KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
http://hdl.handle.net/2117/22137
2024-03-28T12:39:42Z
2024-03-28T12:39:42Z
Understanding effects of cognitive rehabilitation under a knowledge discovery approach
García Rudolph, Alejandro
Gibert, Karina
http://hdl.handle.net/2117/386517
2023-04-23T15:11:34Z
2023-04-21T11:48:16Z
Understanding effects of cognitive rehabilitation under a knowledge discovery approach
García Rudolph, Alejandro; Gibert, Karina
Traumatic brain injury (TBI) is the leading cause of death and disability in children and young adults worldwide. Cognitive rehabilitation (CR) plans consist of a sequence of CR tasks targeting main cognitive functions. There is not enough on-field experience yet regarding which specific intervention (tasks or exercise assignment) is more appropriate to help therapists to design plans with significant effectiveness on patient improvement. The selection of specific tasks to be prescribed to the patient and the order in which they might be executed is currently decided by the therapists based on their experience. In this paper a new data mining methodology is proposed, combining several tools from Artificial Intelligence, clustering and post-processing analysis to identify regularities in the sequences of tasks in such a way that treatment profiles (classes) can be discovered. Due to the cumulative effect of rehabilitation tasks, small variations within the sequence of tasks performed by the patient do not significantly change the final outcomes in rehabilitation and makes it difficult to find discriminant rules by using the traditional machine learning inductive methods. However, by relaxing the formalization of the problem to find patterns that might include small variations, and introducing motif discovery techniques in the proposed methodology, the complexity of the neurorehabilitation phenomenon can be better captured and a global structure of successful treatment task sequences can be devised. Following this, the relationship between the discovered patterns and the CR treatment response are analyzed, offering a richer perspective than that provided by the single task focus traditionally used in the CR field. The paper provides a definition of the whole methodological approach proposed from a formal point of view, and its application to a real dataset. Comparisons with traditional AI approaches are also presented and the contribution of the proposed methodology to the AI field discussed.
© 2016 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
2023-04-21T11:48:16Z
García Rudolph, Alejandro
Gibert, Karina
Traumatic brain injury (TBI) is the leading cause of death and disability in children and young adults worldwide. Cognitive rehabilitation (CR) plans consist of a sequence of CR tasks targeting main cognitive functions. There is not enough on-field experience yet regarding which specific intervention (tasks or exercise assignment) is more appropriate to help therapists to design plans with significant effectiveness on patient improvement. The selection of specific tasks to be prescribed to the patient and the order in which they might be executed is currently decided by the therapists based on their experience. In this paper a new data mining methodology is proposed, combining several tools from Artificial Intelligence, clustering and post-processing analysis to identify regularities in the sequences of tasks in such a way that treatment profiles (classes) can be discovered. Due to the cumulative effect of rehabilitation tasks, small variations within the sequence of tasks performed by the patient do not significantly change the final outcomes in rehabilitation and makes it difficult to find discriminant rules by using the traditional machine learning inductive methods. However, by relaxing the formalization of the problem to find patterns that might include small variations, and introducing motif discovery techniques in the proposed methodology, the complexity of the neurorehabilitation phenomenon can be better captured and a global structure of successful treatment task sequences can be devised. Following this, the relationship between the discovered patterns and the CR treatment response are analyzed, offering a richer perspective than that provided by the single task focus traditionally used in the CR field. The paper provides a definition of the whole methodological approach proposed from a formal point of view, and its application to a real dataset. Comparisons with traditional AI approaches are also presented and the contribution of the proposed methodology to the AI field discussed.
aTLP: a color-based model of uncertainty to evaluate the risk of decisions based on prototypes
Gibert, Karina
Conti, Dante
http://hdl.handle.net/2117/386510
2023-10-19T07:34:00Z
2023-04-21T09:11:00Z
aTLP: a color-based model of uncertainty to evaluate the risk of decisions based on prototypes
Gibert, Karina; Conti, Dante
Clustering techniques find homogeneous and distinguishable prototypes. Careful interpretation of these prototypes is crucial to assist the experts to better organize this know-how and to really improve their decision-making processes. The Traffic Lights Panel was introduced in 2009 as a postprocessing tool to provide understanding of clustering prototypes. In this work, annotated Traffic Lights Panel (aTLP) is presented as an enrichment of the TLP to manage the intrinsic uncertainty related with prototypes themselves. The aTLP handles uncertainty through a quantification of the prototypes' purity based on the variation coefficients (VC) and an associated color-based uncertainty model, with two dimensions - tone and saturation - representing nominal trend and purity of the prototype. An application to a waste-water treatment plant in Slovenia, in a discrete and continuous approach, suggests that aTLP seems a useful and friendly tool able to reduce the gap between data mining and effective decision support, towards informed-decisions.
