Stroke 51, 35413551 (2020). Ulyana et al.48 trained a deep, fully connected network as a regressor in a 5-year longitudinal study on AD to predict cognitive test scores at multiple future time points. Signal Process. Obermeyer, Z. In contrast to early fusion, the loss from the final model is propagated back to the feature extraction model during training so that the learned feature representations are improved through iterative updating of the feature weights. If your provider has already granted you access, follow these steps to set up your account. Deep multimodal learning from MRI and clinical data for early prediction of neurodevelopmental deficits in very preterm infants. These studies employed different types of DL architectures to learn and fuse the imaging and EHR data for diagnosis purposes. Neurosci. Parvathy et al.13 reported diagnosing AD by fusing sMRI and PET imaging features with mini-mental state examination (MMSE) score, clinical dementia rating (CDR), and age of the subjects. Two authors (F.M. Brugnara, G. et al. N. Am. Ebdrup et al.36 proposed integrating MRI and diffusion tensor imaging tractography (DTI) imaging with neurocognitive tests and clinical data for schizophrenia classification. CAS A fusion framework to extract typical treatment patterns from They trained SVM and deep NNs using the fused features for classification in50,51, respectively. Identifying patients at risk for aortic stenosis through learning from multimodal data. & Lungren, M. P. Fusion of medical imaging and electronic health records using deep learning: A systematic review and implementation guidelines. Fusion Web Clinic offers premium only subscriptions starting from USD29.0/month. As this is a fast-growing field and new AI models with multimodal data are constantly being developed, there might exist studies that fall outside our definition of fusion strategies or use a combination of these strategies. Recent lab orders. Therefore, combining multisource data, e.g. Article For this model, they used Bayesian CNN encoder-decoder to extract imaging features and a Bayesian MLP encoder-decoder to process the medical indicators data. Creating value in health care through big data: Opportunities and policy implications. A review on machine learning principles for multi-view biological data integration. The authors declare no competing interests. Comput. These clinical outcomes were implemented using multimodal ML models. A. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. In the feature extractor, they used VGG-19 architecture to extract MRI features and fully connected NN for clinical data. Late fusion: It trains separate ML models on data of each modality, and the final decision leverages the predictions of each model26. In Annual Conference on Medical Image Understanding and Analysis, 267279 (Springer, 2020). A total of 1158 studies were retrieved from the initial search. & Emanuel, E. J. Transl. Zubair Shah. Finally, 34 studies met our inclusion criteria and were selected for data extraction and synthesis. A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. J. Med. Fusion 64, 149187. After that, they concatenated the extracted features of the two modalities and fed them into fully connected NN for prediction. Radiol. What is a Medical Chart? Records and History | Practice Fusion Vis. PubMed Fusion of medical imaging and electronic health records with attention Asian J. Psychiatr. Late fusion was the least common fusion approach used in the included studies, as only two studies used it. Fusion 76, 355375. Dis. In this review, we labeled each study according to its clinical outcome. 18, 270277 (2004). Practice Fusion - EHR Login Knowledge. ),547558 (Springer International Publishing, Cham, 2021). Our objective is to identify "right patient", "right drug", "right dose", "right route", and "right time" from doctor order information. applied a ResNet architecture and MLP for imaging and clinical data feature extraction. et al. From Pediatrics to Geriatrics, Fusion EMR does it all for PTs, OTs & SLPs Practices! 23, 101859 (2019). Deep learning for predicting COVID-19 malignant progression. 144, 105253. https://doi.org/10.1016/j.compbiomed.2022.105253 (2022). Features include billing, scheduling, waitlists, insights, teletherapy, documentation, and more to streamline your clinic! This section summarizes the different clinical tasks of the retrieved studies, the fusion strategy used, and the ML models that were developed for each task. Azam, M. A. et al. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. 72, 102096 (2021). Chen et al.49 used DL for multimodal feature extraction and classification to detect AD; the authors used the VGG-16 model to extract features from MRI images and a bidirectional LSTM network with an attention layer to learn features from MRI reports. Furthermore, it only requires one model to be trained, making the pipeline for training easier than that of joint and late fusion. and H.A. Syst. Compare Practice Fusion vs eClinicalWorks 2023 | Capterra View all plans and pricing for Fusion Web Clinic in 2023. M-US-EMRs: A Multi-modal Data Fusion Method of Ultrasonic - Springer PubMed Parisa et al.50,51 integrated features extracted from MRI and PET images with neuropsychological tests and demographic data (gender, age, and education) to diagnose MCI early. The National Alzheimers Coordinating Center (NACC) database: An Alzheimer disease database. Scientific Reports (Sci Rep) Practice Fusion is the #1 cloud-based electronic health record (EHR) platform for doctors and patients in the U.S. Subsequently, full-text of the selected studies from the title and abstract screening were assessed for eligibility using our inclusion and exclusion criteria. In their study, they fed the fused features into SVM for classification. The Lancet 390, 21832193. In their study, the feature extraction part applied a ResNet architecture and MLP for CT and clinical data, respectively. Moreover, we focused on the type of imaging and EHR data used by the studies, the source of data, and its availability. Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Johnson, A. E. et al. Table 1 provides a detailed comparison of our review with existing reviews. In terms of imaging modality, CT, MRI, fMRI, structural MRI (sMRI), PET, Diffusion MRI, DTI, ultrasound, X-ray, fundus images, and PET were used in the studies. Then, we present the data fusion strategies that we use to investigate the studies from the perspective of multimodal fusion. Thousands o Who uses Fusion? Sci. Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning. Practice Fusion offers just that, a place doctors can store their patients' medical records for free. As a result, the late fusion outperformed both the early and joint fusion models and the single modality models. There are two types of joint fusion: type I and type II. We consider such data as a single modality, i.e., the EHR modality or imaging modality. Then, they fused the features of the two modalities and fed them to different types of ML classifiers, including SVM, RF, linear regression (LR), decision tree (DT), and Nave Bayes (NB) for classification. Fusion of medical imaging and electronic health records using deep Google Scholar. In accordance with the guidelines for scoping reviews30,31, we did not perform quality assessments of the included studies. The Best Electronic Medical Record (EMR) Managers | PCMag World J. In this scoping review, we focused on applying AI models to multimodal medical data-based applications. Fusion strategies associated with clinical outcomes for different diseases. Despite a lot of automatic methods have been proposed for either image or text analysis in computer vision or natural language research areas, much fewer studies have been developed for the fusion of medical image and EHR data for medical problems. Goodfellow, I., Bengio, Y. Qiu et al.37 trained three independent imaging models that took a single MRI slice as input, then aggregated the prediction of these models using maximum, mean, and majority voting. Yidong et al.19 used a Bayesian CNN encoder-decoder to extract imaging features and a Bayesian Multilayer perception (MLP) encoder-decoder to process the medical indicators data. Open Access funding provided by Qatar National Library. 3, 19 (2020). The study also created early, joint fusion models and two single modality models to compare with late fusion performance. Artificial intelligence-based methods for fusion of electronic health records and imaging data. How do I request or sign up for a Patient Fusion account to gain access Because it's Web-based, Practice Fusion has a cost edge on on-premises medical-record solutions. Their model produced MMSE scores for ten unique future time points at six-month intervals by combing biomarkers from cognitive test scores, PET, and MRI. https://doi.org/10.1016/j.inffus.2020.07.006 (2020). It is an industry-leading cloud-based EHR (electronic health record) system capable of doing multiple tasks such as booking patient appointments, medical charting, and providing e-prescriptions. Brief. Specifically, most of the studies that focused on detecting neurological diseases were for AD (\(n=4\))13,14,15,49, and MCI (\(n = 4\))37,42,50,51. Moreover, 10 studies were removed after the full-text screening. The fourth model used an NN classifier as an aggregator, which took as input the single modality models prediction. IEEE J. Biomed. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Article Marinescu, R.V. etal. They have helped healthcare providers share medical notes and other chart data securely and quickly with all those involved in a patients care. Abstract: This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of electrocardiogram (ECG) and patient electronic medical record. Your Patient Fusion account contains important health information including: Medication; Diagnosis; Immunization; Allergies; Appointments (past and future) Office for Civil Rights. Doctors monitor patients remotely via smartphones and fitness - PBS We categorized the diseases and disorders in the included studies into seven types: neurological disorders, cancer, cardiovascular diseases, Covid-19, psychiatric disorders, eye diseases, and other diseases. https://doi.org/10.1038/s41598-022-22514-4, DOI: https://doi.org/10.1038/s41598-022-22514-4. 