International audience

Irritable Bowel Syndrome (IBS) is a prevalent gastrointestinal disorder, particularly prevalent among the Turkish population. Those afflicted with this disease must adhere to a low FODMAP diet in conjunction with the use of pharmaceuticals. As with any dietary regimen, it is often challenging for individuals to adhere to dietary guidelines in their daily lives. In this study, a chatbot health assistant was developed with the objective of supporting individuals in maintaining a low FODMAP diet with ease. The chatbot is supported by a Knowledge Graph (KG). The foods that are suitable or unsuitable for the FODMAP diet, the food groups to which they belong, and the dishes that can be prepared with these foods have been integrated into a KG framework. The chatbot has been constructed on this KG and provides diet guidance services to individuals. The objective of the proposed question-answer system is to enhance user engagement by providing real-time assistance and personalized recommendations. It is currently available in Turkish and is limited to the context of the FODMAP diet.

The COVID-19 pandemic has affected the lives and health of many people. Among them, health care workers indeed played the biggest role. In this work, we investigate the health care workers job applications, and the job postings change with the time series analysis perspective. The dataset is provided by Kariyer.net, which is the foremost job-searching company in Turkey. Since the data set includes job applications over every region of country, the results reveal important facts about health care workers in Turkey. We aim to study the possible effect of vaccination on health care workers job search decisions.

Health data plays a pivotal role in modern healthcare, guiding patient care, diagnoses, treatments, and outcomes. This extensive data repository encompasses electronic health records, medical imaging, test reports, and administrative information, empowering healthcare practitioners and researchers to make evidence-based decisions to improve patient well-being. In the complex healthcare landscape, handling health data presents challenges. While relational databases have historically dominated many industries, including healthcare, innovative alternatives like graph databases are gaining favor. Due to its complex and interconnected nature, healthcare data often loses semantic data integrity when modeled in relational databases. In contrast, graph databases have shown remarkable performance with interconnected data. Consequently, there is a belief that modeling health data as a whole on a graph database would produce excellent results. This preliminary study investigates how graph databases can efficiently manage health data by comparing simple data modeling and query performance. The research utilizes a dataset that is publicly available from a hospital in the United States. The dataset covers multiple areas, including hospital admissions, diagnoses, laboratory results, and prescription information for patients diagnosed with diabetes. Initially, an Entity-Relationship Diagram (ERD) models this two-dimensional tabular dataset and is built on a relational database. Subsequently, the ERD is transformed into a graph database schema and built on a NoSQL graph database system. Both databases are normalized during the modeling process, and they share identical data to ensure consistency in data entry. Following this, varying degrees of complex queries are constructed and enacted using the query languages of both database management systems. The primary results indicate that Neo4j outperforms PostgreSQL in performance, though slight inconsistencies in data entry were noted. It highlights their potential in enhancing healthcare data management for better patient care and outcomes.

The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions. The latter holds significant importance when available candidates in a specific sector are also scarce; hence, finding candidates from similar sectors becomes crucial. This work focuses on discovering possible sector similarities through relational analysis. We employ several algorithms from the frequent pattern mining and collaborative filtering domains, namely negFIN, Alternating Least Squares, Bilateral Variational Autoencoder, and Collaborative Filtering based on Pearson’s Correlation, Kendall and Spearman’s Rank Correlation coefficients. The algorithms are compared on a real-world dataset supplied by a major recruitment company, Kariyer.net, from Turkey. The insights and methods gained through this work are expected to increase the efficiency and accuracy of various methods, such as recommending jobs or finding suitable candidates for open positions.

Respiratory rate (RR) is a physiological parameter typically used to monitor patient status in clinical settings. The goal of the Respiratory Rate Prediction Project is to use supervised machine learning techniques to estimate a person’s respiratory rate using real-time, continuous Photoplethysmogram (PPG) and Electrocardiogram (ECG) and oximeter data. In addition, it is also our goal to investigate the feasibility of using such data to improve diagnostic processes in healthcare. It consists of a series of studies of different algorithms for respiratory rate estimation from clinical data and is complemented by the provision of publicly available datasets and resources.

With the image processing techniques, nowadays it is possible to obtain 3D X-ray tomography which is used in medical diagnosis. Such systems require a reconstruction of an object - a human body in this case- in 3D from a set of its 2D projections. The reconstruction volume is usually discretized on a regular grid of isotropic voxels which implies an increase in their number to achieve good spatial resolution. In this work, we propose a process to obtain 3D mesh generation. method allowing to discretize the 3D reconstruction space in a relevant way directly from the structural information contained in the projection data. The idea is to obtain a representation adapted to the studied object. Here we have recourse to a tetrahedral mesh matching the structure of the object: the mesh density adapts according to the interfaces and homogeneous regions. To build such a mesh, the first step of the method consists in detecting the edges in the 2D projection data. The structural information thus obtained is then merged in the reconstruction space in order to construct a point cloud sampling the 3D interfaces of the imaged object.

Wearing a non-medical mask or face covering helps reduce the spread of COVID-19 in the community. The use of non-medical masks or face covers changes communication for everyone, but this presents an additional challenge for people with hearing loss or communication difficulties. People with hearing loss may have difficulty hearing in difficult situations, such as in noisy places or when they cannot trust lip reading cues or body expressions. face. In this study, a survey is implemented where speech recognition level-based questions are asked to the participants. Then, the same questions are asked again for the cases where medical masks are worn. In doing so, participants are also asked to explain the main difference between the pandemic period and the time before pandemic. Results show that wearing a medical mask has a concrete impact on speech recognition and causes a degrade on hearing level.

Air pollution is the contamination of the internal or external environment of any chemical, physical or biological material and altering the natural properties of the atmosphere. Air pollution is caused by natural causes such as volcanic eruptions, forest fires, or excessive evaporation, as well as by increasing traffic density, especially by artificial causes such as fuel types used by urban inhabitants and industrialization. Each year, countries keep statistics of deaths caused by different types of direct air pollution or indirectly by diseases caused by these different types of pollution. In this study, the impact of countries' geographical region and population density on mortality rates due to air pollution is analyzed. A dataset of Turkey and the twenty six countries covers information such as the population density, different types of pollution rates, number of deaths caused by each type of pollution and their ratios to current population number throughout a time period between 1990-2017. In the data analysis, air pollution types were examined one by one and it was sought to identify the similarities among countries. As a result of these analysis, it was shown that there are similarities between Turkey and the different neighbour countries on the death cases observed due to different types of air pollution.

Investigating the incidence, type, and preventability of adverse drug events (ADE) and medication errors is crucial to improving the quality of health care service. ADEs, medications errors can be extracted from practice data, patients feedback and especially from medication order. In this study, we examine the dataset filled with medication orders and with a web service based approach, each medication order is analyzed to avoid from drug-drug interaction. Firstly, the study is performed on a training set to explore the drug-drug interactions based on the clinical drug component of each medication. Then the proposed web service approach is integrated into the actual centralized Medication Order Management System (MOMS) of the hospital. With this real time checking ADE mechanism, doctors are warned in case there is a possible ADE or a medication error between the medications.