Time Series Forecasting of Cardiovascular Mortality: Machine Learning Based on State Economic and Local Medical Data.

This study focuses on the complex interplay of healthcare, economic factors, and population dynamics, addressing a research gap in regional-level models that integrate diverse features within a temporal framework. Our primary objective is to develop an advanced temporal model for predicting cardiovascular mortality in Russian regions by integrating global and local healthcare features with economic and population dynamics. Utilizing a dataset from the Almazov Center's Department of Mortality Performance Monitoring, covering 94 regions and 752 records from January 1, 2015, to December 31, 2023, our analysis incorporates key parameters such as angioplasty procedures, population morbidity rates, Ischemic Heart Disease (IHD) and Cardiovascular Diseases (CVD) monitoring, and demographic data. Employing XGBoost and a regression model, our methodology ensures the model's robustness and generalizability.

Development of a Method for Automatic Matching of Unstructured Medical Data to ICD-10 Codes.

Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to address this challenge. The system automatically matches unstructured medical text (doctor notes, complaints) to ICD-10 codes. It leverages a unique architecture featuring a training layer for model development and a knowledge base that captures relationships between symptoms and diseases. Experiments using data from a large medical research center demonstrated the system's effectiveness in disease classification prediction. Logistic regression emerged as the optimal model due to its superior processing speed, achieving an accuracy of 81.07% with acceptable error rates during high-load testing. This approach offers a promising solution to improve healthcare informatics by overcoming coding standard incompatibility and automating code prediction from unstructured medical text.

Unraveling Endometrial Cancer Survival Predictors Through Advanced Machine Learning Techniques.

This study explores endometrial cancer (EC) within the broader context of oncogynecology, focusing on 3,845 EC patients at the Almazov National Research Center. The research analyzes clinical data, employing machine learning techniques like random forest regression and decision tree analysis. Key findings include age-dependent impacts on EC outcomes, unexpected correlations between dietary habits and recurrence risk (e.g., higher risk for vegans), and intriguing associations like soft drink consumption influencing relapse. Despite limitations like a retrospective design and self-reported data, the study's extended eight-year follow-up and robust database enhance its credibility. The nuanced insights into EC risk factors, influenced by factors like physical activity and diet, open avenues for targeted diagnostics and prevention strategies, showcasing the potential of machine learning in predicting outcomes.

Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods.

Aortic stenosis (AS) is the most commonly diagnosed valvular heart disease, and its prevalence increases with the aging of the general population. However, AS is often diagnosed at a severe stage, necessitating surgical treatment, due to its long asymptomatic period. The objective of this study was to analyze the frequency of AS in a population of cardiovascular patients using echocardiography (ECHO) and to identify clinical factors and features associated with these patient groups. We utilized machine learning methods to analyze 84,851 echocardiograms performed between 2010 and 2018 at the National Medical Research Center named after V.A. Almazov. The primary indications for ECHO were coronary artery disease (CAD) and hypertension (HP), accounting for 33.5% and 14.2% of the cases, respectively. The frequency of AS was found to be 13.26% among the patients (n = 11,252). Within our study, 1544 patients had a bicuspid aortic valve (BAV), while 83,316 patients had a tricuspid aortic valve (TAV). BAV patients were observed to be younger compared to TAV patients. AS was more prevalent in the BAV group (59%) compared to the TAV group (12%), with a p-value of <0.0001. By employing a machine learning algorithm, we randomly identified significant features present in AS patients, including age, hypertension (HP), aortic regurgitation (AR), ascending aortic dilatation (AscAD), and BAV. These findings could serve as additional indications for earlier observation and more frequent ECHO in specific patient groups for the earlier detection of developing AS.

Machine Learning Methods for Pregnancy and Childbirth Risk Management.

Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management.

Study of the User Behaviour Caused by Automatic Recommendation Systems Call to Action.

Diagnostics accuracy and usability of symptom checkers have been researched in several studies. Their ability to set a correct diagnosis especially in the urgent cases is questionable. There is one aspect of symptom checkers that has not been deeply studied yet. It is their ability to motivate patients to follow up after receiving a direct recommendation and to decrease a load on the health care professionals. The goal of this research is to study how patients behave after receiving a recommendation from a symptom checker and motivation of this behavior. We studied how patients react on the symptom checker recommendations and the motivation behind this behavior. In total we invited 3615 patients to have a symptom checker screening; 2374 of them agreed to run a symptom checker screening; 867 of them agreed to participate in the study. The proportion of the patients who agreed to have a symptom checker screening. So, we can clearly see that symptom checker screening doesn't result in a significant decrease of the load on healthcare professionals. This is supported by the quantitative study results. The patients emphasized the ease of use of the tool and clearness of the recommendations it gives. However, they perceived it as rather a second opinion tool or a tool that helps to prepare to the doctor's visit.

