Surprising and novel multivariate sequential patterns using odds ratio for temporal evolution in healthcare.

BACKGROUND: Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of measures of interest for patterns are focused on reducing the number of patterns and quantifying their relevance (utility/usefulness). However, although the temporal dimension plays a key role in medical records, few efforts have been made to extract temporal knowledge about the patient's evolution from multivariate sequential patterns. METHODS: In this paper, we propose a method to extract a new type of patterns in the clinical domain called Jumping Diagnostic Odds Ratio Sequential Patterns (JDORSP). The aim of this method is to employ the odds ratio to identify a concise set of sequential patterns that represent a patient's state with a statistically significant protection factor (i.e., a pattern associated with patients that survive) and those extensions whose evolution suddenly changes the patient's clinical state, thus making the sequential patterns a statistically significant risk factor (i.e., a pattern associated with patients that do not survive), or vice versa. RESULTS: The results of our experiments highlight that our method reduces the number of sequential patterns obtained with state-of-the-art pattern reduction methods by over 95%. Only by achieving this drastic reduction can medical experts carry out a comprehensive clinical evaluation of the patterns that might be considered medical knowledge regarding the temporal evolution of the patients. We have evaluated the surprisingness and relevance of the sequential patterns with clinicians, and the most interesting fact is the high surprisingness of the extensions of the patterns that become a protection factor, that is, the patients that recover after several days of being at high risk of dying. CONCLUSIONS: Our proposed method with which to extract JDORSP generates a set of interpretable multivariate sequential patterns with new knowledge regarding the temporal evolution of the patients. The number of patterns is greatly reduced when compared to those generated by other methods and measures of interest. An additional advantage of this method is that it does not require any parameters or thresholds, and that the reduced number of patterns allows a manual evaluation.

Spatiotemporal Data Modelling for Epidemiological Research in Hospitals.

Nosocomial infections are a great source of concern for healthcare organizations. The spatial layout of hospitals and the movements of patients play significant roles in the spread of outbreaks. However, the existing models are ad-hoc for a specific hospital and research topic. This work shows the design of a data model to study the spread of infections among hospital patients. Its spatial dimension describes the hospital layout with several levels of detail, and the temporal dimension describes everything that happens to the patients in the form of events, which can relate to the spatial dimension. The model is meant to be sufficiently general to fit any hospital layout and to be used for different epidemiological research topics. We proved the model's suitability by defining six queries based on patients' movements and contacts that could assist in several epidemiological research tasks, such as discovering potential transmission routes. The model was implemented as an RDF* knowledge graph, and the queries were in SPARQL*. Finally, we designed two experiments in which two outbreaks of Clostridium difficile were analyzed using several queries (four in the first experiment and two in the second) on a knowledge graph (105,000 nodes, 185,000 edges) with synthetic data.

Spatial-temporal simulation for hospital infection spread and outbreaks of Clostridioides difficile.

Validated and curated datasets are essential for studying the spread and control of infectious diseases in hospital settings, requiring clinical information on patients' evolution and their location. The literature shows that approaches based on Artificial Intelligence (AI) in the development of clinical-support systems have benefits that are increasingly recognized. However, there is a lack of available high-volume data, necessary for trusting such AI models. One effective method in this situation involves the simulation of realistic data. Existing simulators primarily focus on implementing compartmental epidemiological models and contact networks to validate epidemiological hypotheses. Nevertheless, other practical aspects such as the hospital building distribution, shifts or safety policies on infections has received minimal attention. In this paper, we propose a novel approach for a simulator of nosocomial infection spread, combining agent-based patient description, spatial-temporal constraints of the hospital settings, and microorganism behavior driven by epidemiological models. The predictive validity of the model was analyzed considering micro and macro-face validation, parameter calibration based on literature review, model alignment, and sensitive analysis with an expert. This simulation model is useful in monitoring infections and in the decision-making process in a hospital, by helping to detect spatial-temporal patterns and predict statistical data about the disease.

The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review.

OBJECTIVES: To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission. METHODS: a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases. RESULTS: Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common. CONCLUSION: We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.

Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework.

Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.

Business Process Model and Notation and openEHR Task Planning for Clinical Pathway Standards in Infections: Critical Analysis.

