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.

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

[EN] The study presents some results of customer paths¿ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men¿s bathroom or women¿s bathroom. Since the study has a comprehensive scope, we focused on male and female customers¿ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.Dogan, O.; Bayo-Monton, JL.; Fernández Llatas, C.; Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors. 19(3):1-20. https://doi.org/10.3390/s19030557S120193Oosterlinck, D., Benoit, D. F., Baecke, P., & Van de Weghe, N. (2017). Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits. Applied Geography, 78, 55-65. doi:10.1016/j.apgeog.2016.11.005Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B., & Drap, P. (2016). Tracking multiple persons under partial and global occlusions: Application to customers’ behavior analysis. Pattern Recognition Letters, 81, 11-20. doi:10.1016/j.patrec.2016.04.011Wu, Y., Wang, H.-C., Chang, L.-C., & Chou, S.-C. (2015). Customer’s Flow Analysis in Physical Retail Store. Procedia Manufacturing, 3, 3506-3513. doi:10.1016/j.promfg.2015.07.672Dogan, O., & Öztaysi, B. (2018). In-store behavioral analytics technology selection using fuzzy decision making. Journal of Enterprise Information Management, 31(4), 612-630. doi:10.1108/jeim-02-2018-0035Hwang, I., & Jang, Y. J. (2017). Process Mining to Discover Shoppers’ Pathways at a Fashion Retail Store Using a WiFi-Base Indoor Positioning System. IEEE Transactions on Automation Science and Engineering, 14(4), 1786-1792. doi:10.1109/tase.2017.2692961Abedi, N., Bhaskar, A., Chung, E., & Miska, M. (2015). Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses. Transportation Research Part C: Emerging Technologies, 60, 124-141. doi:10.1016/j.trc.2015.08.010Mou, S., Robb, D. J., & DeHoratius, N. (2018). Retail store operations: Literature review and research directions. European Journal of Operational Research, 265(2), 399-422. doi:10.1016/j.ejor.2017.07.003Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., & Weijters, A. J. M. M. (2003). Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering, 47(2), 237-267. doi:10.1016/s0169-023x(03)00066-1Ou-Yang, C., & Winarjo, H. (2011). Petri-net integration – An approach to support multi-agent process mining. Expert Systems with Applications, 38(4), 4039-4051. doi:10.1016/j.eswa.2010.09.066Partington, A., Wynn, M., Suriadi, S., Ouyang, C., & Karnon, J. (2015). Process Mining for Clinical Processes. ACM Transactions on Management Information Systems, 5(4), 1-18. doi:10.1145/2629446Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., … Song, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88, 34-43. doi:10.1016/j.ijmedinf.2015.12.018Funkner, A. A., Yakovlev, A. N., & Kovalchuk, S. V. (2017). Towards evolutionary discovery of typical clinical pathways in electronic health records. Procedia Computer Science, 119, 234-244. doi:10.1016/j.procs.2017.11.181Jans, M., Alles, M., & Vasarhelyi, M. (2013). The case for process mining in auditing: Sources of value added and areas of application. International Journal of Accounting Information Systems, 14(1), 1-20. doi:10.1016/j.accinf.2012.06.015Yoshimura, Y., Sobolevsky, S., Ratti, C., Girardin, F., Carrascal, J. P., Blat, J., & Sinatra, R. (2014). An Analysis of Visitors’ Behavior in the Louvre Museum: A Study Using Bluetooth Data. Environment and Planning B: Planning and Design, 41(6), 1113-1131. doi:10.1068/b130047pDe Leoni, M., van der Aalst, W. M. P., & Dees, M. (2016). A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems, 56, 235-257. doi:10.1016/j.is.2015.07.003Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Arroyo, R., Yebes, J. J., Bergasa, L. M., Daza, I. G., & Almazán, J. (2015). Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls. Expert Systems with Applications, 42(21), 7991-8005. doi:10.1016/j.eswa.2015.06.016Popa, M. C., Rothkrantz, L. J. M., Shan, C., Gritti, T., & Wiggers, P. (2013). Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information. Pattern Recognition Letters, 34(7), 809-819. doi:10.1016/j.patrec.2012.04.015Kang, L., & Hansen, M. (2017). Behavioral analysis of airline scheduled block time adjustment. Transportation Research Part E: Logistics and Transportation Review, 103, 56-68. doi:10.1016/j.tre.2017.04.004Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42(23), 9236-9251. doi:10.1016/j.eswa.2015.07.040Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Van der Aalst, W. M. P., Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Alves de Medeiros, A. K., Song, M., & Verbeek, H. M. W. (2007). Business process mining: An industrial application. Information Systems, 32(5), 713-732. doi:10.1016/j.is.2006.05.003M. Valle, A., A.P. Santos, E., & R. Loures, E. (2017). Applying process mining techniques in software process appraisals. Information and Software Technology, 87, 19-31. doi:10.1016/j.infsof.2017.01.004Juhaňák, L., Zounek, J., & Rohlíková, L. (2019). Using process mining to analyze students’ quiz-taking behavior patterns in a learning management system. Computers in Human Behavior, 92, 496-506. doi:10.1016/j.chb.2017.12.015Sedrakyan, G., De Weerdt, J., & Snoeck, M. (2016). Process-mining enabled feedback: «Tell me what I did wrong» vs. «tell me how to do it right». Computers in Human Behavior, 57, 352-376. doi:10.1016/j.chb.2015.12.040Schoor, C., & Bannert, M. (2012). Exploring regulatory processes during a computer-supported collaborative learning task using process mining. Computers in Human Behavior, 28(4), 1321-1331. doi:10.1016/j.chb.2012.02.016Werner, M., & Gehrke, N. (2015). Multilevel Process Mining for Financial Audits. IEEE Transactions on Services Computing, 8(6), 820-832. doi:10.1109/tsc.2015.2457907De Weerdt, J., Schupp, A., Vanderloock, A., & Baesens, B. (2013). Process Mining for the multi-faceted analysis of business processes—A case study in a financial services organization. Computers in Industry, 64(1), 57-67. doi:10.1016/j.compind.2012.09.010Herbert, L., Hansen, Z. N. L., Jacobsen, P., & Cunha, P. (2014). Evolutionary Optimization of Production Materials Workflow Processes. Procedia CIRP, 25, 53-60. doi:10.1016/j.procir.2014.10.010Yim, J., Jeong, S., Gwon, K., & Joo, J. (2010). Improvement of Kalman filters for WLAN based indoor tracking. Expert Systems with Applications, 37(1), 426-433. doi:10.1016/j.eswa.2009.05.047Delafontaine, M., Versichele, M., Neutens, T., & Van de Weghe, N. (2012). Analysing spatiotemporal sequences in Bluetooth tracking data. Applied Geography, 34, 659-668. doi:10.1016/j.apgeog.2012.04.003Frisby, J., Smith, V., Traub, S., & Patel, V. L. (2017). Contextual Computing : A Bluetooth based approach for tracking healthcare providers in the emergency room. Journal of Biomedical Informatics, 65, 97-104. doi:10.1016/j.jbi.2016.11.008Yoshimura, Y., Krebs, A., & Ratti, C. (2017). Noninvasive Bluetooth Monitoring of Visitors’ Length of Stay at the Louvre. IEEE Pervasive Computing, 16(2), 26-34. doi:10.1109/mprv.2017.33Cao, Q., Jones, D. R., & Sheng, H. (2014). Contained nomadic information environments: Technology, organization, and environment influences on adoption of hospital RFID patient tracking. Information & Management, 51(2), 225-239. doi:10.1016/j.im.2013.11.007Larson, J. S., Bradlow, E. T., & Fader, P. S. (2005). An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22(4), 395-414. doi:10.1016/j.ijresmar.2005.09.005Fernandez-Llatas, C., Martinez-Millana, A., Martinez-Romero, A., Benedi, J. M., & Traver, V. (2015). Diabetes care related process modelling using Process Mining techniques. Lessons learned in the application of Interactive Pattern Recognition: coping with the Spaghetti Effect. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2015.7318809Conca, T., Saint-Pierre, C., Herskovic, V., Sepúlveda, M., Capurro, D., Prieto, F., & Fernandez-Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. Journal of Medical Internet Research, 20(4), e127. doi:10.2196/jmir.8884De Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery, 14(2), 245-304. doi:10.1007/s10618-006-0061-7Heyer, L. J. (1999). Exploring Expression Data: Identification and Analysis of Coexpressed Genes. Genome Research, 9(11), 1106-1115. doi:10.1101/gr.9.11.1106Yang, W.-S., & Hwang, S.-Y. (2006). A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications, 31(1), 56-68. doi:10.1016/j.eswa.2005.09.00

