Data mining in medical records for the enhancement of strategic decisions: a case study

The impact and popularity of competition concept has been increasing in the last decades and this concept has escalated the importance of giving right decision for organizations. Decision makers have encountered the fact of using proper scientific methods instead of using intuitive and emotional choices in decision making process. In this context, many decision support models and relevant systems are still being developed in order to assist the strategic management mechanisms. There is also a critical need for automated approaches for effective and efficient utilization of massive amount of data to support corporate and individuals in strategic planning and decision-making. Data mining techniques have been used to uncover hidden patterns and relations, to summarize the data in novel ways that are both understandable and useful to the executives and also to predict future trends and behaviors in business. There has been a large body of research and practice focusing on different data mining techniques and methodologies. In this study, a large volume of record set extracted from an outpatient clinic’s medical database is used to apply data mining techniques. In the first phase of the study, the raw data in the record set are collected, preprocessed, cleaned up and eventually transformed into a suitable format for data mining. In the second phase, some of the association rule algorithms are applied to the data set in order to uncover rules for quantifying the relationship between some of the attributes in the medical records. The results are observed and comparative analysis of the observed results among different association algorithms is made. The results showed us that some critical and reasonable relations exist in the outpatient clinic operations of the hospital which could aid the hospital management to change and improve their managerial strategies regarding the quality of services given to outpatients.Decision Making, Medical Records, Data Mining, Association Rules, Outpatient Clinic.

Developmental characteristics of children aged 1-6 years with food refusal

Objective: The aim of this study was to compare the sociodemographic and developmental characteristics of children with food refusal and children with no history of feeding problems. Design: Cross-sectional case-control study. Sample. 30 children aged 1-6 years who were seen in the outpatient clinics for food refusal formed the case group, and 30 healthy children matched for age, sex, and socioeconomic status formed the control group. Methods. Anthropometric indices and early developmental characteristics of all the children in the study were evaluated and also their developmental levels were determined using the Ankara Developmental Screening Inventory. Results: The mean age of children with food refusal was 42.4 +/- 17.6 months, and the male/female ratio was 12/18. Children with food refusal had shorter mean breastfeeding durations and lower mean birth weights, body mass index, percentage height for age, and percentage weight for height values than those of the controls. There were no significant differences between the 2 groups in developmental delays. Conclusions: These results suggest that food refusal may be related to lower birth weight and shorter breastfeeding duration. Further research with larger samples is needed to clarify these relationships and the effects of feeding problems on the growth and development of children

The Validity and Reliability of Turkish Version of Separation Anxiety Symptom Inventory and Adult Separation Anxiety Questionnaire

The Validity and Reliability of Turkish Version of Separation Anxiety Symptom Inventory and Adult Separation Anxiety Questionnair

Psychometric Properties of the Turkish Version of the Structured Clinical Interview for Separation Anxiety Symptoms

Objective: The aim of this study was to assess psychometric properties of the Turkish version of the Structured Clinical Interview for Separation Anxiety Symptoms (SCI-SAS)

The Validity and Reability of Turkish Version of Separation Anxiety Symptoms Inventory and Adult Separation Anxiety Questionnaire

The Validity and Reliability of Turkish Version of Separation Anxiety Symptom Inventory and Adult Separation Anxiety Questionnair

Green synthesis of silver nanoparticles using Sambucus ebulus leaves extract: Characterization, quantitative analysis of bioactive molecules, antioxidant and antibacterial activities

Sambucus ebulus is a well-known medicinal plant used as a traditional medicine due to its bioactive compound contents. The importance of nanotechnology has increased recently since it has effective and widespread usage areas. In this study, silver nanoparticles (AgNPs@Se) were synthesized using S. ebulus leaf extract and their structures were clarified by spectroscopic analyses including FTIR, UV–Vis, and XRD. The morphology and particle size of AgNPs@Se were determined by SEM analysis. Quantitative analysis of bioactive compounds of S. ebulus leaf extract was defined by HPLC analysis. Antioxidant activity of extract and AgNPs@Se was executed by DPPH•, ABTS•+, and FRAP assays. The antibacterial activity of AgNPs was carried out using agar well diffusion and microdilution broth methods. Quantitative analysis of bioactive compounds in S. ebulus leaf extract was established by HPLC. The average particle size of nanoparticles was calculated as 18.6 nm. Isoquercitrin was found as a major product. AgNPs@Se displayed excellent antioxidant activity, but they revealed a moderate antibacterial effect. The structure of nanoparticles was assigned as a face center cubic unit cell. Hence, nanoparticles could be a favorable agent for the pharmaceutical and food industry

Age and gender classification from facial features and object detection with machine learning

In recent years, development of the machine learning algorithms has led to the creation of intelligent surveillance systems. Thanks to the machine learning, it is possible to perform intelligent surveillance by recognizing people's facial features, classifying their age and gender, and detecting objects around instead of ordinary surveillance. In this study, a novel algorithm has been developed that classifies people's age and gender with a high accuracy rate. In addition, a novel object recognition algorithm has been developed that detects objects quickly and with high accuracy. In this study, age and gender classification was made based on the facial features of people using Convolutional Neural Network (CNN) architecture. Secondly, object detection was performed using different machine learning algorithms and the performance of the different machine learning algorithms was compared in terms of median average precision and inference time. The accuracy of the age and gender classification algorithm was tested using the Adience dataset and the results were graphed. The experimental results show that age and gender classification algorithms successfully classify people's age and gender. Then, the performances of object detection algorithms were tested using the COCO dataset and the results were presented in graphics. The experimental results stress that machine learning algorithms can successfully detect objects

Analysis of Amaryllidaceae alkaloids in Galanthus krasnovii by GC-MS

63rd International Congress and Annual Meeting of the Society-for-Medicinal-Plant-and-Natural-Product-Research (GA) -- AUG 23-27, 2015 -- Budapest, HUNGARYWOS: 00036755810052