BACKGROUND: Clinical pathways (CPs) are usually expressed by means of workflow formalisms, providing health care personnel with an easy-to-understand, high-level conceptual model of medical steps in specific patient conditions, thereby improving overall health care process quality in clinical practice. From a standardized perspective, the business process model and notation (BPMN), a widely spread general-purpose process formalism, has been used for conceptual modeling in clinical domains, mainly because of its easy-to-use graphical notation, facilitating the common understanding and communication of the parties involved in health care. However, BPMN is not particularly oriented toward the peculiarities of complex clinical processes such as infection diagnosis and treatment, in which time plays a critical role, which is why much of the BPMN clinical-oriented research has revolved around how to extend the standard to address these special needs. The shift from an agnostic, general-purpose BPMN notation to a natively clinical-oriented notation such as openEHR Task Planning (TP) could constitute a major step toward clinical process improvement, enhancing the representation of CPs for infection treatment and other complex scenarios. OBJECTIVE: Our work aimed to analyze the suitability of a clinical-oriented formalism (TP) to successfully represent typical process patterns in infection treatment, identifying domain-specific improvements to the standard that could help enhance its modeling capabilities, thereby promoting the widespread adoption of CPs to improve medical practice and overall health care quality. METHODS: Our methodology consisted of 4 major steps: identification of key features of infection CPs through literature review, clinical guideline analysis, and BPMN extensions; analysis of the presence of key features in TP; modeling of relevant process patterns of catheter-related bloodstream infection as a case study; and analysis and proposal of extensions in view of the results. RESULTS: We were able to easily represent the same logic applied in the extended BPMN-based process models in our case study using out-of-the-box standard TP primitives. However, we identified possible improvements to the current version of TP to allow for simpler conceptual models of infection CPs and possibly of other complex clinical scenarios. CONCLUSIONS: Our study showed that the clinical-oriented TP specification is able to successfully represent the most complex catheter-related bloodstream infection process patterns depicted in our case study and identified possible extensions that can help increase its adequacy for modeling infection CPs and possibly other complex clinical conditions.

Using the Diagnostic Odds Ratio to Select Patterns to Build an Interpretable Pattern-Based Classifier in a Clinical Domain: Multivariate Sequential Pattern Mining Study.

BACKGROUND: It is important to exploit all available data on patients in settings such as intensive care burn units (ICBUs), where several variables are recorded over time. It is possible to take advantage of the multivariate patterns that model the evolution of patients to predict their survival. However, pattern discovery algorithms generate a large number of patterns, of which only some are relevant for classification. OBJECTIVE: We propose to use the diagnostic odds ratio (DOR) to select multivariate sequential patterns used in the classification in a clinical domain, rather than employing frequency properties. METHODS: We used data obtained from the ICBU at the University Hospital of Getafe, where 6 temporal variables for 465 patients were registered every day during 5 days, and to model the evolution of these clinical variables, we used multivariate sequential patterns by applying 2 different discretization methods for the continuous attributes. We compared 4 ways in which to employ the DOR for pattern selection: (1) we used it as a threshold to select patterns with a minimum DOR; (2) we selected patterns whose differential DORs are higher than a threshold with regard to their extensions; (3) we selected patterns whose DOR CIs do not overlap; and (4) we proposed the combination of threshold and nonoverlapping CIs to select the most discriminative patterns. As a baseline, we compared our proposals with Jumping Emerging Patterns, one of the most frequently used techniques for pattern selection that utilizes frequency properties. RESULTS: We have compared the number and length of the patterns eventually selected, classification performance, and pattern and model interpretability. We show that discretization has a great impact on the accuracy of the classification model, but that a trade-off must be found between classification accuracy and the physicians' capacity to interpret the patterns obtained. We have also identified that the experiments combining threshold and nonoverlapping CIs (Option 4) obtained the fewest number of patterns but also with the smallest size, thus implying the loss of an acceptable accuracy with regard to clinician interpretation. The best classification model according to the trade-off is a JRIP classifier with only 5 patterns (20 items) that was built using unsupervised correlation preserving discretization and differential DOR in a beam search for the best pattern. It achieves a specificity of 56.32% and an area under the receiver operating characteristic curve of 0.767. CONCLUSIONS: A method for the classification of patients' survival can benefit from the use of sequential patterns, as these patterns consider knowledge about the temporal evolution of the variables in the case of ICBU. We have proved that the DOR can be used in several ways, and that it is a suitable measure to select discriminative and interpretable quality patterns.

Handling Time Constraints in Infection Clinical Pathways Using openEHR TP.

