Implementation of a clinical decision support system for interpretation of laboratory tests for patients

The paper presents the results of the development and implementation of an expert system that automatically generates doctors' letters based on the results of laboratory tests. Medical knowledge is expressed using a first order predicate based language. The implementation of the system allowed increasing the number of patients who refer to a doctor after laboratory tests by 14%. A qualitative study with 100 patients demonstrated a high acceptance of the system. The majority (82%) of the patients reported that they trust the system and follow its advice to visit a doctor if necessary

Crowdsourcing for Reminiscence Chatbot Design

In this work-in-progress paper we discuss the challenges in identifying effective and scalable crowd-based strategies for designing content, conversation logic, and meaningful metrics for a reminiscence chatbot targeted at older adults. We formalize the problem and outline the main research questions that drive the research agenda in chatbot design for reminiscence and for relational agents for older adults in general

Automatic allergy classification based on Russian unstructured medical texts

Most of the medical data in hospital information systems databases are stored in an unstructured form. Techniques for processing unstructured records are widely presented in scientific papers focused on English data. This paper proposes a method for intellectual analysis of unstructured allergy anamnesis in Russian in order to identify the presence and type of allergy and intolerance of a patient. The method is based on machine learning algorithms and uses international standards for the exchange of medical data and terminology standards, such as FHIR and SNOMED CT. As a result of the experiment, about 12 thousand medical records were processed. F-measure for the developed classification models ranged from 0.93 to 0.96. The models showed high values of metrics for evaluating the effectiveness of the models. In the future, structured data can be used in models for predicting medical risks. Further development of methods for structuring medical texts will ensure the interoperability of medical data

Experimental study of stress-strain state of adhesive joints steel/carbon fiber under tension with a bend by digital image correlation

This paper presents a study of characteristics of an evolution of deformation fields in surface layers of medium-carbon low-alloy specimens under compression. The experiments were performed on the "Universal Testing Machine 4500" using a digital stereoscopic image processing system Vic-3D. A transition between stages is reflected as deformation redistribution on the near-surface layers

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC((R)))

OBJECTIVE: Our study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems. MATERIALS AND METHODS: We used an innovative approach based on a decomposition and re-composition of LOINC concepts according to the transversal relations that may be described between LOINC concepts and their definitional attributes. TLAB entities were first anchored to LOINC attributes and then aligned to LOINC concepts through the appropriate combination of definitional attributes. Finally, using laboratory results of the Bordeaux data-warehouse, an instance-based filtering process has been applied. RESULTS: We found a small overlap between the tokens constituting the labels of TLAB and LOINC. However, the TLAB entities have been easily aligned to LOINC attributes. Thus, 99.8% of TLAB entities have been related to a LOINC analyte and 61.0% to a LOINC system. A total of 55.4% of used TLAB entities in the hospital data-warehouse have been mapped to LOINC concepts. We performed a manual evaluation of all 1-1 mappings between TLAB entities and LOINC concepts and obtained a precision of 0.59. CONCLUSION: We aligned TLAB and LOINC with reasonable performances, given the poor quality of TLAB labels. In terms of interoperability, the alignment of interface terminologies with LOINC could be improved through a more formal LOINC structure. This would allow queries on LOINC attributes rather than on LOINC concepts only

Modelling of COVID-19 morbidity in russia

The outbreak of COVID-19 has led to a crucial change in ordinary healthcare approaches. In comparison with emergencies re-allocation of resources for a long period of time is required and the peak utilization of the resources is also hard to predict. Furthermore, the epidemic models do not provide reliable information of the development of the pandemic's development, so it creates a high load on the healthcare systems with unforeseen duration. To predict morbidity of the novel COVID-19, we used records covering the time period from 01-03-2020 to 25-05-2020 and include sophisticated information of the morbidity in Russia. Total of 45238 patients were analyzed. The predictive model was developed as a combination of Holt and Holt-Winter models with Gradient boosting Regression. As we can see from the table 2, the models demonstrated a very good performance on the test data set. The forecast is quite reliable, however, due to the many uncertainties, only a real-world data can prove the correctness of the forecast

Applying open source data visualization tools to standard based medical data

The urgency of the paper deals with the necessity of using flexible and simple software tools for standard-based medical data visualization. The aim of the study: to implement ISO 13606 standard-based medical data visualization using open source tools. The methods: ISO 13606 is an archetype clinical standard. A canonical approach and Open source JavaScript libraries are used for data transformation. MS Visual Studio is the development environment. The results: For working with medical set of archetypes the XML informational model was developed. Using open source JavaScript libraries the insulin and blood sugar and dynamic blood sugar diagrams were constructed. Conclusion: Applying the open source tools (JavaScript libraries) a developer gains a variety of prepared solutions which realize flexible and simple methods for standard-based medical data representation

Patient facing decision support system for interpretation of laboratory test results

Abstract Background In some healthcare systems, it is common that patients address laboratory test centers directly without a physician’s recommendation. This practice is widely spread in Russia with about 28% of patients who visiting laboratory test centers for diagnostics. This causes an issue when patients get no help from the physician in understanding the results. Computer decision support systems proved to efficiently solve a resource consuming task of interpretation of the test results. So, a decision support system can be implemented to rise motivation and empower the patients who visit a laboratory service without a doctor’s referral. Methods We have developed a clinical decision support system for patients that solves a classification task and finds a set of diagnoses for the provided laboratory tests results. The Wilson and Lankton’s assessment model was applied to measure patients’ acceptance of the solution. Results A first order predicates-based decision support system has been implemented to analyze laboratory test results and deliver reports in natural language to patients. The evaluation of the system showed a high acceptance of the decision support system and of the reports that it generates. Conclusions Detailed notification of the laboratory service patients with elements of the decision support is significant for the laboratory data management, and for patients’ empowerment and safety