ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

Statistical analysis and data mining of Medicare patients with diabetes.

The purpose of this dissertation is to find ways to decrease Medicare costs and to study health outcomes of diabetes patients as well as to investigate the influence of Medicare, part D since its introduction in 2006 using the CMS CCW (Chronic Condition Data Warehouse) Data and the MEPS (Medical Expenditure Panel Survey) data. In this dissertation, we introduce pattern recognition analysis into the study of medical characteristics and demographic characteristics of the inpatients who have a higher readmission risk. We also broaden the cost-effectiveness analysis by including medical resources usage when investigating the effects of Medicare, part D. In addition, we apply several statistical linear models such as the generalized linear model and data mining techniques such as the neural network model to study the costs and outcomes of both inpatients and outpatients with diabetes in Medicare. Moreover, some descriptive statistics such as kernel density estimation and survival analysis are also employed. One important conclusion from these analyses is that only diseases and procedures, rather than age are key factors to inpatients\u27 mortality rate. Another important discovery is that at the influence of Medicare part 0, insulin is the most efficient oral anti-diabetes drug treatment and that the drug usage in 2006 is not as stable as that in 2005. We also find that the patients who are discharged to home or hospice are more likely to re-enter the hospital after discharge within 30 days. Two - way interaction effect analysis demonstrates that diabetes complications interact with each other, which makes healthcare costs and health outcomes different between a case with one complication and a case with two complications. Accordingly, we propose some useful suggestions. For instance, as for how to decrease Medicare payments for outpatients with diabetes, we suggest that the patients should often monitor their blood glucose level. We also recommend that inpatients with diabetes should pay more attention to their kidney disease, and use prevention to avoid such diseases to decrease the costs

Utilizing Temporal Information in The EHR for Developing a Novel Continuous Prediction Model

Type 2 diabetes mellitus (T2DM) is a nation-wide prevalent chronic condition, which includes direct and indirect healthcare costs. T2DM, however, is a preventable chronic condition based on previous clinical research. Many prediction models were based on the risk factors identified by clinical trials. One of the major tasks of the T2DM prediction models is to estimate the risks for further testing by HbA1c or fasting plasma glucose to determine whether the patient has or does not have T2DM because nation-wide screening is not cost-effective. Those models had substantial limitations on data quality, such as missing values. In this dissertation, I tested the conventional models which were based on the most widely used risk factors to predict the possibility of developing T2DM. The AUC was an average of 0.5, which implies the conventional model cannot be used to screen for T2DM risks. Based on this result, I further implemented three types of temporal representations, including non-temporal representation, interval-temporal representation, and continuous-temporal representation for building the T2DM prediction model. According to the results, continuous-temporal representation had the best performance. Continuous-temporal representation was based on deep learning methods. The result implied that the deep learning method could overcome the data quality issue and could achieve better performance. This dissertation also contributes to a continuous risk output model based on the seq2seq model. This model can generate a monotonic increasing function for a given patient to predict the future probability of developing T2DM. The model is workable but still has many limitations to overcome. Finally, this dissertation demonstrates some risks factors which are underestimated and are worthy for further research to revise the current T2DM screening guideline. The results were still preliminary. I need to collaborate with an epidemiologist and other fields to verify the findings. In the future, the methods for building a T2DM prediction model can also be used for other prediction models of chronic conditions

A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data

A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. Prognostic models have become essential instruments in medicine. The models are used for prediction purposes of guiding doctors to make a smart diagnosis, patient-specific decisions or help in planning the utilization of resources for patient groups who have similar prognostic paths. Dynamic Bayesian networks theoretically provide a very expressive and flexible model to solve temporal problems in medicine. However, this involves various challenges due both to the nature of the clinical domain, and the nature of the DBN modelling and inference process itself. The challenges from the clinical domain include insufficient knowledge of temporal interactions of processes in the medical literature, the sparse nature and variability of medical data collection, and the difficulty in preparing and abstracting clinical data in a suitable format without losing valuable information in the process. Challenges about the DBN methodology and implementation include the lack of tools that allow easy modelling of temporal processes. Overcoming this challenge will help to solve various clinical temporal reasoning problems. In this thesis, we addressed these challenges while building a temporal network with explanations of the effects of predisposing factors, such as age and gender, and the progression information of all diagnoses using claims data from an insurance company in Kenya. We showed that our network could differentiate the possible probability exposure to a diagnosis given the age and gender and possible paths given a patient's history. We also presented evidence that the more patient history is provided, the better the prediction of future diagnosis

Sickness absence among patients with chronic pain in Swedish specialist healthcare

