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.

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.

Prediction of Childbirth Mortality Using Machine Learning.

Timely identification of risk factors in the early stages of pregnancy, risk management and mitigation, prevention, adherence management can reduce the number of adverse perinatal outcomes and complications for both mother and a child. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov specialized medical center in Saint-Petersburg, Russia. Correlation analysis was performed using Pearson correlation coefficient to select the most relevant predictors. We used APGAR score as a metrics for the childbirth outcomes. Score of 5 and less was considered as a negative outcome. To analyze the influence of the unstructured anamnesis data on the prediction accuracy we have run two prediction experiments for every classification task: 1. Without unstructured data and 2. With unstructured data. This study presents implementation of predictive models for adverse childbirth events that provides higher precision than state of the art models. This is due to the use of unstructured medical data in addition to the structured dataset that allowed to reach 0.92 precision. Identification of main risk factors using the results of the features importance analysis can support clinicians in early identification of possible complications and planning and execution preventive measures.