,18,20]. In practice, the computerized diagnostic systems take advantage of this wide stored information (named Knowledge Base), which «do not forget» and in a way through inference mechanisms, they mimic the doctor's procedure of thinking, by assisting him in conclusion making [16].…”

Section: Introductionmentioning

confidence: 99%

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Decision Support System for Classification of Epilepsies in Childhood

2002

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In Medicine, Information Technology has often been used to assist doctors in their diagnostic procedures, especially whenever there are problems of differential diagnosis in diseases. Methods from the domain of Artificial Intelligence give us the opportunity to formalize the medical knowledge and standardize various diagnostic procedures, in specific domains of Medicine and store them in computer systems (Knowledge Base). In the present research, we have developed a Decision Support System (DSS) for the disease of Epilepsy during childhood, when certain epileptic syndromes present difficulties in diagnosis and laboratory tests do not lead to specific results. The development of this specific DSS is based on the «International Classification of Epileptic Syndromes and Epilepsies», as is proposed by the «International League Against Epilepsy». In order to check the effectiveness of this diagnostic system we examine cases of patients with epileptic seizures. The results from the application of the Expert System is being compared with the diagnoses proposed by an experienced doctor.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision Support System for Classification of Epilepsies in Childhood

2002

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“…The current state of affairs in sequence analysis is analogous to that faced by the designers of Bayesian medical diagnostic systems (Gorry & Barnett, 1968;de Dombal et al, 1972). The strict assumption of conditional independence was also made with these early systems: the probability of seeing any 1 symptom, given a specific disease, was assumed to be independent of the presence or absence of any other finding.…”

Section: ~-mentioning

confidence: 99%

Discovering structural correlations in α‐helices

Klingler

Brutlag

1994

Protein Science

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We have developed a new representation for structural and functional motifs in protein sequences based on correlations between pairs of amino acids and applied it to a-helical and P-sheet sequences. Existing probabilistic methods for representing and analyzing protein sequences have traditionally assumed conditional independence of evidence. In other words, amino acids are assumed to have no effect on each other. However, analyses of protein structures have repeatedly demonstrated the importance of interactions between amino acids in conferring both structure and function. Using Bayesian networks, we are able to model the relationships between amino acids at distinct positions in a protein sequence in addition to the amino acid distributions at each position. We have also developed an automated program for discovering sequence correlations using standard statistical tests and validation techniques. In this paper, we test this program on sequences from secondary structure motifs, namely a-helices and @sheets. In each case, the correlations our program discovers correspond well with known physical and chemical interactions between amino acids in structures. Furthermore, we show that, using different chemical alphabets for the amino acids, we discover structural relationships based on the same chemical principle used in constructing the alphabet. This new representation of 3-dimensional features in protein motifs, such as those arising from structural or functional constraints on the sequence, can be used to improve sequence analysis tools including pattern analysis and database search.Keywords: a-helix structure; amino acid correlations; motif modeling; sequence analysis; side-chain interactions; structure analysis Understanding the 3-dimensional structure of a protein is a necessary and critical step toward understanding the protein's function. For example, only after the structure of hemoglobin was solved was it possible to dissect the mechanisms responsible for the cooperative binding of oxygen, for the effects of pH and 2-3-diphosphoglycerate (DPG) on affinity, and for the defects causing various anemias (Stryer, 1988). Despite the increasing wealth of sequence data, the laborious and time-consuming process of empirical structure determination hampers the availability of detailed structural information. Instead, sequence analysis tools offer the best hope for quickly eliciting structural and functional information from new sequences.Traditional methods for analyzing sequences rely on the prior analyses of known sequences and on procedures for matching sequences. These techniques encompass database search (Wilbur & Lipman, 1983), sequence classification (Klein et al., 1984;Klein & DeLisi, 1986), and analysis for motifs (Bairoch & Boeckmann, 1991;Henikoff & Henikoff, 1991) ~-techniques for both analysis and matching emphasize the conservation of amino acids during evolution. Specifically, one usually assumes that if 2 sequences are homologous, then the amino acids that one observes at corresponding loca...

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“…Moreover, several early systems appeared. The most well-known system was developed in Leeds University in 1972 [9] to diagnose abdomen pain using Bayesian probability theory. Later in 1976, another wellknown medical diagnosing system appeared, MYCIN [10].…”

Section: Expert Systems Bmentioning

confidence: 99%

An Emergency Unit Support System to Diagnose Chronic Heart Failure Embedded with SWRL and Bayesian Network

Al-Hamadani¹

2016

ijacsa

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Abstract-In all the regions of the world, heart failure is common and on raise caused by several aetiologies. Although the development of the treatment is fast, there are still lots of cases that lose their lives in emergence sections because of slow response to treat these cases. In this paper we propose an expert system that can help the practitioners in the emergency rooms to fast diagnose the disease and advise them with the appropriate operations that should be taken to save the patient's life. Based on the mostly binary information given to the system, Bayesian Network model was selected to support the process of reasoning under uncertain or missing information. The domain concepts and the relations between them were building by using ontology supported by the Semantic Web Rule Language to code the rules. The system was tested on 105 patients and several classification functions were tested and showed remarkable results in the accuracy and sensitivity of the system.

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Computer-aided Diagnosis of Acute Abdominal Pain

Cited by 596 publications

References 7 publications

Decision Support System for Classification of Epilepsies in Childhood

Decision Support System for Classification of Epilepsies in Childhood

Discovering structural correlations in α‐helices

An Emergency Unit Support System to Diagnose Chronic Heart Failure Embedded with SWRL and Bayesian Network

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