Clinical Pathways (CP) provide healthcare personnel with an easy-to-understand high level model of medical steps in specific patient conditions, thereby improving overall process quality in clinical practice. The emergence of new clinical-oriented standards such as openEHR Task Planning (TP) could pose a major step towards clinical process improvement, particularly in complex domains such as infection diagnosis and treatment, where time plays a critical role. In this work, we analyze the suitability of TP to successfully represent time constraints of common process patterns in infections, modelling some of the Catheter-Related Blood Stream Infection (CR-BSI) process patterns as a case study. Our research shows that TP is useful to represent time constraints of infection CPs, although minor improvements could increase its suitability not only for infection processes but for other time-related complex clinical scenarios.

Follow-up after failed newborn hearing screening: Parental and primary care provider awareness.

PURPOSE: The current loss to follow-up rate after failed newborn hearing screening (NBHS) is 34.4%. Previous studies have found that lack of parental and primary care provider (PCP) awareness of NBHS results are significant contributors to loss to follow-up. The objective of this study was to identify factors associated with parental and PCP awareness of NBHS results. MATERIALS AND METHODS: Retrospective cohort study. A survey asking about demographics and knowledge of NBHS testing and results was offered to parents in the waiting room of an urban pediatric primary care office. Included were biological parents ≥18 years of age of children ≤10 years of age born in Pennsylvania. Each child's chart was reviewed for PCP documentation of NBHS results. The odds of knowing NBHS results were evaluated using logistic regression. RESULTS: The survey was completed by 304 parents. 74.0% were aware of their child's NBHS results. Child age ≥1 year old (OR: 0.49, 95%CI[0.29, 0.82], P = 0.007) and Hispanic ethnicity (OR: 0.38, 95%CI[0.16, 0.89], P = 0.03) were associated with decreased odds of a parent knowing NBHS results. In addition, fewer fathers knew the results of their child's NBHS compared with mothers (OR: 0.33, 95%CI[0.18, 0.62], P < 0.001). However, parental awareness was not associated with birthing facility or insurance type. 222 charts were reviewed for NBHS documentation, revealing PCP awareness in 95.5% of cases and no associations with any of the factors examined. CONCLUSIONS: Factors associated with parents not knowing NBHS results included being the parent of an older child, Hispanic, or the father.

A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy.

BACKGROUND: The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspect for selecting an appropriate and adequate antimicrobial therapy is that prescription must be based on local epidemiology and knowledge since many aspects, such as prevalence of microorganisms and effectiveness of antimicrobials, change from hospitals, or even areas and services within a single hospital. Therefore, the selection of combinations of antibiotics requires the application of a methodology that provides objectivity, completeness and reproducibility to the analysis of the detailed microbiological, epidemiological, pharmacological information on which to base a rational and reasoned choice. METHODS: We proposed a methodology for decision making that uses a multiple criteria decision analysis (MCDA) to support the clinician in the selection of an efficient combined empiric therapy. The MCDA includes a multi-objective constrained optimization model whose criteria are the maximum efficacy of therapy, maximum activity, the minimum activity overlapping, the minimum use of restricted antibiotics, the minimum toxicity of antibiotics and the activity against the most prevalent and virulent bacteria. The decision process can be defined in 4 steps: (1) selection of clinical situation of interest, (2) definition of local optimization criteria, (3) definition of constraints for reducing combinations, (4) manual sorting of solutions according to patient's clinical conditions, and (5) selection of a combination. EXPERIMENTS AND RESULTS: In order to show the application of the methodology to a clinical case, we carried out experiments with antibiotic susceptibility tests in blood samples taken during a five years period at a university hospital. The validation of the results consists of a manual review of the combinations and experiments carried out by an expert physician that has explained the most relevant solutions proposed according to current clinical knowledge and their use. CONCLUSION: We show that with the decision process proposed, the physician is able to select the best combined therapy according to different criteria such as maximum efficacy, activity and minimum toxicity. A method for the recommendation of combined antibiotic therapy developed on the basis of a multi-objective optimization model may assist the physicians in the search for alternatives to the use of broad-spectrum antibiotics or restricted antibiotics for empirical therapy. The decision proposed can be easily reproduced for any local epidemiology and any different clinical settings.

Comprehensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines.