3, 19 (2020). Finally, Bai et al.52 compared different multimodal biomarkers (clinical data, biochemical and hemologic parameters, and ultrasound elastography parameters) for predicting the assessment of fibrosis in chronic hepatitis B using SVM. Hyun, S. H., Ahn, M. S., Koh, Y. W. & Lee, S. J. Figure 1 shows a flowchart of the study screening and selection process. They concatenated the features of all modalities and fed them to an RF model. In the meantime, to ensure continued support, we are displaying the site without styles 2023 Practice Fusion, Inc. | Site Map | Terms|Privacy Policy Many studies found that missing pertinent clinical and laboratory data during image interpretation decreases the radiologists ability to accurately make diagnostic decisions6. Tabular data were mainly processed using dense layers when fed into a model, while text data were mostly processed using LSTM layers followed by the attention layer. College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar, Farida Mohsen,Hazrat Ali,Nady El Hajj&Zubair Shah, College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar, You can also search for this author in Also, we excluded studies that used different types of data from the same modality, such as studies that only combined two or more imaging types (e.g. Accessed on September 15, 2021. Z.S. They used a ResNet50 architecture to extract features from the X-ray images and fully connected NN to process the ICU data. Our smart, flexible content makes documentation fast and convenient. The former55 proposed a multimodal network for cardiomegaly classification, which simultaneously integrates the non-imaging intensive care unit (ICU) data (laboratory values, vital sign values, and static patient metadata, including demographics) and the imaging data (chest X-ray). Tricco, A. C. et al. In these studies, EHRs were combined with medical imaging to diagnose a spectrum of diseases including neurological disorders (\(n = 9\))4,13,14,15,32,37,42,49,50, psychiatric disorders (\(n = 2\))33,36, CVD (\(n =3\))54,55,56, Cancer (\(n = 2\))16,55, and four studies for other different diseases18,19,40,53. supervised the study. As a result of this approach, they require an electronic medical records software that is not utilized by companies such as Patient Fusion. Then, they jointly learned the non-linear correlations among all modalities using fully connected NN. Then, they concatenated the different features and fed them into an LSTM network followed by a fully connected NN for prediction. A typology of reviews: An analysis of 14 review types and associated methodologies. Joint fusion: It combines the learned features from intermediate layers of NN with features from other modalities as inputs to a final model during training26. In addition, we recorded implementation details of the model, such as feature extraction and single modality evaluation. You are using a browser version with limited support for CSS. Hsu et al.17 concatenated the imaging features extracted using Inception-V3 model with the clinical data features before feeding them to fully connected NN. We used Rayyan web-based review management tool29 for the first screening and study selection. Ten studies of the prediction tasks were disease prediction12,17,34,38,39,41,44,46,48,52, which involved determining whether an individual might develop a given disease in the future. Although we focus on EHR and medical imaging as multimodal data, other modalities such as multi-omics and environmental data could also be integrated using the aforementioned fusion approaches. We have defined and summarized key terminology, techniques, and evaluated the state of the art for multimodal fusion in medical imaging, honing in on key insights and unexplored questions to guide task and modality-specific strategies. 342350 (Springer International Publishing, Cham, 2021). The former is when NNs are used to extract features from all modalities. Non-English publications were also excluded. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Joint fusion was the second most common fusion strategy used in 10 out of the 34 studies. Control 68, 102729 (2021). Three models took the average of the predicted probabilities from the imaging and EHR modality models as the final prediction. Fourteen early fusion studies evaluated their fusion models performance against that of single modality models12,13,15,16,18,25,32,33,34,36,41,42,43,44,51 . It also allows patients or healthcare proxies to ensure the accuracy of all information in their medical records and to identify any inaccuracies that require correction. In late fusion, multiple models are employed, each specialized in a single modality, thereby limiting the size of the input feature vector for each model. Their model integrated features extracted from UGI endoscopic images with corresponding textual medical data. The Practice Fusion electronic health record (EHR) system enables you to easily capture all the following information in your patients electronic medical charts, including whats often called PAMI, referring to Problems, Allergies, Medications, and Immunizations: As discussed above, patient charts include office notes for every patient visit or encounter, which contain specific information based on the encounter type, including initial consultations, second opinions, follow-up visits, procedure visits, or encounters during which diagnostic testing takes place. ISSN 2045-2322 (online). In another study32, Xin et al. of updated, real-time information - Not designed to be shared outside the individual practice EHR - Electronic Health Records | Practice Fusion