Application of Machine Learning Methods for Epilepsy Risk Ranking in Patients with Hematopoietic Malignancies Using.

Machine learning methods to predict the risk of epilepsy, including vascular epilepsy, in oncohematological patients are currently considered promising. These methods are used in research to predict pharmacoresistant epilepsy and surgical treatment outcomes in order to determine the epileptogenic zone and functional neural systems in patients with epilepsy, as well as to develop new approaches to classification and perform other tasks. This paper presents the results of applying machine learning to analyzing data and developing diagnostic models of epilepsy in oncohematological and cardiovascular patients. This study contributes to solving the problem of often unjustified diagnosis of primary epilepsy in patients with oncohematological or cardiovascular pathology, prescribing antiseizure drugs to patients with single seizure syndromes without finding a disease associated with these cases. We analyzed the hospital database of the V.A. Almazov Scientific Research Center of the Ministry of Health of Russia. The study included 66,723 treatment episodes of patients with vascular diseases (I10-I15, I61-I69, I20-I25) and 16,383 episodes with malignant neoplasms of lymphoid, hematopoietic, and related tissues (C81-C96 according to ICD-10) for the period from 2010 to 2020. Data analysis and model calculations indicate that the best result was shown by gradient boosting with mean accuracy cross-validation score = 0.96. f1-score = 98, weighted avg precision = 93, recall = 96, f1-score = 94. The highest correlation coefficient for G40 and different clinical conditions was achieved with fibrillation, hypertension, stenosis or occlusion of the precerebral arteries (0.16), cerebral sinus thrombosis (0.089), arterial hypertension (0.17), age (0.03), non-traumatic intracranial hemorrhage (0.07), atrial fibrillation (0.05), delta absolute neutrophil count (0.05), platelet count at discharge (0.04), transfusion volume for stem cell transplantation (0.023). From the clinical point of view, the identified differences in the importance of predictors in a broader patient model are consistent with a practical algorithm for organic brain damage. Atrial fibrillation is one of the leading factors in the development of both ischemic and hemorrhagic strokes. At the same time, brain infarction can be accompanied both by the development of epileptic seizures in the acute period and by unprovoked epileptic seizures and development of epilepsy in the early recovery and in a longer period. In addition, a microembolism of the left heart chambers can lead to multiple microfocal lesions of the brain, which is one of the pathogenetic aspects of epilepsy in elderly patients. The presence of precordial fibrillation requires anticoagulant therapy, the use of which increases the risk of both spontaneous and traumatic intracranial hemorrhage.

Application of Machine Learning Methods to Analyze Occurrence and Clinical Features of Ascending Aortic Dilatation in Patients with and without Bicuspid Aortic Valve.

Aortic aneurysm (AA) rapture is one of the leading causes of death worldwide. Unfortunately, the diagnosis of AA is often verified after the onset of complications, in most cases after aortic rupture. The aim of this study was to evaluate the frequency of ascending aortic aneurysm (AscAA) and aortic dilatation (AD) in patients with cardiovascular diseases undergoing echocardiography, and to identify the main risk factors depending on the morphology of the aortic valve. We processed 84,851 echocardiographic (ECHO) records of 13,050 patients with aortic dilatation (AD) in the Almazov National Medical Research Centre from 2010 to 2018, using machine learning methodologies. Despite a high prevalence of AD, the main reason for the performed ECHO was coronary artery disease (CAD) and hypertension (HP) in 33.5% and 14.2% of the patient groups, respectively. The prevalence of ascending AD (>40 mm) was 15.4% (13,050 patients; 78.3% (10,212 patients) in men and 21.7% (2838 patients) in women). Only 1.6% (n = 212) of the 13,050 patients with AD knew about AD before undergoing ECHO in our center. Among all the patients who underwent ECHO, we identified 1544 (1.8%) with bicuspid aortic valve (BAV) and 635 with BAV had AD (only 4.8% of all AD patients). According to the results of the random forest feature importance analysis, we identified the eight main factors of AD: age, male sex, vmax aortic valve (AV), aortic stenosis (AS), blood pressure, aortic regurgitation (AR), diabetes mellitus, and heart failure (HF). The known factors of AD-like HP, CAD, hyperlipidemia, BAV, and obesity, were also AD risk factors, but were not as important. Our study showed a high frequency of AscAA and dilation. Standard risk factors of AscAA such as HP, hyperlipidemia, or obesity are significantly more common in patients with AD, but the main factors in the formation of AD are age, male sex, vmax AV, blood pressure, AS, AR, HF, and diabetes mellitus. In males with BAV, AD incidence did not differ significantly, but the presence of congenital heart disease was one of the 12 main risk factors for the formation of AD and association with more significant aortic dilatation in AscAA groups.

