Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system

Nilsson, Markus; Funk, Peter; Olsson, Erik; Schéele, Bo von; Xiong, Naixue

doi:10.1016/j.artmed.2005.04.004

Cited by 39 publications

(31 citation statements)

References 26 publications

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“…In recent years, several CBR works dealing with cases with time series features have been published, in various application domains: robot control [22], process forecast [18,24], process supervision [8], pest management [6], prediction of faulty situations [11], and medical problems [26,25,19,17]. These approaches often rely on classical mathematical dimensionality reduction techniques, such as DFT [17] and DWT [19].…”

Section: Comparisons With Related Workmentioning

confidence: 99%

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Multi-level Abstractions and Multi-dimensional Retrieval of Cases with Time Series Features

Montani

Bottrighi

Leonardi

et al. 2009

Lecture Notes in Computer Science

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Abstract. Time series retrieval is a critical issue in all domains in which the observed phenomenon dynamics have to be dealt with. In this paper, we propose a novel, domain independent time series retrieval framework, based on Temporal Abstractions (TA). Our framework allows for multilevel abstractions, according to two dimensions, namely a taxonomy of (trend or state) symbols, and a variety of time granularities. Moreover, we allow for flexible querying, where queries can be expressed at any level of detail in both dimensions, also in an interactive fashion, and ground cases as well as generalized ones can be retrieved. We also take advantage of multi-dimensional orthogonal index structures, which can be refined progressively and on demand. The framework in practice is illustrated by means of a case study in hemodialysis.

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Section: Comparisons With Related Workmentioning

confidence: 99%

“…These approaches often rely on classical mathematical dimensionality reduction techniques, such as DFT [17] and DWT [19]. Sometimes (see e.g.…”

Section: Comparisons With Related Workmentioning

confidence: 99%

Multi-level Abstractions and Multi-dimensional Retrieval of Cases with Time Series Features

Montani

Bottrighi

Leonardi

et al. 2009

Lecture Notes in Computer Science

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“…Despite the above comments about the simplicity in the acquisition of a case, in several applications case representation is becoming more complex than in the past [8]: case data can partly come in the form of time series [9,10], or images [11,12], or free text [13], and can be intrinsically high-dimensional.…”

Section: Reasons For Failure and Improvement Directionsmentioning

confidence: 99%

“…Several recent systems address this point. Time series data pre-processing is pursued by means of different methodologies, such as Temporal Abstractions [24] in [25], filtering and distortion reduction techniques in [10]. Data dimensionality is reduced e.g.…”

Section: Dealing With Feature Miningmentioning

confidence: 99%

Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support

Montani

2007

Appl Intell

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Background Supporting medical decision making is a complex task, that offers challenging research issues to Artificial Intelligence (AI) scientists. The Case-based Reasoning (CBR) methodology has been proposed as a possible means for supporting decision making in this domain since the 1980s. Nevertheless, despite the variety of efforts produced by the CBR research community, and the number of issues properly handled by means of this methodology, the success of CBR systems in medicine is somehow limited, and almost no research product has been fully tested and commercialized; one of the main reasons for this may be found in the nature of the problem domain, which is extremely complex and multi-faceted.Materials and methods In this environment, we propose to design a modular architecture, in which several AI methodologies cooperate, to provide decision support. In the resulting context CBR, originally conceived as a well suited reasoning paradigm for medical applications, can extend its original roles, and cover a set of additional tasks.Results and conclusions As an example, in the paper we will show how CBR can be exploited for configuring the parameters relied upon by other (reasoning) modules. Other possible ways of deploying CBR in this domain will be the object of our future investigations, and, in our opinion, a possible research direction for people working on CBR in the health sciences.

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“…For example, a procedure using CBR for diagnosing stress-related disorder was put forwarded by Nilsson et al [15] where stress-related disorders were diagnosed by classifying the heart rate patterns. A CBR system was outlined in [2] where the cases were fuzzified depends on finger temperature changes for diagnosing stress in the psychophysiological domain, but it is not sufficient to depend on only the temperature changes for classifying individual sensitivity to stress.…”

Section: Related Workmentioning

confidence: 99%

Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning

Begum

Ahmed

Funk

et al. 2007

Case-Based Reasoning Research and Development

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Abstract. Increased exposure to stress may cause health problems. An experienced clinician is able to diagnose a person's stress level based on sensor readings. Large individual variations and absence of general rules make it difficult to diagnose stress and the risk of stress-related health problems. A decision support system providing clinicians with a second opinion would be valuable. We propose a novel solution combining case-based reasoning and fuzzy logic along with a calibration phase to diagnose individual stress. During calibration a number of individual parameters are established. The system also considers the feedback from the patient on how well the test was performed. The system uses fuzzy logic to incorporating the imprecise characteristics of the domain. The cases are also used for the individual treatment process and transfer experience between clinicians. The validation of the approach is based on close collaboration with experts and measurements from 24 persons used as reference.

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Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system

Cited by 39 publications

References 26 publications

Multi-level Abstractions and Multi-dimensional Retrieval of Cases with Time Series Features

Multi-level Abstractions and Multi-dimensional Retrieval of Cases with Time Series Features

Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support

Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning

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