2023-04-21T09:11:00Z
Gibert, Karina
Conti, Dante
Clustering techniques find homogeneous and distinguishable prototypes. Careful interpretation of these prototypes is crucial to assist the experts to better organize this know-how and to really improve their decision-making processes. The Traffic Lights Panel was introduced in 2009 as a postprocessing tool to provide understanding of clustering prototypes. In this work, annotated Traffic Lights Panel (aTLP) is presented as an enrichment of the TLP to manage the intrinsic uncertainty related with prototypes themselves. The aTLP handles uncertainty through a quantification of the prototypes' purity based on the variation coefficients (VC) and an associated color-based uncertainty model, with two dimensions - tone and saturation - representing nominal trend and purity of the prototype. An application to a waste-water treatment plant in Slovenia, in a discrete and continuous approach, suggests that aTLP seems a useful and friendly tool able to reduce the gap between data mining and effective decision support, towards informed-decisions.
Classification based on rules and thyroids dysfunctions
Gibert, Karina
Sonicki, Z
http://hdl.handle.net/2117/386491
2023-04-23T15:12:43Z
2023-04-20T14:10:50Z
Classification based on rules and thyroids dysfunctions
Gibert, Karina; Sonicki, Z
Classification in ill-structured domains (ISD) is a difficult problem for the actual statistical and artificial intelligence techniques, because of the intrinsic characteristics of those domains. Classification based on rules is our proposal to overcome the limitations of Statistics and Artificial Intelligence techniques referred to in this particular context. In this paper, an application of the classification based on rules to a set of real data is presented. Data base is about thyroid function and data was provided by a hospital from Zagreb (Croatia) covering a period of two years.
This is the peer reviewed version of the following article: Gibert, K.; Sonicki, Z. Classification based on rules and thyroids dysfunctions. "Applied stochastic models and data analysis", Octubre 1999, vol. 15, núm. 4, p. 319-324, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1526-4025(199910/12)15:4%3C319::AID-ASMB396%3E3.0.CO;2-H/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
2023-04-20T14:10:50Z
Gibert, Karina
Sonicki, Z
Classification in ill-structured domains (ISD) is a difficult problem for the actual statistical and artificial intelligence techniques, because of the intrinsic characteristics of those domains. Classification based on rules is our proposal to overcome the limitations of Statistics and Artificial Intelligence techniques referred to in this particular context. In this paper, an application of the classification based on rules to a set of real data is presented. Data base is about thyroid function and data was provided by a hospital from Zagreb (Croatia) covering a period of two years.
Ontology-driven web-based semantic similarity
Sánchez Ruenes, David
Batet, Montserrat
Valls Mateu, Aïda
Gibert, Karina
http://hdl.handle.net/2117/386489
2023-10-19T07:26:32Z
2023-04-20T14:06:30Z
Ontology-driven web-based semantic similarity
Sánchez Ruenes, David; Batet, Montserrat; Valls Mateu, Aïda; Gibert, Karina
Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge—such as the structure of a taxonomy—or implicit knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.
The version of record is available online at: http://dx.doi.org/10.1007/s10844-009-0103-x
2023-04-20T14:06:30Z
Sánchez Ruenes, David
Batet, Montserrat
Valls Mateu, Aïda
Gibert, Karina
Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge—such as the structure of a taxonomy—or implicit knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.
Advantages of combining AI and statistic for knowledge discovery on functional disability: multivariate analysis of assessment scales using clustering based on rules
Gibert, Karina
Annicchiarico, Roberta
Cortés García, Claudio Ulises
Caltagirone, Carlo
http://hdl.handle.net/2117/386488
2024-03-03T20:33:47Z
2023-04-20T14:02:20Z
Advantages of combining AI and statistic for knowledge discovery on functional disability: multivariate analysis of assessment scales using clustering based on rules
Gibert, Karina; Annicchiarico, Roberta; Cortés García, Claudio Ulises; Caltagirone, Carlo
In Europe, and other developed areas, senior citizens are a fast growing part of population. This increases proportion of disabled persons and proportion of persons with reduced quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on the concept of disability, the World Health Organization (WHO) developed the clinical test WHO Disability Assessment Schedule, (WHO-DASII) that includes physical, mental, and social wellbeing, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge about the different kinds of disabilities from the responses to the WHO-DAS II of a sample of patients from an Italian hospital. This Knowledge Discovery problem has been faced by using clustering based on rules, an hybrid AI and Statistics technique introduced by Gibert (1994), which combines some Inductive Learning (from AI) with clustering (from Statistics) to extract knowledge from certain complex domains in form of typical profiles. In this paper, the results of applying this technique to the WHODAS II results is presented together with a comparison of other more classical analysis approaches. Four profiles of increasing degree of disability are identified together with the main characteristics associated to them.