BACKGROUND: The over-use of antibiotics in clinical domains is causing an alarming increase in bacterial resistance, thus endangering their effectiveness as regards the treatment of highly recurring severe infectious diseases. Whilst Clinical Guidelines (CGs) focus on the correct prescription of antibiotics in a narrative form, Clinical Decision Support Systems (CDSS) operationalize the knowledge contained in CGs in the form of rules at the point of care. Despite the efforts made to computerize CGs, there is still a gap between CGs and the myriad of rule technologies (based on different logic formalisms) that are available to implement CDSSs in real clinical settings. OBJECTIVE: To helpCDSS designers to determine the most suitable rule-based technology (medical-oriented rules, production rules and semantic web rules) with which to model knowledge from CGs for the prescription of antibiotics. We propose a framework of criteria for this purpose that is extensible to more generic CGs. MATERIALS AND METHODS: Our proposal is based on the identification of core technical requirements extracted from both literature and the analysis of CGs for antibiotics, establishing three dimensions for analysis: language expressivity, interoperability and industrial aspects. We present a case study regarding the John Hopkins Hospital (JHH) Antibiotic Guidelines for Urinary Tract Infection (UTI), a highly recurring hospital acquired infection. We have adopted our framework of criteria in order to analyse and implement these CGs using various rule technologies: HL7 Arden Syntax, general-purpose Production Rules System (Drools), HL7 standard Rule Interchange Format (RIF), Semantic Web Rule Language (SWRL) and SParql Inference Notation (SPIN) rule extensions (implementing our own ontology for UTI). RESULTS: We have identified the main criteria required to attain a maintainable and cost-affordable computable knowledge representation for CGs. We have represented the JHH UTI CGs knowledge in a total of 12 Arden Syntax MLMs, 81 Drools rules and 154 ontology classes, properties and individuals. Our experiments confirm the relevance of the proposed set of criteria and show the level of compliance of the different rule technologies with the JHH UTI CGs knowledge representation. CONCLUSIONS: The proposed framework of criteria may help clinical institutions to select the most suitable rule technology for the representation of CGs in general, and for the antibiotic prescription domain in particular, depicting the main aspects that lead to Computer Interpretable Guidelines (CIGs), such as Logic expressivity (Open/Closed World Assumption, Negation-As-Failure), Temporal Reasoning and Interoperability with existing HIS and clinical workflow. Future work will focus on providing clinicians with suggestions regarding new potential steps for CGs, considering process mining approaches and CGs Process Workflows, the use of HL7 FHIR for HIS interoperability and the representation of Knowledge-as- a-Service (KaaS).

Impact of expert knowledge on the detection of patients at risk of antimicrobial therapy failure by clinical decision support systems.

Antimicrobial Susceptibility Tests (ASTs) are performed in hospitals to detect whether an infectious agent is resistant or susceptible to a set of antimicrobials. When AST results are available, the evaluation of the patient's antimicrobial therapy is a critical task to ensure its effectiveness against the found microorganism. Since not all the available antimicrobials can be tested in ASTs, clinicians rely on their expert knowledge to complement AST results and prescribe the most appropriate antimicrobials for each infection. Our goal is to help physicians in this task by improving the detection of antimicrobial therapies at risk of failure by Clinical Decision Support Systems (CDSSs). With this aim, we have incorporated the EUCAST expert rules in antimicrobial susceptibility testing into a CDSS to improve the results of ASTs. In order to achieve this, we have combined both ontologies and production rules. Furthermore, we have evaluated the impact of EUCAST expert rules on the detection of antimicrobial therapies at risk of failure. We performed a retrospective study with one year of clinical data, obtaining a total of 148 alerts from which 62 (41.9%) were based on the additional expert knowledge. Furthermore, the evaluation of the clinical relevance of 27 alerts resulted in 8 of them (29.7%) being clinically relevant. Of these, 6 were based on expert knowledge. Finally, an alarm fatigue study suggests that waiting between 48 and 72 h from the reception of the AST results can significantly reduce the number of alerts that are unnecessary in our CDSS because they are already being addressed in the hospital's daily workflow. In conclusion, we demonstrate that the incorporation of expert knowledge improves the capabilities of CDSSs as regards detecting the risk of antimicrobial therapy failure, which may improve the institutional outcomes in antimicrobial stewardship.

A decision support system for antibiotic prescription based on local cumulative antibiograms.