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.

Study of patients' attitude to automatic interpretation of laboratory test results and its influence on follow-up rate.

BACKGROUND: One of the current major factors of not following up on the abnormal test results is the lack of information about the test results and missing interpretations. Clinical decision support systems (CDSS) can become a solution to this problem. However, little is known how patients react to the automatically generated interpretations of the test results, and how this can affect a decision to follow up. In this research, we study how patients perceive the interpretations of the laboratory tests automatically generated by a clinical decision support system depending on how they receive these recommendations and how this affects the follow-up rate. METHODS: A study of 3200 patients was done querying the regional patient registry. The patients were divided into 4 groups who received: 1. Recommendations automatically generated by a CDSS with a clear indication of their automatic nature. 2. Recommendations received personally from a doctor with a clear indication of their automatic nature. 3. Recommendations from a doctor with no indication of their automated generation. 4. No recommendations, only the test results. A follow-up rate was calculated as the proportion of patients referred to a laboratory service for a follow-up investigation after receiving a recommendation within two weeks after the first test with abnormal test results had been completed and the interpretation was delivered to the patient. The second phase of the study was a research of the patients' motivation. It was performed with a group of 789 patients. RESULTS: All the patients who received interpretations on the abnormal test results demonstrated a significantly higher rate of follow-up (71%) in comparison to the patients who received only test results without interpretations (49%). Patients mention a time factor as a significant benefit of the automatically generated interpretations in comparison to the interpretations they can receive from a doctor. CONCLUSION: The results of the study show that delivering automatically generated interpretations of test results can support patients in making a decision to follow up. They are trusted by patients and raise their motivations and engagement.

Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability.

The paper presents a conceptual framework for building practically applicable clinical decision support systems (CDSSs) using data-driven (DD) predictive modelling. With the proposed framework we have tried to fill the gap between experimental CDSS implementations widely covered in the literature and solutions acceptable by physicians in daily practice. The framework is based on a three-stage approach where DD model definition is accomplished with practical norms referencing (scales, clinical recommendations, etc.) and explanation of the prediction results and recommendations. The approach is aimed at increasing the applicability of CDSSs based on DD models through better integration into decision context and higher explainability. The approach has been implemented in software solutions and tested within a case study in type 2 diabetes mellitus (T2DM) prediction, enabling us to improve known clinical scales (such as FINDRISK) while keeping the problem-specific reasoning interface similar to existing applications. A survey was performed to assess and investigate the acceptance level and provide insights on the influences of the introduced framework's element on physicians' behavior.

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.

Dynamic Aortic Aneurism Risk Factors.

According to different systematic reviews incidence of thoracic aortic aneurysms (TAA) in the general population is increasing in frequency ranging from 5 to 10.4 per 100000 patients. However, only few studies have illustrated the role of different risk factors in the onset and progression of ascending aortic dilatation. Currently, noninvasive imaging techniques are used to assess the progression rate of aortic and aortic valve disease. Transthoracic (TT) Echocardiographic examination routinely includes evaluation of the aorta It is the most available screening method for diagnosis of proximal aortic dilatation. Since the predominant area of dilation is the proximal aorta, TT-echo is often sufficient for screening. We retrospectively analyzed the ECHO database with 78499 echocardiographic records in the Almazov National Medical Research Centre to identify patients with aneurysm. Detailed information including demographic characteristics, ECHO results and comorbidities were extracted from outpatient clinic and from hospital charts related to hospitalizations occurring within a year before index echocardiography was performed. Comorbid diseases were similarly extracted from outpatient clinic and/or hospital admissions. The classifier showed an AUC-ROC for predicting of aneurism detection after a repeated ECHO at 82%.

Lifestyle Cancer Survival Predictors: Influence of Vegetarian Diet on the Relapse of Endometrial Cancer.

Endometrial cancer (EC) is the most common gynecological tumor in high-income countries, and its incidence has increased over time. The most critical risk factor for EC is the long-term unopposed exposure to increased estrogens both exogenous and endogenous. Machine learning can be used as a promising tool to resolve longstanding challenges and support identification of the risk factors and their correlations before the clinical trials and make them more focused. In this paper we present the results of the research of the correlation analysis of Endometrial cancer risk factors. The study was performed with EC patients of the Almazov center in Saint-Petersburg, Russia. All women involved in the current study underwent radical surgical intervention due to EC. After initial cancer treatment, they were referred to the Almazov center outpatient specialists for follow-up visits. Many of them were readmitted of the inpatient clinic due to relapse. We extracted a variety of parameters related to lifestyle, dietary habits, socioeconomic, and reproductive features from the inpatient and outpatient databases of Almazov center. The medical records of the women with enough data were included in the study. Prediction of Progression-free survival (PFS) and overall survival (OS) were analyzed respectively. The AUC of ROC was calculated for PFS = 0.93 and for OS = 0.94.