2023-04-20T14:02:20Z
Gibert, Karina
Annicchiarico, Roberta
Cortés García, Claudio Ulises
Caltagirone, Carlo
In Europe, and other developed areas, senior citizens are a fast growing part of population. This increases proportion of disabled persons and proportion of persons with reduced quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on the concept of disability, the World Health Organization (WHO) developed the clinical test WHO Disability Assessment Schedule, (WHO-DASII) that includes physical, mental, and social wellbeing, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge about the different kinds of disabilities from the responses to the WHO-DAS II of a sample of patients from an Italian hospital. This Knowledge Discovery problem has been faced by using clustering based on rules, an hybrid AI and Statistics technique introduced by Gibert (1994), which combines some Inductive Learning (from AI) with clustering (from Statistics) to extract knowledge from certain complex domains in form of typical profiles. In this paper, the results of applying this technique to the WHODAS II results is presented together with a comparison of other more classical analysis approaches. Four profiles of increasing degree of disability are identified together with the main characteristics associated to them.
Definición de "dependencia funcional": implicaciones para la política sociosanitaria
Salvador Carulla, Luís
Gibert, Karina
Ochoa, Susana
http://hdl.handle.net/2117/386487
2023-10-19T07:23:49Z
2023-04-20T13:57:03Z
Definición de "dependencia funcional": implicaciones para la política sociosanitaria
Salvador Carulla, Luís; Gibert, Karina; Ochoa, Susana
2023-04-20T13:57:03Z
Salvador Carulla, Luís
Gibert, Karina
Ochoa, Susana
A preliminary taxonomy and a standard knowledge base for mental-health system indicators in Spain
Salvador Carulla, Luís
Salinas Pérez, José Alberto
Martín Carrasco, Manuel
Grané Alsina, Montserrat
Gibert, Karina
Roca Adrover, Miquel Jesus
Balbuena Díaz-Pinés, Antonio
http://hdl.handle.net/2117/386486
2023-10-19T07:15:59Z
2023-04-20T13:54:17Z
A preliminary taxonomy and a standard knowledge base for mental-health system indicators in Spain
Salvador Carulla, Luís; Salinas Pérez, José Alberto; Martín Carrasco, Manuel; Grané Alsina, Montserrat; Gibert, Karina; Roca Adrover, Miquel Jesus; Balbuena Díaz-Pinés, Antonio
2023-04-20T13:54:17Z
Salvador Carulla, Luís
Salinas Pérez, José Alberto
Martín Carrasco, Manuel
Grané Alsina, Montserrat
Gibert, Karina
Roca Adrover, Miquel Jesus
Balbuena Díaz-Pinés, Antonio
Knowledge-driven delivery of home care services
Batet, Montserrat
Isern, David
Marín, Lucas
Martínez Pérez, Sergio
Moreno Ribas, Antonio
Sánchez Ruenes, David
Valls Mateu, Aïda
Gibert, Karina
http://hdl.handle.net/2117/386485
2023-10-19T07:15:01Z
2023-04-20T13:49:07Z
Knowledge-driven delivery of home care services
Batet, Montserrat; Isern, David; Marín, Lucas; Martínez Pérez, Sergio; Moreno Ribas, Antonio; Sánchez Ruenes, David; Valls Mateu, Aïda; Gibert, Karina
Home Care (HC) assistance is emerging as an effective and efficient alternative to institutionalized care, especially for the case of senior patients that present multiple co-morbidities and require life long treatments under continuous supervision. The care of such patients requires the definition of specially tailored treatments and their delivery involves the coordination of a team of professionals from different institutions, requiring the management of many kinds of knowledge (medical, organizational, social and procedural). The K4Care project aims to assist the HC of elderly patients by proposing a standard HC model and implementing it in a knowledge-driven e-health platform aimed to support the provision of HC services.