AN OVERVIEW OF FOOD FRAUD IN TURKEY AND THE POTENTIAL RISK FOR PEANUT

Depending on the increase in the demand for ready-to-eat food products in recent years, food fraud tends to increase as well. The color, smell, taste, appearance, content, nutritive value, origin, etc. in foods determine purchasing preferences of consumers. Food adulteration and counterfeit have been practiced since ancient times. This paper consists of the results of two different study on food fraud in Turkey. In the first study, questionnaire data were collected from 263 -people with different occupations and ages with a total eleven questions throughout Turkey by using the face‑to‑face interview survey method.The collected data showed that the top three product categories that the highest probabilities of being fraudulent were milk and milk products (42.6%), meat and meat products (20.2%), and bread and bakery products (16%). In the second study, research was conducted on what types of fraudulent were applied in Turkish peanuts. Total 30 peanut samples were analysed to determine synthetic colorant such as E124 ponceau 4R, E129 allura red and E122 carmosine etc. E124 ponceau 4R was found as a color material in 4 samples of the total 30 roasted unshelled peanuts in concentrations of 4.24 mg/kg, 3.30 mg/kg, 4.47 mg/kg and 2.49 mg/kg. On the other hand, it was below the detectable limit in other samples

Coplanar Asymmetric Angles and Symmetric Energy Sharing Triple Differential Cross Sections for 200 EV Electron-Impact Ionization of Ar (3p)

We have measured triple differential cross sections (TDCSs) for electron-impact ionization of the 3p shell of Ar at 200 eV incident electron energy. The experiments have been performed in coplanar asymmetric energy sharing geometry. The experimental results are compared with the theoretical models of three body distorted wave (3DW) and distorted wave Born approximation (DWBA)

Rediscovery of penicillin of psychiatry: haloperidol decanoate

BACKGROUND: Haloperidol has been used as an effective antipsychotic for many years and continues to be one of the first options in difficult patients who require parenteral therapy in the acute phase. However, the depot form is less preferred in the treatment of patients with non-adherence among these patients whose clinical stabilization has been achieved by using parenteral haloperidol in the acute phase. Therefore, updating the information about the side effects of the depot form of haloperidol, which is still an effective treatment option, will be useful in reconsidering the position of this medicine among new and different options. METHODS: A total of 54 schizophrenic patients with severe symptoms and poor adherence to treatment who were hospitalized and treated with depot haloperidol following an acute stabilization period were included in this study. First, the Structured Clinical Interview for DSM-IV Axis I disorders (SCID-CV) was used to confirm the diagnosis, the Brief Psychiatric Rating Scale (BPRS), Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) to assess the clinical severity and Global Assessment of Functioning (GAF) to assess the functionality. The Simpson-Angus Scale (SAS) was used to assess extrapyramidal side effects. With the exception of Visit 0, plasma haloperidol levels were measured at all visits. Also, measurements of waist circumference and weight, plasma fasting blood glucose, triglyceride, HDL, iron, haemoglobin (Hgb), prolactin (PRL) and HbA1c were also used for evaluation of the metabolic effects. RESULTS: Significant improvements were observed in the BPRS, SANS, SAPS scores in the long-term follow-up with the depot haloperidol treatment. While the dosage decreased over time, the plasma levels remained changed, and symptom improvement was maintained. No signs such as neuroleptic malignant syndrome or acute dystonia were observed and SAS scores were within acceptable limits during the treatment (mu = 1.40 +/- 2.55). There is no statistically significant difference between measurements of the weight even there was a significant difference between three of the waist circumference values (p = 0.987). The first measurement of the waist circumference is statistically significantly higher than both the mid-measurement and the final measurement, interestingly (p = 0.002). When fasting blood glucose, triglyceride, HDL, iron, Hgb, PRL and HbA1c were measured at different times throughout the study, only prolactin levels increased significantly over time with the use of haloperidol (p < 0.001). At the end of a year, 50% of the patients participating in the study still continued to use the haloperidol decanoate. This means also that half of the patients had stopped to use haloperidol decanoate. However, only 18.5% of them (n = 5) discontinued use of this drug because of extrapyramidal side effects. CONCLUSION: Depot haloperidol remains an effective treatment option that improves treatment compliance in challenging schizophrenia patients with severe symptoms. The long-term metabolic and extrapyramidal side effect profile of the patients were generally within the safe limits with the use of haloperidol depot. According to the obtained data, the depot haloperidol continues to be a reliable treatment option in terms of adverse effects in the maintenance treatment of schizophrenia patients with severe symptoms and poor adherence to treatment