BACKGROUND: Local cumulative antibiograms are useful tools with which to select appropriate empiric or directed therapies when treating infectious diseases at a hospital. However, data represented in traditional antibiograms are static, incomplete and not well adapted to decision-making. METHODS: We propose a decision support method for empiric antibiotic therapy based on the Number Needed to Fail (NNF) measure. NNF indicates the number of patients that would need to be treated with a specific antibiotic for one to be inadequately treated. We define two new measures, Accumulated Efficacy and Weighted Accumulated Efficacy in order to determine the efficacy of an antibiotic. We carried out two experiments: the first during which there was a suspicion of infection and the patient had empiric therapy, and the second by considering patients with confirmed infection and directed therapy. The study was performed with 15,799 cultures with 356,404 susceptibility tests carried out over a four-year period. RESULTS: The most efficient empiric antibiotics are Linezolid and Vancomycin for blood samples and Imipenem and Meropenem for urine samples. In both experiments, the efficacies of recommended antibiotics are all significantly greater than the efficacies of the antibiotics actually administered (P < 0.001). The highest efficacy is obtained when considering 2 years of antibiogram data and 80% of the cumulated prevalence of microorganisms. CONCLUSION: This extensive study on real empiric therapies shows that the proposed method is a valuable alternative to traditional antibiograms as regards developing clinical decision support systems for antimicrobial stewardship.

Spatiotemporal data visualisation for homecare monitoring of elderly people.

OBJECTIVE: Elderly people who live alone can be assisted by home monitoring systems that identify risk scenarios such as falls, fatigue symptoms or burglary. Given that these systems have to manage spatiotemporal data, human intervention is required to validate automatic alarms due to the high number of false positives and the need for context interpretation. The goal of this work was to provide tools to support human action, to identify such potential risk scenarios based on spatiotemporal data visualisation. METHODS AND MATERIALS: We propose the MTA (multiple temporal axes) model, a visual representation of temporal information of the activity of a single person at different locations. The main goal of this model is to visualize the behaviour of a person in their home, facilitating the identification of health-risk scenarios and repetitive patterns. We evaluate the model's insight capacity compared with other models using a standard evaluation protocol. We also test its practical suitability of the MTA graphical model in a commercial home monitoring system. In particular, we implemented 8VISU, a visualization tool based on MTA. RESULTS: MTA proved to be more than 90% accurate in identify non-risk scenarios, independently of the length of the record visualised. When the spatial complexity was increased (e.g. number of rooms) the model provided good accuracy form up to 5 rooms. Therefore, user preferences and user performance seem to be balanced. Moreover, it also gave high sensitivity levels (over 90%) for 5-8 rooms. Fall is the most recurrent incident for elderly people. The MTA model outperformed the other models considered in identifying fall scenarios (66% of correctness) and was the second best for burglary and fatigue scenarios (36% of correctness). Our experiments also confirm the hypothesis that cyclic models are the most suitable for fatigue scenarios, the Spiral and MTA models obtaining most positive identifications. CONCLUSIONS: In home monitoring systems, spatiotemporal visualization is a useful tool for identifying risk and preventing home accidents in elderly people living alone. The MTA model helps the visualisation in different stages of the temporal data analysis process. In particular, its explicit representation of space and movement is useful for identifying potential scenarios of risk, while the spiral structure can be used for the identification of recurrent patterns. The results of the experiments and the experience using the visualization tool 8VISU proof the potential of the MTA graphical model to mine temporal data and to support caregivers using home monitoring infrastructures.

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

OBJECTIVE: This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. METHODS AND MATERIALS: The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. RESULTS: The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. CONCLUSIONS: Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization.

Temporal similarity measures for querying clinical workflows.

