🦉 Snow Owl Terminology Server - a production-ready, scalable, FHIR Terminology Service compliant server that supports SNOMED CT International and Extensions, LOINC, RxNorm, UMLS, ICD-10, custom code systems and many others java elasticsearch owl ontology healthcare knowledge-graph fhir snomed ...
quality of VTE code reporting [13], with important implications for academic research, VTE surveillance, health insurance reimbursement, resource allocation, and the development of tools (such as clinical risk scores or prediction models) based on VTE ICD-10 codes [[14], [15], [16], [17]]...
with the ICD-10 full-code corresponding to the underlying cause of death, the ICD-10 block for the underlying cause of death, and ICD-10 codes corresponding to auxiliary conditions or injuries present in the deceased, other than those from the underlying cause of death in our dataset. ...
This framework is applied to the extensive and publicly available MIMIC-III dataset, enabling us to leverage both numerical and text-based data for improved ICD-9 code prediction. Our system uses text representation models to understand the text-based medical records; the Gated Recurrent Unit (...
[2]collected in the patient recovery process from 58,576 adults in an intensive care unit. In this dataset, there are both numerical test results as well as unstructured written documents. While most of the work in the literature for ICD-9 code prediction is performed on unstructured text ...
Clinical data acquired from this version is recorded with the both ICD-9 and ICD-10 ontologies. Thus, in this version of the dataset, the label scale becomes larger, the parameters and memory usages of the model, the training times, and the prediction performance are all issues that need ...
The AUROC of GatorTron was significantly higher than that of TF-IDF (Delong’s test, P-value < 2.2 × 10-16). Table 5. Highest hazard ratios of ICD2Vec risk score for eight diseases. DatasetTarget diseaseBaseline (risk score derivation)Top 10% of IRIS, [no. of samples, (no. of ...
The W2V embeddings are trained based on the sentences from Fuwai dataset and MIMIC- III. 10 We simplified ‘the most frequent top-N codes’ as ‘the top-N codes’ below. 11 We chose the values of N according to the standard that the least frequent qualified code involves at least (or...
code prediction. Since our medical record data still follows ICD-9, in this study we focus on the ICD-9 code prediction. In addition, our proposed method is generic to ICD-9 and ICD-10. The ICD-9 codes contain various levels such as chapter, block, three-digit code, and full code. ...
The network integrates positive- and negative-click information in the distance layer of the change-detection network, and users can correct the prediction defects by adding clicks. We carried out experiments on the open source dataset WHU and LEVIR-CD. By adding clicks, their F1-scores can ...