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نتیجه جستجو - Clinical decision support

تعداد مقالات یافته شده: 11
ردیف عنوان نوع
1 Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review
سیستم های پشتیبانی از تصمیم گیری بالینی برای آزمایش در بخش اورژانس با استفاده از سیستم های هوشمند: یک مرور-2020
Motivation: Emergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage. Objectives: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation. Methods: We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency room” and concepts within the field of intelligent systems. Results: From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patients prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage. Conclusions: In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals’ decision-making thereby leading to better clinical management and patients’ outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.
Keywords: Triage | CDSS | EHR | Machine learning | Critical care
مقاله انگلیسی
2 Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019
Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to establish methodological best practices for the application of machine learning (ML) to Triage in pediatric ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our work is among the first attempts in this direction. Following very recent works in the literature, we use the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain labels provided by experts. The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission, therefore our dataset is highly class imbalanced. Our reported performance comparison results focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use different well known metrics to evaluate performance of ML models in three different experimental settings: (a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high versus low case severity according to its clinical outcome, and (c) comparison of the number of patients assigned to each standard Triage urgency level against the Triage rule based expert system currently in use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the dychotomic classification experiments. On the implementation side, our study shows that ML predictive models trained according to clinical outcomes, provide better Triage performance than the current rule based expert system in operation at the hospital.
Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems
مقاله انگلیسی
3 Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy
هوش مصنوعی و یادگیری ماشین | برنامه های کاربردی در فیزیوتراپی عضلانی اسکلتی-2019
Introduction: Artificial intelligence (AI) is a field of mathematical engineering which has potential to enhance healthcare through new care delivery strategies, informed decision making and facilitation of patient engagement. Machine learning (ML) is a form of narrow artificial intelligence which can be used to automate decision making and make predictions based upon patient data. Purpose: This review outlines key applications of supervised and unsupervised machine learning in musculoskeletal medicine; such as diagnostic imaging, patient measurement data, and clinical decision support. The current literature base is examined to identify areas where ML performs equal to or more accurately than human levels. Implications: Potential is apparent for intelligent machines to enhance various areas of physiotherapy practice through automization of tasks which involve data analysis, classification and prediction. Changes to service provision through applications of ML, should encourage physiotherapists to increase their awareness of and experiences with emerging technologies. Data literacy should be a component of professional development plans to assist physiotherapists in the application of ML and the preparation of information technology systems to use these techniques.
Keywords: Artificial intelligence | Machine learning | Low back pain | Physiotherapy
مقاله انگلیسی
4 Decision Support in Transfusion Medicine and Blood Banking
پشتیبانی تصمیم گیری در پزشکی انتقال خون و بانکداری خون-2019
Clinical decision support (CDS) tools applied with good design can greatly enhance patient blood management through optimizing ordering and providing concurrent tailored patient information.  Prediction and modeling will have increasingly important roles in managing blood inventory and coordination at donor centers and transfusion services to prevent wastage and supply shortfalls.  Decision support and prediction have powerful potential applications for side-effect detection and management in both donors and recipients related to transfusion.  With improved standards for health care data sharing, such as Fast Healthcare Interoperability Resources and increased adoption, there is a trend toward centralization of CDS content and tools
KEYWORDS : Decision support | Transfusion | Prediction | Modeling | Patient blood management
مقاله انگلیسی
5 A machine learning approach for predicting urine output after fluid administration
یک روش یادگیری ماشین برای پیش بینی خروجی ادرار پس از تجویز مایعات-2019
Background and objective: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation. Methods: We identified sepsis patients in the Multiparameter Intelligent Monitoring in Intensive Care-III v1.4 database according to the Sepsis-3 criteria. We focused on two outcomes: whether the UO decreased after fluid administration and whether oliguria (defined as UO less than the threshold of 0.5 mL/kg/h) de- veloped. A gradient tree-based machine learning model implemented with an eXtreme Gradient Boosting algorithm was used to integrate relevant physiological parameters for predicting the aforementioned out- comes. A confusion matrix was computed. Results: A total of 232,929 events in 19,275 patients were included. Using decreased UO as the outcome measure, the optimal model achieved an area under the curve (AUC) of 0.86; for predicting oliguria, most models achieved an AUC greater than 0.86, and the highest sensitivity was 92.2% when the model was applied to patients with baseline oliguria. Conclusions: Machine learning could help clinicians evaluate fluid status in sepsis patients after fluid administration, thus preventing fluid overload-related complications.
Keywords: Sepsis | Prediction | Machine learning | Electronic health records | Clinical decision support | Fluid resuscitation
مقاله انگلیسی
6 MedGA: A novel evolutionary method for image enhancement in medical imaging systems
MedGA: یک روش جدید تکاملی برای تقویت تصویر در سیستم های تصویربرداری پزشکی-2019
Medical imaging systems often require the application of image enhancement techniques to help physi- cians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underly- ing sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image pro- cessing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various im- age enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solu- tion for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements.
