Summary
Objectives
: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt ...Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions.
Methods
: We included PubMed and Web of Science
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publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies.
Results
: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data.
Conclusions
: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.
Process model discovery covers the different methodologies used to mine a process model from traces of process executions, and it has an important role in artificial intelligence research. Current ...approaches in this area, with a few exceptions, focus on determining a model of the flow of actions only. However, in several contexts, (i) restricting the attention to actions is quite limiting, since the effects of such actions also have to be analyzed, and (ii) traces provide additional pieces of information in the form of states (i.e., values of parameters possibly affected by the actions); for instance, in several medical domains, the traces include both actions and measurements of patient parameters. In this paper, we propose AS-SIM (Action-State SIM), the first approach able to mine a process model that comprehends two distinct classes of nodes, to capture both actions and states.
The traces of process executions are a strategic source of information, from which a model of the process can be mined. In our recent work, we have proposed SIM (semantic interactive miner), an ...innovative process mining tool to discover the process model incrementally: it supports the interaction with domain experts, who can selectively merge parts of the model to achieve compactness, generalization, and reduced redundancy. We now propose a substantial extension of SIM, making it able to exploit (both automatically and interactively) pre-encoded taxonomic knowledge about the refinement (ISA relations) and composition (part-of relations) of process activities, as is available in many domains. The extended approach allows analysts to move from a process description where activities are reported at the ground level to more user-interpretable/compact descriptions, in which sets of such activities are abstracted into the “macro-activities” subsuming them or constituted by them. An experimental evaluation based on a real-world setting (stroke management) illustrates the advantages of our approach.
We aimed to evaluate the degree of realism and involvement, stress management and awareness of performance improvement in practitioners taking part in high fidelity simulation (HFS) training program ...for delivery room (DR) management, by means of a self-report test such as flow state scale (FSS).
This is an observational pretest-test study. Between March 2016 and May 2019, fourty-three practitioners (physicians, midwives, nurses) grouped in multidisciplinary teams were admitted to our training High Fidelity Simulation center. In a time-period of 1 month, practitioners attended two HFS courses (model 1, 2) focusing on DR management and resuscitation maneuvers. FSS test was administred at the end of M1 and M2 course, respectively.
FSS scale items such as unambiguous feed-back, loss of self consciousness and loss of time reality, merging of action and awareness significantly improved (P < 0.05, for all) between M1 and M2.
The present results showing the high level of practitioner involvement during DR management-based HFS courses support the usefulness of HFS as a trustworthy tool for improving the awareness of practitioner performances and feed-back. The data open the way to the usefulness of FSS as a trustworthy tool for the evaluation of the efficacy of training programs in a multidisciplinary team.
In this paper, we introduce a telemedicine architecture for supporting emergency patient stabilization and patient transportation to a fully equipped health care center. In particular, we focus on ...the description of a set of mobile apps, designed for supporting data recording and transmission during patient transportation by ambulance. Some of the apps are interfaced to the monitoring devices in the ambulance, and automatically send all the recorded data to a server at the destination center. One additional app enables the travelling personnel to input and transmit further significant patient data, or comments. At the destination center, the specialist physician is allowed to inspect the data as soon as they are received, possibly providing immediate advice. The exploitation of the apps also allows to maintain the transportation data over time, for medico-legal purposes, or to perform a-posteriori analyses. Some first evaluation results are discussed in the paper.
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•We increase deep learning classification explainability for medical process traces.•Motivating application: stroke management quality assessment.•Trace saliency maps: a novel tool to ...“open the black box”.•Positive evaluation results on the use case.•Transferrable to other black box models, to be tested in other domains.
Medical process trace classification exploits the activity sequences logged by an healthcare organization to classify traces themselves on the basis of some performance properties; this information can be used for quality assessment. State-of-the-art process trace classification resorts to deep learning, a very powerful technique which however suffers from the lack of explainability.
In this paper we aim at addressing this issue, motivated by a relevant application, i.e., the classification of process traces for quality assessment in stroke management. To this end we introduce the novel concept of trace saliency maps, an instrument able to highlight what trace activities are particularly significant for the classification task. Through trace saliency maps we justify the output of the deep learning architecture, and make it more easily interpretable to medical users. The good results in our use case have shown the feasibility of the approach, and let us make the hypothesis that it might be translated to other application settings and to other black box learners as well.
This article presents an affective-based sensemaking system for grouping and suggesting stories created by the users about the cultural artefacts in a museum. By relying on the
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commonsense reasoning framework, the system exploits the spatial structure of the Plutchik's "wheel of emotions" to organize the stories according to their extracted emotions. The process of emotion extraction, reasoning, and suggestion is triggered by an app, called GAMGame, and integrated with the sensemaking engine. Following the framework of Citizen Curation, the system allows classifying and suggesting stories encompassing cultural items able to evoke not only the very same emotions of already experienced or preferred museum objects but also novel items sharing different emotional stances and, therefore, able to break the filter bubble effect and open the users' view toward more inclusive and empathy-based interpretations of cultural content. The system has been designed tested, in the context of the H2020EU SPICE project (Social cohesion, Participation, and Inclusion through Cultural Engagement), in cooperation with the community of the d/Deaf and on the collection of the Gallery of Modern Art (GAM) in Turin. We describe the user-centered design process of the web app and of its components and we report the results concerning the effectiveness of the diversity-seeking, affective-driven, recommendations of stories.
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•We provide a multi-level medical process trace abstraction mechanism.•Multi-level semantic abstraction manages delays and interleaved actions.•We extend a similarity metric to ...compare abstracted traces.•We provide abstracted traces as an input to semantic process discovery.•In experiments abstraction hides irrelevant details while maintaining key behavior.
Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept. Trace comparison resorts to a similarity metric able to take into account abstraction phase penalties, and to deal with quantitative and qualitative temporal constraints in abstracted traces. As for process discovery, we rely on classical algorithms embedded in the framework ProM, made semantic by the capability of abstracting the actions on the basis of their conceptual meaning. The approach has been tested in stroke care, where we adopted abstraction and trace comparison to cluster event logs of different stroke units, to highlight (in)correct behavior, abstracting from details. We also provide process discovery results, showing how the abstraction mechanism allows to obtain stroke process models more easily interpretable by neurologists.
•We provide a multi-level process execution trace abstraction mechanism.•Multi-level abstraction manages delays and interleaving actions.•We extend a similarity metric to compare abstracted ...traces.•We provide abstracted traces as an input to process discovery.•In experiments abstraction hides irrelevant details while maintaining key behavior.
Many information systems record executed process instances in the event log, a very rich source of information for several process management tasks, like process mining and trace comparison. In this paper, we present a framework, able to convert activities in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Our abstraction mechanism manages non trivial situations, such as interleaved activities or delays between two activities that abstract to the same concept.
Abstracted traces can then be provided as an input to an intelligent system, meant to implement a variety of process management tasks, significantly enhancing the quality and the usefulness of its output.
In particular, in the paper we demonstrate how trace abstraction can impact on the quality of process discovery, showing that it is possible to obtain more readable and understandable process models.
We also prove, through our experimental results, the impact of our approach on the capability of trace comparison and clustering (realized by means of a metric able to take into account abstraction phase penalties) to highlight (in)correct behaviors, abstracting from details.
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