1,720,991 research outputs found
Predicting clinical outcomes in patients with traumatic bleeding: A secondary analysis of the CRASH-2 dataset
No full textShared Decision-Making Ontology for a Healthcare Team Executing a Workflow, an Instantiation for Metastatic Spinal Cord Compression Management
No full textRegardless of potential benefits and better outcomes, adoption of shared decision-making between a patient and providers involved in his/her care is still in its infancy. This paper intends to fill this gap by formalizing shared decision-making, situating it as part of team-based care delivery, and incorporating workflow concepts allowing for identification of shared decision-making tasks. We accomplish that by creating novel shared decision-making ontology which constitutes the first step required in the development of a decision support system for shared decision-making. The proposed ontology formally defines and describes the key concepts and relations in the shared decision-making domain and lays the foundation for the formalization and support of the patient management process. We illustrate the applicability of the proposed ontology by creating its instantiation for the complex patient management scenario involving shared decision-making about the treatment of metastatic spinal cord compression
Eliciting and Exploiting Utility Coefficients in an Integrated Environment for Shared Decision-Making
No full textBackground In shared decision-making, a key step is quantifying the patient's preferences in relation to all the possible outcomes of the compared clinical options. According to utility theory, this can be done by eliciting utility coefficients (UCs) from the patient. The obtained UCs are then used in decision models (e.g., decision trees). The elicitation process involves the choice of one or more elicitation methods, which is not easy for decision-makers who are unfamiliar with the theoretical framework. Moreover, to our knowledge there are no tools that integrate functionalities for UC elicitation with functionalities to run decision models that include the elicited values. Objectives The first aim of this work is to provide decision support to the clinicians for the selection of the elicitation method. The second aim is to bridge the gap between UC elicitation and the exploitation of those UCs in shared decision-making. Methods Based on evidence from the utility theory literature, we developed a set of production rules that recommend the optimal elicitation method(s) according to the patient's profile and health state. We then complemented this decision support tool with a functionality for quantifying and running decision trees defined through the commercial software TreeAge. Results The result is an integrated framework for shared decision-making. Given the primary aim of this work, we focus for result evaluation on the elicitation tool. It was tested on 51 volunteers, who expressed UCs for four purposely selected health states. The insights on the collected UCs validated the rules included in the decision support system. The usability of the tool was assessed through the System Usability Scale, obtaining positive results. Conclusion We developed an integrated environment to facilitate shared decision-making in the clinical practice. The next step is the validation of the entire framework and its use besides shared decision-making. As a matter of fact, it may also be exploited to target cost-utility analysis to a specific patient population
Patient similarity for precision medicine: A systematic review
No full textEvidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Why did AI get this one wrong? — Tree-based explanations of machine learning model predictions
No full textIncreasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to interpret and explain, culminating in black-box machine learning models. Model developers and users alike are often presented with a trade-off between performance and intelligibility, especially in high-stakes applications like medicine. In the present article we propose a novel methodological approach for generating explanations for the predictions of a generic machine learning model, given a specific instance for which the prediction has been made. The method, named AraucanaXAI, is based on surrogate, locally-fitted classification and regression trees that are used to provide post-hoc explanations of the prediction of a generic machine learning model. Advantages of the proposed XAI approach include superior fidelity to the original model, ability to deal with non-linear decision boundaries, and native support to both classification and regression problems. We provide a packaged, open-source implementation of the AraucanaXAI method and evaluate its behaviour in a number of different settings that are commonly encountered in medical applications of AI. These include potential disagreement between the model prediction and physician's expert opinion and low reliability of the prediction due to data scarcity
Agent-based models and spatial enablement: a simulation tool to improve health and wellbeing in big cities
No full textAs the percentage of the population living in urban areas is constantly increasing throughout the world, big cities’ municipalities and public health policy makers have to deal with raising socioeconomic disparities and need for environmental interventions to reduce pollution and improve wellbeing. The PULSE project, funded by the EU commission under the H2020 program, aims at providing an instrument that assesses health and wellbeing in cities through sensing technologies and data integration. The system has been deployed in 7 cities – Barcelona, Birmingham, Keelung, New York, Paris, Pavia and Singapore – and includes several state-of-the-art technologies, such as a smartphone App, a WebGIS, air quality sensors, a Decision Support System and dashboards. A crucial aspect of the project is the direct involvement of the citizens and the creation of Public Health Observatories (PHOs) that can help taking informed decisions and organize targeted interventions. To this end, PHOs are provided with powerful visual analytics to study different areas of the city, and with simulation tools that can be used to model the effect of interventions of public health authorities the city. In this paper, a first agent-based simulation model, based on the results of spatio-temporal data analytics, is presented. The model simulates the effect of traffic pollution, industrial land use and green areas on the probability of asthma hospitalizations in an area of East Harlem, one the neighborhoods with the highest asthma hospitalizations rate in New York City
How Do Spinal Surgeons Perceive The Impact of Factors Used in Post-Surgical Complication Risk Scores?
No full textWhen deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in the way spine surgeons perceive the importance of attributes used to calculate risk of post-operative and quantify the differences by building individual formal models of risk perceptions. We employ a preference-learning method - ROR-UTADIS - to build surgeon-specific additive value functions for risk of complications. Comparing these functions enables the identification and discussion of differences among personal perceptions of risk factors. Our results show there exist differences in surgeons' perceived factors including primary diagnosis, type of surgery, patient's age, body mass index, or presence of comorbidities
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