Keywords: Medical imaging systems | Image enhancement | Genetic Algorithms | Magnetic resonance imaging | Bimodal image histogram | Uterine fibroids
مقاله انگلیسی
7 A neuro-heuristic approach for recognition of lung diseases from X-ray images
یک روش عصبی و اکتشافی برای شناخت بیماری های ریه از تصاویر اشعه ایکس-2019
Background and objective: The X-ray screening is one of the most popular methodologies in detection of respiratory system diseases. Chest organs are screened on the film or digital file which go to the doctor for evaluation. However, the analysis of x-ray images requires much experience and time. Clinical decision support is very important for medical examinations. The use of Computational Intelligence can simulate the evaluation and decision processes of a medical expert. We propose a method to provide a decision support for the doctor in order to help to consult each case faster and more precisely. Methods: We use image descriptors based on the spatial distribution of Hue, Saturation and Brightness values in x-ray images, and a neural network co-working with heuristic algorithms (Moth-Flame, Ant Lion) to detect degenerated lung tissues in x-ray image. The neural network evaluates the image and if the possibility of a respiratory disease is detected, the heuristic method identifies the degenerated tissues in the x-ray image in detail based on the use of the proposed fitness function. Results: The average accuracy is 79.06% in pre-detection stage, similarly the sensitivity and the specificity averaged for three pre-classified diseases are 84.22% and 66.7%, respectively. The misclassification errors are 3.23% for false positives and 3.76% for false negatives. Conclusions: The proposed neuro-heuristic approach addresses small changes in the structure of lung tissues, which appear in pneumonia, sarcoidosis or cancer and some consequences that may appear after the treatment. The results show high potential of the newly proposed method. Additionally, the method is flexible and has low computational burden.
Keywords: Medical image processing | Clinical decision support | Neural networks | Heuristic methods
مقاله انگلیسی
8 Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019
Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to establish methodological best practices for the application of machine learning (ML) to Triage in pediatric ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our work is among the first attempts in this direction. Following very recent works in the literature, we use the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain labels provided by experts. The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission, therefore our dataset is highly class imbalanced. Our reported performance comparison results focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use different well known metrics to evaluate performance of ML models in three different experimental settings: (a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high versus low case severity according to its clinical outcome, and (c) comparison of the number of patients assigned to each standard Triage urgency level against the Triage rule based expert system currently in use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the dychotomic classification experiments. On the implementation side, our study shows that ML predictive models trained according to clinical outcomes, provide better Triage performance than the current rule based expert system in operation at the hospital.
Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems
مقاله انگلیسی
9 ProFUSO: Business process and ontology-based framework to develop ubiquitous computing support systems for chronic patients’ management
ProFUSO: فرآیند تجاری و چارچوب مبتنی بر هستی شناسی برای توسعه سیستم های پشتیبانی محاسباتی در همه جا برای مدیریت بیماران مزمن-2018
New advances in telemedicine, ubiquitous computing, and artificial intelligence have supported the emergence of more advanced applications and support systems for chronic patients. This trend addresses the important problem of chronic illnesses, highlighted by multiple international organizations as a core issue in future healthcare. Despite the myriad of exciting new developments, each application and system is designed and implemented for specific purposes and lacks the flexibility to support different healthcare concerns. Some of the known problems of such developments are the integration issues between applications and existing healthcare systems, the reusability of technical knowledge in the creation of new and more sophisticated systems and the usage of data gathered from multiple sources in the generation of new knowledge. This paper proposes a fra mework for the development of chronic disease support systems and applications as an answer to these short comings. Through this framework our pursuit is to create a common ground methodology upon which new developments can be created and easily integrated to provide better support to chronic patients, medical staff and other relevant participants. General requirements are inferred for any support system from the primary attention process of chronic patients by the Business Process Management Notation. Numerous technical ap proaches are proposed to design a general architecture that considers the medical organizational requirements in the treatment of a patient. A framework is presented for any application in support of chronic patients and evaluated by a case study to test the applicability and pertinence of the solution.
Keywords: Data architecture ، Chronic disease management ، Ubiquitous health (u-health) services ، Clinical decision support systems (CDSS ) ، Ubiquitous computing ، Interoperability
مقاله انگلیسی
10 A new standardized data collection system for interdisciplinary thyroid cancer management: Thyroid COBRA
یک سیستم جمع آوری داده استاندارد برای مدیریت سرطان تیروئید: تیروئید COBRA-2018
The big data approach offers a powerful alternative to Evidence-based medicine. This approach could guide cancer management thanks to machine learning application to large-scale data. Aim of the Thyroid CoBRA (Consortium for Brachytherapy Data Analysis) project is to develop a standardized web data collection system, focused on thyroid cancer. The Metabolic Radiotherapy Working Group of Italian Association of Radiation Oncology (AIRO) endorsed the implementation of a consortium directed to thyroid cancer management and data collection. The agreement conditions, the ontology of the collected data and the related software services were defined by a multicentre ad hoc working-group (WG). Six Italian cancer centres were firstly started the project, defined and signed the Thyroid COBRA consortium agreement. Three data set tiers were identified: Registry, Procedures and Research. The COBRA-Storage System (C-SS) appeared to be not time-consuming and to be privacy respecting, as data can be extracted directly from the single centres storage platforms through a secured connection that ensures reliable encryption of sensible data. Automatic data archiving could be directly performed from Image Hospital Storage System or the Radiotherapy Treatment Planning Systems. The C-SS architecture will allow “Cloud storage way” or “distributed learning” approaches for predictive model definition and further clinical decision support tools development. The development of the Thyroid COBRA data Storage System C-SS through a multicentre consortium ap proach appeared to be a feasible tool in the setup of complex and privacy saving data sharing system oriented to the management of thyroid cancer and in the near future every cancer type.
Keywords: Big data ، Data pooling ، Personalized medicine ، Radiotherapy ، Thyroid ، Cancer management
مقاله انگلیسی
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