Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data.

The complications of thoracic aortic disease include aortic dissection and aneurysm. The risks are frequently compounded by many cardiovascular comorbidities, which makes the process of clinical decision making complicated. The purpose of this study is to develop risk predictive models for patients after thoracic aneurysm surgeries, using integrated data from different medical institutions. Seven risk features were formulated for prediction. The CatBoost classifier performed best and provided an ROC AUC of 0.94-0.98 and an F-score of 0.95-0.98. The obtained results are widely in line with the current literature. The obtained findings provide additional support for clinical decision making, guiding a patient care team prior to surgical treatment, and promoting a safe postoperative period.

Predicting the Aortic Aneurysm Postoperative Risks Based on Russian Integrated Data.

This article describes the results of feature extraction from unstructured medical records and prediction of postoperative complications for patients with thoracic aortic aneurysm operations using machine learning algorithms. The datasets from two different medical centers were integrated. Seventy-two features were extracted from Russian unstructured medical records. We formulated 8 target features: Mortality, Temporary neurological deficit (TND), Permanent neurological deficit (PND), Prolonged (> 7 days) lung ventilation (LV), Renal replacement therapy (RRT), Bleeding, Myocardial infarction (MI), Multiple organ failure (MOF). XGBoost showed the best performance for most target variables (F-measure 0.74-0.95) which is comparable to recent results in cardiovascular postoperative risks prediction.

The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource.

BACKGROUND: The larger part of essential medical knowledge is stored as free text which is complicated to process. Standardization of medical narratives is an important task for data exchange, integration, and semantic interoperability. OBJECTIVES: The article aims to develop the end-to-end pipeline for structuring Russian free-text allergy anamnesis using international standards. METHODS: The pipeline for free-text data standardization is based on FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) to ensure semantic interoperability. The pipeline solves common tasks such as data preprocessing, classification, categorization, entities extraction, and semantic codes assignment. Machine learning methods, rule-based, and dictionary-based approaches were used to compose the pipeline. The pipeline was evaluated on 166 randomly chosen medical records. RESULTS: AllergyIntolerance resource was used to represent allergy anamnesis. The module for data preprocessing included the dictionary with over 90,000 words, including specific medication terms, and more than 20 regular expressions for errors correction, classification, and categorization modules resulted in four dictionaries with allergy terms (total 2,675 terms), which were mapped to SNOMED CT concepts. F-scores for different steps are: 0.945 for filtering, 0.90 to 0.96 for allergy categorization, 0.90 and 0.93 for allergens reactions extraction, respectively. The allergy terminology coverage is more than 95%. CONCLUSION: The proposed pipeline is a step to ensure semantic interoperability of Russian free-text medical records and could be effective in standardization systems for further data exchange and integration.

Category of Allergy Identification from Free-Text Medical Records for Data Interoperability.

The use of different data formats complicates the standardization and exchange of valuable medical data. Moreover, a big part of medical data is stored as unstructured medical records that are complicated to process. In this work we solve the task of unstructured allergy anamnesis categorization according to categories provided by FHIR. We applied two stage classification model with manually labeled records. On the first stage the model filters records with information about allergies and on the second stage it categorizes each record. The model showed high performance. The development of this approach will ensure secondary use of data and interoperability.

Evaluating Manual Mappings of Russian Proprietary Formats and Terminologies to FHIR.

BACKGROUND: Evaluating potential data losses from mapping proprietary medical data formats to standards is essential for decision making. The article implements a method to evaluate the preliminary content overlap of proprietary medical formats, including national terminologies and Fast Healthcare Interoperability Resources (FHIR)-international medical standard. METHODS: Three types of mappings were evaluated in the article: proprietary format matched to FHIR, national terminologies matched to the FHIR mappings, and concepts from national terminologies matched to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). We matched attributes of the formats with FHIR definitions and calculated content overlap. RESULTS: The article reports the results of a manual mapping between a proprietary medical format and the FHIR standard. The following results were obtained: 81% of content overlap for the proprietary format to FHIR mapping, 88% of content overlap for the national terminologies to FHIR mapping, and 98.6% of concepts matching can be reached from national terminologies to SNOMED CT mapping. Twenty tables from the proprietary format and 20 dictionaries were matched with FHIR resources; nine dictionaries were matched with SNOMED CT concepts. CONCLUSION: Mapping medical formats is a challenge. The obtained overlaps are promising in comparison with the investigated results. The study showed that standardization of data exchange between proprietary formats and FHIR is possible in Russia, and national terminologies can be used in FHIR-based information systems.