Process mining based on patient waiting time: an application in health processes

Purpose Similar to many business processes, waiting times are also essential for health care processes, especially in obstetrics and gynecology outpatient department (GOD), because pregnant women may be affected by long waiting times. Since creating process models manually presents subjective and nonrealistic flows, this study aims to meet the need of an objective and realistic method. Design/methodology/approach In this study, the authors investigate time-related bottlenecks in both departments for different doctors by process mining. Process mining is a pragmatic analysis to obtain meaningful insights through event logs. It applies data mining techniques to business process management with more comprehensive perspectives. Process mining in this study enables to automatically create patient flows to compare considering each department and doctor. Findings The study concludes that average waiting times in the GOD are higher than obstetrics outpatient department. However, waiting times in departments can change inversely for different doctors. Research limitations/implications The event log was created by expert opinions because activities in the processes had just starting timestamp. The ending time of activity was computed by considering the average duration of the corresponding activity under a normal distribution. Originality/value This study focuses on administrative (nonclinical) health processes in obstetrics and GOD. It uses a parallel activity log inference algorithm (PALIA) to produce process trees by handling duplicate activities. Infrequent information in health processes can have critical information about the patient. PALIA considers infrequent activities in the event log to extract meaningful information, in contrast to many discovery algorithms

A Recommendation System in E-Commerce with Profit-Support Fuzzy Association Rule Mining (P-FARM)

E-commerce is snowballing with advancements in technology, and as a result, understanding complex transactional data has become increasingly important. To keep customers engaged, e-commerce systems need to have practical product recommendations. Some studies have focused on finding the most frequent items to recommend to customers. However, this approach fails to consider profitability, a crucial aspect for companies. From the researcher’s perspective, this study introduces a novel method called Profit-supported Association Rule Mining with Fuzzy Theory (P-FARM), which goes beyond just recommending frequent items and considers a company’s profit while making product suggestions. P-FARM is an advanced data mining technique that creates association rules by finding the most profitable items in frequent item sets. From the practitioners’ standpoints, this method helps companies make better decisions by providing them with more profitable products with fewer rules. The results of this study show that P-FARM can be a powerful tool for improving e-commerce sales and maximizing profit for businesses

Yüksekten düşme iş kazalarının giyilebilir sensörler ile tespiti ve nesnelerin interneti kullanılarak kaza etkilerinin azaltılması.

Hazardous and labor-intensive nature of the construction industry has a prominent impact on increasing the number of occupational accidents and fatalities in construction. Falls-from-height (FFH) is one of the most important sources of these fatalities. Despite many valuable prevention strategies and efforts implemented against occupational fall accidents on construction sites, the fatality rate records do not indicate a significant decrease. In medical literature, the time passed after the accident is critical to avoid preventable deaths and permanent disabilities of trauma patients. By combining these, a novel approach is exhibited to timely detect FFH accidents on construction sites using a wearable device to provide emergency medical team (EMT) with real-time notification including the height of fall and the time of fall information by leveraging Internet-of-Things (IoT). It is aimed to maintain the earliest possible medical intervention to the victim on site to help reducing severe and fatal consequences of FFH accidents for construction workers. A wearable system that can be used by construction workers on site has been developed and tested against FFH on construction sites using dummies. The experiments have shown promising results with 100% successful detection of FFH accidents by having an overall error rate of 5,8% in the calculation of the accident fall height. In order to detect FFH time correctly, an additional metric that shows the detection of the disconnected network time of the system has been investigated and the results are accurate with an overall error rate of 3.16%. Additional tests have also been conducted for the validation of the system against false positives on construction sites and none of the experiments produced false alarms during tests.M.S. - Master of Scienc

A process-centric performance management in a call center

Discovering valuable information needs some extra focuses on business processes. Although data-centric techniques yield useful results, they are insufficient to explain the causes of the problems in the process. This study aims to reveal the relationship between customer satisfaction and other key performance indicators (KPIs) affected by the activities performed during the call process. The research applies process mining, a pragmatic analysis to obtain meaningful insights through event logs. Several statistical analyses also support the process mining to test the statistical significance. The study showed that customer satisfaction is positively affected by average handle time and first call resolution, whereas staff mistakes diminish it. Moreover, problem solving is much more important than waiting in the system. Waitlisted and Waitlisted back activities are crucial elements of a call center system. Moreover, the research presents an insight for customers who give the same score after the call. It explains not only KPIs' effects but also reasons for giving satisfaction scores based on call process. Additionally, in previous studies, the customer satisfaction indicator was mainly emphasized, but other KPIs' effects on satisfaction level were ignored. This paper evaluates the impact of the identified KPIs on satisfaction in a process-oriented manner