Abstract Functional dependencies ( FD s) typically represent associations over facts stored by a database, such as “ patients with the same symptom get the same therapy .” In more recent years, some ...extensions have been introduced to represent both temporal constraints (temporal functional dependencies – TFD s), as “ for any given month, patients with the same symptom must have the same therapy, but their therapy may change from one month to the next one ,” and approximate properties (approximate functional dependencies – AFD s), as “ patients with the same symptom generally have the same therapy .” An AFD holds most of the facts stored by the database, enabling some data to deviate from the defined property: the percentage of data which violate the given property is user-defined. According to this scenario, in this paper we introduce approximate temporal functional dependencies ( ATFD s) and use them to mine clinical data. Specifically, we considered the need for deriving new knowledge from psychiatric and pharmacovigilance data. ATFD s may be defined and measured either on temporal granules (e.g. grouping data by day, week, month, year) or on sliding windows (e.g. a fixed-length time interval which moves over the time axis): in this regard, we propose and discuss some specific and efficient data mining techniques for ATFD s. We also developed two running prototypes and showed the feasibility of our proposal by mining two real-world clinical data sets. The clinical interest of the dependencies derived considering the psychiatry and pharmacovigilance domains confirms the soundness and the usefulness of the proposed techniques.
Functional dependencies (FDs) allow us to represent database constraints, corresponding to requirements as “
patients having the same symptoms undergo the same medical tests
.” Some research efforts ...have focused on extending such dependencies to consider also temporal constraints such as “
patients having the same symptoms undergo in the next period the same medical tests
.” Temporal functional dependencies are able to represent such kind of temporal constraints in relational databases. Another extension for FDs allows one to represent approximate functional dependencies (AFDs), as “
patients with the same symptoms
generally
undergo the same medical tests
.” It enables data to deviate from the defined constraints according to a user-defined percentage. Approximate temporal functional dependencies (ATFDs) merge the concepts of temporal functional dependency and of approximate functional dependency. Among the different kinds of ATFD, the
Approximate Pure Temporally Evolving Functional Dependencies
(
APE
-FDs for short) allow one to detect patterns on the evolution of data in the database and to discover dependencies as “
For most patients with the same initial diagnosis, the same medical test is prescribed after the occurrence of same symptom
.” Mining ATFDs from large databases may be computationally expensive. In this paper, we focus on
APE
-FDs and prove that, unfortunately, verifying a single
APE
-FD over a given database instance is in general NP-complete. In order to cope with this problem, we propose a framework for mining complex
APE
-FDs in real-world data collections. In the framework, we designed and applied sound and advanced model-checking techniques. To prove the feasibility of our proposal, we used real-world databases from two medical domains (namely, psychiatry and pharmacovigilance) and tested the running prototype we developed on such databases.
Abstract 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.
Healthcare processes are by nature complex, mostly due to their multidisciplinary character that requires continuous coordination between care providers. They encompass both organizational and ...clinical tasks, the latter ones driven by medical knowledge, which is inherently incomplete and distributed among people having different expertise and roles. Care pathways refer to planning and coordination of care processes related to specific groups of patients in a given setting. The goal in defining and following care pathways is to improve the quality of care in terms of patient satisfaction, costs reduction, and medical outcome. Thus, care pathways are a promising methodological tool for standardizing care and decision-making. Business process management techniques can successfully be used for representing organizational aspects of care pathways in a standard, readable, and accessible way, while supporting process development, analysis, and re-engineering. In this paper, we introduce a methodological framework that fosters the integrated design, implementation, and enactment of care processes and related decisions, while considering proper representation and management of organizational and clinical information. We focus here and discuss in detail the design phase, which encompasses the simulation of care pathways. We show how business process model and notation (BPMN) and decision model and notation (DMN) can be combined for supporting intertwined aspects of decision-intensive care pathways. As a proof-of-concept, the proposed methodology has been applied to design care pathways related to chronic obstructive pulmonary disease (COPD) in the region of Veneto, in Italy.
Biologic drugs have revolutionized the treatments in many therapeutic areas and their pre and post-marketing evaluation is crucial for investigating their benefit-risk profile. The VALORE project is ...an AIFA (Italian Medicines Agency)-funded research project aiming at establishing a multi-regional network for the integrated analysis of healthcare and clinical data from different sources. Here we deal with the design and implementation of a data mart supporting the multidimensional data analysis. We also discuss the design of a portable architecture, making each regional center autonomous with respect to the required analysis. It is a first step in the direction of providing all the regional centers and AIFA with a common tool for the analysis and monitoring of marketed biologic drugs.
The modelling and management of temporal constraints over business processes has received some attention in the past years. Recently, we have introduced and discussed the concept of "controllability" ...for workflow schemata modelling real world business processes: controllability, originally introduced in the AI community for temporal constraint networks, refers to the capability of executing a workflow for all possible durations of all tasks. In this paper, we first extend the execution strategy proposed by Morris, Muscettola and Vidal in the context of temporal constraint networks, to deal with the execution of business processes in a more suitable way. Then, we discuss and propose a new algorithm to deal with the (dynamic) controllability of an overall workflow schema, where several, possibly disjoint, execution paths are possible, due to the presence of alternative paths in the workflow schema. We show that the presence of several controllable alternative execution paths in a workflow schema does not guarantee that the overall workflow schema is controllable.
In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes ...were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th–11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics—IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.
Conceptual modeling of flexible temporal workflows Combi, Carlo; Gozzi, Matteo; Posenato, Roberto ...
ACM transactions on autonomous and adaptive systems,
07/2012, Letnik:
7, Številka:
2
Journal Article
Recenzirano
Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing business processes. The management of temporal aspects in the definition of a workflow ...process has been considered only recently in the literature. Currently available Workflow Management Systems (
WfMS
) and research prototypes offer a very limited support for the definition, detection, and management of temporal constraints over business processes. In this article, we propose a new advanced workflow conceptual model for expressing time constraints in business processes and we present a general technique to check different levels of temporal consistency for workflow schemata at process design time: since a time constraint can be satisfied in different ways, we propose a classification of temporal workflows according to the way time constraints are satisfied. Such classification can be used to successfully manage flexible workflows at runtime.
PDF
