However, patient data consists of diverse types of data. By exploiting such data, multimodal approaches promise to revolutionize our ability to provide personalized care. Attempts to combine two modalities in a single diagnostic task have utilized the evolving field of multimodal representation learning ...
visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information....
We forwarded each CT slice and its accompanying textual clinical information to MDT to obtain one holistic representation. Since we had multiple CT slices, we obtained a number of holistic representations (equal to the number of CT slices) for the same patient. Then, we performed average pooling...
computational modeling and information fusion that outcomes of interest such as drug and treatment targets ultimately facilitate better decision making at the patient level in those care centers. This phenomenon has sparked an interest in fusion studies using health care data. Fig. 1: Multimodal precis...
computational modeling and information fusion that outcomes of interest such as drug and treatment targets ultimately facilitate better decision making at the patient level in those care centers. This phenomenon has sparked an interest in fusion studies using health care data....
The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bot...
Effective learning strategies include group discussions, listening to podcasts or recordings of meetings, Q&A sessions, and holding debates. Auditory learners may have to talk through their ideas before reaching a conclusion, so it’s important to be patient and allow them to process the new infor...
Understanding the health condition of the patient by observing the clinical measurements, laboratory test results and predicting the condition of patients during their ICU stay is a vital problem. In this paper, we focus on two different common risk prediction tasks, mortality (in-hospital & in-IC...
Liu, X., et al.: Self-supervised learning: generative or contrastive. IEEE Trans. Knowl. Data Eng. (2021) Google Scholar Maier, J., Eulig, E., Dorn, S., Sawall, S., Kachelrieß, M.: Real-time patient-specific CT dose estimation using a deep convolutional neural network. In: ...
(Supplementary Fig.A3in supplementary material) to obtain a high level representation of the patient data. Each auto-encoder layer takes an input\(x\)of dimension\(n \times d\), where\(n\)is the number of training samples and\(d\)is input dimensionality (\(d = m\)for first layer)....