The version of record is available online at: http://dx.doi.org/10.1007/s10844-010-0145-0
2023-04-20T13:49:07Z
Batet, Montserrat
Isern, David
Marín, Lucas
Martínez Pérez, Sergio
Moreno Ribas, Antonio
Sánchez Ruenes, David
Valls Mateu, Aïda
Gibert, Karina
Home Care (HC) assistance is emerging as an effective and efficient alternative to institutionalized care, especially for the case of senior patients that present multiple co-morbidities and require life long treatments under continuous supervision. The care of such patients requires the definition of specially tailored treatments and their delivery involves the coordination of a team of professionals from different institutions, requiring the management of many kinds of knowledge (medical, organizational, social and procedural). The K4Care project aims to assist the HC of elderly patients by proposing a standard HC model and implementing it in a knowledge-driven e-health platform aimed to support the provision of HC services.
Semantic similarity estimation from multiple ontologies
Batet, Montserrat
Sánchez Ruenes, David
Valls Mateu, Aïda
Gibert, Karina
http://hdl.handle.net/2117/386482
2023-10-19T07:14:12Z
2023-04-20T13:38:07Z
Semantic similarity estimation from multiple ontologies
Batet, Montserrat; Sánchez Ruenes, David; Valls Mateu, Aïda; Gibert, Karina
The version of record is available online at: http://dx.doi.org/10.1007/s10489-012-0355-y
2023-04-20T13:38:07Z
Batet, Montserrat
Sánchez Ruenes, David
Valls Mateu, Aïda
Gibert, Karina
Evaluation of adherence to nutritional intervention through trajectory analysis
Sevilla-Villanueva, Beatriz
Gibert, Karina
Sànchez-Marrè, Miquel
Fitó Colomer, Montserrat
Covas, Maria Isabel
http://hdl.handle.net/2117/386477
2023-10-19T07:13:22Z
2023-04-20T13:28:43Z
Evaluation of adherence to nutritional intervention through trajectory analysis
Sevilla-Villanueva, Beatriz; Gibert, Karina; Sànchez-Marrè, Miquel; Fitó Colomer, Montserrat; Covas, Maria Isabel
Classical Pre-Post Intervention Studies are often analyzed using traditional statistics. Nevertheless, the nutritional interventions have small effects on the metabolism and traditional statistics are not enough to detect these subtle nutrient effects. Generally, this kind of studies assumes that the participants are adhered to the assigned dietary intervention and directly analyzes its effects over the target parameters. Thus, the evaluation of adherence is generally omitted. Although, sometimes, participants do not effectively adhere to the assigned dietary guidelines. For this reason, the Trajectory Map is proposed as a visual tool where dietary patterns of individuals can be followed during the intervention and can also be related with nutritional prescriptions. The Trajectory Analysis is also proposed allowing both analysis: 1) adherence to the intervention and 2) intervention effects. The analysis is made by projecting the differences of the target parameters over the resulting trajectories between states of different time-stamps which might be considered either individually or by groups. The proposal has been applied over a real nutritional study showing that some individuals adhere better than others and some individuals of the control group modify their habits during the intervention. In addition, the intervention effects are different depending on the type of individuals, even some subgroups have opposite response to the same intervention.
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2023-04-20T13:28:43Z
Sevilla-Villanueva, Beatriz
Gibert, Karina
Sànchez-Marrè, Miquel
Fitó Colomer, Montserrat
Covas, Maria Isabel
Classical Pre-Post Intervention Studies are often analyzed using traditional statistics. Nevertheless, the nutritional interventions have small effects on the metabolism and traditional statistics are not enough to detect these subtle nutrient effects. Generally, this kind of studies assumes that the participants are adhered to the assigned dietary intervention and directly analyzes its effects over the target parameters. Thus, the evaluation of adherence is generally omitted. Although, sometimes, participants do not effectively adhere to the assigned dietary guidelines. For this reason, the Trajectory Map is proposed as a visual tool where dietary patterns of individuals can be followed during the intervention and can also be related with nutritional prescriptions. The Trajectory Analysis is also proposed allowing both analysis: 1) adherence to the intervention and 2) intervention effects. The analysis is made by projecting the differences of the target parameters over the resulting trajectories between states of different time-stamps which might be considered either individually or by groups. The proposal has been applied over a real nutritional study showing that some individuals adhere better than others and some individuals of the control group modify their habits during the intervention. In addition, the intervention effects are different depending on the type of individuals, even some subgroups have opposite response to the same intervention.