OBJECTIVE: In this paper, we extend a preliminary proposal and discuss in a deeper and more formal way an approach to evaluate temporal similarity between clinical workflow cases (i.e., executions of clinical processes). More precisely, we focus on (i) the representation of clinical processes by using a temporal conceptual workflow model; (ii) the definition of ad hoc temporal constraint networks to formally represent clinical workflow cases; (iii) the definition of temporal similarity for clinical workflow cases based on the comparison of temporal constraint networks; (iv) the management of the similarity of clinical processes related to the Italian guideline for stroke prevention and management (SPREAD). BACKGROUND: Clinical processes are composed by clinical activities to be done by given actors in a given order satisfying given temporal constraints. This description means that clinical processes can be seen as organizational processes, and modeled by workflow schemata. When a workflow schema represents a clinical process, its cases represent different instances derived from dealing with different patients in different situations. With respect to all the cases related to a workflow schema, each clinical case can be different with respect to its structure and to its temporal aspects. Clinical cases can be stored in clinical databases and information retrieval can be done evaluating the similarity between workflow cases. METHODOLOGY: We first describe a possible approach to the conceptual modeling of a clinical process, by using a temporally extended workflow model. Then, we define how a workflow case can be represented as a set of activities, and show how to express them through temporal constraint networks. Once we have built temporal constraint networks related to the cases to compare, we propose a similarity function able to evaluate the differences between the considered cases with respect to the order and duration of corresponding activities, and with respect to the presence/absence of some activities. RESULTS: In this work, we propose an approach to evaluate temporal similarity between workflow cases. The proposed approach can be used (i) to query clinical databases storing clinical cases representing activities related to the management of different patients in different situations; (ii) to evaluate the quality of the service comparing the similarity between a (possibly synthetic) case, perceived as the good one with respect to a given clinical situation, and the other clinical cases; and (iii) to retrieve a particular class of cases similar to an interesting one.

Quality of life in obese pregnant women: a longitudinal study.

OBJECTIVE: The purpose of this study was to assess the contribution of obesity on quality of a woman's life during pregnancy. STUDY DESIGN: At the Hospital of Gynecology and Obstetrics in León, Mexico, we followed-up 220 pregnant women (110 obese and 110 nonobese) who completed the 12-item short-form health survey at the beginning and during the third trimester of pregnancy. RESULTS: The mental component score was lower in obese than in nonobese women at the beginning of gestation and at the third trimester but increased in the entire group during pregnancy. The physical component score (PCS) decreased during pregnancy and was lower in obese than in nonobese pregnant women (43.5 vs 47.2; P = .01) at the third trimester. Baseline body mass index, weight gain, and complications during pregnancy were associated negatively with PCS (R2=0.11; P < .001 for the model). CONCLUSION: Baseline body mass index, weight gain, and complications during gestation are associated negatively with PCS of quality of life.

Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains.

OBJECTIVE: The aim of this work is to provide a theoretical framework which is sufficiently expressive to describe temporal evolution of diseases, and also to propose a diagnostic process for building explanations of patient's observed temporal evolution based on these disease descriptions. BACKGROUND: Model-based diagnosis (MBD) tackles the problem of troubleshooting systems by starting from a description of their structure and function (or behaviour). It is in this area where the use of deep causal models, as part of MBD systems, has shown its greater efficiency over classical rule based systems. From its beginnings, the temporal dimension was considered as an important component in MBD, since it makes it possible to define the dynamic behaviour. Several approaches have been proposed to represent time in MBD, enabling the representation of temporal concepts and relations, as well as the use of temporal reasoning mechanisms. METHODOLOGY: We first propose a temporal behavioural model (TBM), which allows us to capture the dynamics underlying temporal evolution of diseases and to include contextual information. Contextual information is required to model how contextual factors change the temporal evolution of diseases. The temporal component is modelled by fuzzy temporal constraints networks (FTCN), which makes the representation of quantitative and qualitative imprecise temporal information possible. We also provide a diagnostic process, which is based on a temporal adaptation of classical cover and differentiate method. RESULTS: The TBM and diagnostic process proposed provides a unique framework which addresses three problems not dealt with together so far: (a) the inclusion of contextual information, (b) the expressivity of the solution provided, and (c) the evaluation of the diagnostic hypotheses. This proposal demonstrates that the FTCN formalism provides mechanisms sufficiently expressive to cope with the intrinsic imprecision in the description of diseases' temporal evolution. The explanation generated provides the user with a complete picture of the temporal evolution of diseases and its causal links, thus allowing the appearance of repeated instances of the same disease through time. Mechanisms are provided which evaluate the credibility of alternative hypotheses, based on possibility theory. A prototype is presented along with a knowledge acquisition tool that guides medical experts in the model building process. CONCLUSIONS: In this paper, we propose a model that tightly couples methods from MBD area with constraint-based temporal reasoning techniques. The proposed model allows us to model complex contextual relationships in a compact way as well as providing solutions expressive enough to be used for decision support purposes. The solution provided conforms a causal network entailing the abnormal observations, including pathophysiological and etiological states. Furthermore, different instances of the same diagnostic hypotheses, located at different time instants, are also possible in the final solution. Finally, we provide an analysis of related and future works.