Regulation Modelling and Analysis Using Machine Learning During the Covid-19 Pandemic in Russia.

Due to the specific circumstances related to the COVID-19 pandemic, many countries have enforced emergency measures such as self-isolation and restriction of movement and assembly, which are also directly affecting the functioning of their respective public health and judicial systems. The goal of this study is to identify the efficiency of the criminal sanctions in Russia that were introduced in the beginning of COVID-19 outbreak using machine learning methods. We have developed a regression model for the fine handed out, using random forest regression and XGBoost regression, and calculated the features importance parameters. We have developed classification models for the remission of the penalty and for setting a sentence using a gradient boosting classifier.

Assessing Acceptance Level of a Hybrid Clinical Decision Support Systems.

We present a user acceptance study of a clinical decision support system (CDSS) for Type 2 Diabetes Mellitus (T2DM) risk prediction. We focus on how a combination of data-driven and rule-based models influence the efficiency and acceptance by doctors. To evaluate the perceived usefulness, we randomly generated CDSS output in three different settings: Data-driven (DD) model output; DD model with a presence of known risk scale (FINDRISK); DD model with presence of risk scale and explanation of DD model. For each case, a physician was asked to answer 3 questions: if a doctor agrees with the result, if a doctor understands it, if the result is useful for the practice. We employed a Lankton's model to evaluate the user acceptance of the clinical decision support system. Our analysis has proved that without the presence of scales, a physician trust CDSS blindly. From the answers, we can conclude that interpretability plays an important role in accepting a CDSS.

Influence of Healthcare Organization Factors on Cardiovascular Diseases Mortality.

One serious pandemic can nullify years of efforts to extend life expectancy and reduce disability. The coronavirus pandemic has been a perturbing factor that has provided an opportunity to assess not only the effectiveness of health systems for cardio-vascular diseases (CVD), but also their sustainability. The goal of our research is to analyze the influence of public health factors on the mortality from circulatory diseases using machine learning methods. We analysed a very large dataset that consisted of the information collected from the national registers in Russia. We included data from 2015 to 2021. It included 340 factors that characterize organization of healthcare in Russia. The resulting area under receiver operating characteristic curve (AUC of ROC) of the Random Forest based regression model was 92% with a testing dataset. The models allow for automated retraining as time passes and epidemiological and other situations change. They also allow additional characteristics of regions and health care organizations to be added to existing training datasets depending on the target. The developed models allow the calculation of the probability of the target for 6-12 months with an error of 8%. Moreover, the models allow to calculate scenarios and the value of the target indicator when other indicators of the region change.

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.

Gynecological Surgery and Machine Learning: Complications and Length of Stay Prediction.

In this study we are developing predictive models for a length of stay after a gynecological surgery, complications and the length of the surgery using machine learning methods. The study was performed with the data of patients with the diseases of the female reproductive system. The patients were admitted to the Almazov National Medical Research Centre (Saint-Petersburg, Russia) within the period 2010-2020. The study included 8170 electronic medical records of inpatient episodes including 3500 operation protocols. The data included anamnesis of life, anamnesis of disease, laboratory tests, severity, outcome of a surgery, main and comorbid diagnosis, complications, case outcome. The dataset was randomly split into 70% train and 30% test datasets. Validation with the test dataset provided the following prediction metrics for the length of stay after a surgery model. Training score: AUC of ROC: 0.9582230976834093; K-fold CV average score: -8.73; MSE: 5.65; RMSE: 2.83.

Prediction of a Due Date Based on the Pregnancy History Data Using Machine Learning.

Prediction of a labor due date is important especially for the pregnancies with high risk of complications where a special treatment is needed. This is especially valid in the countries with multilevel health care institutions like Russia. In Russia medical organizations are distributed into national, regional and municipal levels. Organizations of each level can provide treatment of different types and quality. For example, pregnancies with low risk of complications are routed to the municipal hospitals, moderate risk pregnancies are routed to the reginal and high risk of complications are routed to the hospitals of the national level. In the situation of resource deficiency especially on the national level it is necessary to plan admission date and a treatment team in advance to provide the best possible care. When pregnancy data is not standardized and semantically interoperable, data driven models. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov perinatal medical center in Saint-Petersburg, Russia. The dataset was exported from the medical information system. It consisted of structured and semi structured data with the total of 73115 lines for 12989 female patients. The proposed due date prediction data-driven model allows a high accuracy prediction to allow proper resource planning. The models are based on the real-world evidence and can be applied with limited amount of predictors.