Background: Chronic pain beyond three months is a global public health problem. Every third adult suffers from a chronic pain condition, resulting in a socioeconomic burden that corresponds to 3-10% of gross domestic product in western economies. This burden can be largely attributed to absenteeism-related productivity loss where a few highly impaired individuals are the most resource-intensive. Simultaneously, a detailed overview of sickness absence (SA) associated with chronic pain is complicated by incongruent classification due to conflicting perspectives on the condition as either a symptom or a disease in its own right. Aim: Based on a well-defined chronic pain population in the Swedish specialist healthcare, this thesis primarily aims to provide a SA overview, to explore the possibility of SA prevention, and to evaluate interdisciplinary treatment (IDT) as a SA intervention. A secondary objective was to assess the psychometric properties of three questionnaires that measure the core domains of the chronic pain experience. Methods: The aims were addressed in three register-based studies using microdata from five Swedish national registers. Study I used sequence analysis to describe SA in 44,241 patients over a 7-year period and subsequently developed a machine learning-based model to predict chronic pain-related SA in the final two years. Study II emulated a target trial to compare the total SA duration over a 5-year period for 25,613 patients that were either included in an IDT program or in other/no interventions. Study III analyzed the properties of the Short Form-36 Health Survey (SF-36), the EuroQol 5-Dimensions instrument (EQ-5D), and the Hospital Anxiety and Depression Scale (HADS) within the item response theory-framework. Results: SA increased from 17% to 48% over the five years before specialist healthcare entry to then decrease to 38% over the final two years. With information on eight predictors, it was possible to discriminate between patients that would have low or high SA in the coming two years with 80% accuracy. SA trends were similar for patients in IDT programs and other/no interventions, albeit the IDT patients had 67 (95% CI: 48, 87) more SA days over the complete 5-year period. Finally, the psychometric evaluation revealed that SF-36 adequately captured physical and mental health, while HADS was suitable as a measure of overall emotional distress, and EQ-5D had insufficient precision for any meaningful application. Conclusion: Our findings are most useful to guide policy and research. SA in the studied patients remained high over the entire observation period. Decision support tools could prove valuable in identifying patients at risk of high SA earlier in the healthcare chain in order to direct preventative measures. We found no support for IDT decreasing SA more than other/no interventions, but it is possible that this was a consequence of our methodology. Further studies of the IDT effects are needed, but uncontrolled designs that attribute SA change over time to IDT are inappropriate for this purpose, as the SA peak observed around specialist healthcare entry is likely to be driven by the referral procedure. Finally, SF-36 and HADS are psychometrically sound measures of the chronic pain experience core domains

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling

Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks

The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine

Use of statistical analysis, data mining, decision analysis and cost effectiveness analysis to analyze medical data : application to comparative effectiveness of lumpectomy and mastectomy for breast cancer.

Statistical models have been the first choice for comparative effectiveness in clinical research. Though effective, these models are limited when the data to be analyzed do not fit the assumed distributions; which is mostly the case when the study is not a clinical trial. In this project, data mining, decision analysis and cost effectiveness analysis methods were used to supplement statistical models in comparing lumpectomy to mastectomy for surgical treatment of breast cancer. Mastectomy has been the gold standard for breast cancer treatment for since the 1800s. In the 20th century, an equivalence of mastectomy and lumpectomy was established in terms of long-term survival and disease free survival. However, short term comparative effectiveness in post-operative outcomes has not been fully explored. Studies using administrative data are lacking and no study has used new technologies of self-expression, particularly the internet discussion board. In this study, data used were from the Nationwide Inpatient Sample (NIS) 2005, the Thomson Reuter\u27s MarketScan 2000 - 2001, the medical literature on clinical trials and online individuals\u27 posts in discussion boards on breastcancer.org. The NIS was used to compare lumpectomy to mastectomy in terms of hospital length of stay, total charges and in-hospital death at the time of surgery. MarketScan data was used to evaluate the comparative follow-up outcomes in terms of risk of repeat hospitalization, risk of repeat operation, number of outpatient services, number of prescribed medications, length of stay, and total charges per post-operative hospital admission on a period of eight months average. The MarketScan was also used to construct a simple post-operative hospital admission predictive model and to perform short-term cost-effectiveness analysis. The medical literature was used to analyze long term -10 years- mortality and recurrence for both treatments. The web postings were used to evaluate the comparative cost to improve quality of life in terms of patient satisfaction. In NIS and MarketScan data, International Classification of Disease, 9th revision, Clinical Modification (lCD-9-CM) diagnosis codes were used to extract cases of breast cancer; and ICD-9-CM procedure codes and Current Procedural Terminology, 4th edition procedure codes were used to form groups of treatment. Data were pre-processed and prepared for analysis using data mining techniques such as clustering, sampling and text mining. To clean the data for statistical models, some continuous variables were normalized using methods such as logarithmic transformation. Statistical models such as linear regression, generalized linear models, logistic and proportional hazard (Cox) regressions were used to compare post-operative outcomes of lumpectomy versus mastectomy. Neural networks, decision tree and logistic regression predictive modeling techniques were compared to create a simple predictive model predicting 90-day post-operative hospital re-admission. Cost and effectiveness were compared with the Incremental Cost Effectiveness Ratio (ICER). A simple method to process and analyze online po stings was created and used for patients\u27 input in the comparison of lumpectomy to mastectomy. All statistical analyses were performed in SAS 9.2. Data Mining was performed in SAS Enterprise Miner (EM) 6.1 and SAS Text Miner. Decision analysis and Cost Effectiveness Analysis were performed in TreeAge Pro 2011. A simple comparison of the two procedures using the NIS 2005, a discharge-level data, showed that in general, a lumpectomy surgery is associated with a significantly longer stay and more charges on average. From the MarketScan data, a person-level data where a patient can be followed longitudinally, it was found that for the initial hospitalization, patients who underwent mastectomy had a non-significant longer hospital stay and significantly lower charges. The post-operative number of outpatient services, prescribed medications as well as length of stay and charges for post-operative hospital admissions were not statistically significant. Using the MarketScan data, it was also found that the best model to predict 90-day post-operative hospital admission was logistic regression. A logistic regression revealed that the risk of a hospital re-admission within 90 days after surgery was 65% for a patient who underwent lumpectomy and 48% for a patient who underwent mastectomy. A cost effectiveness analysis using Markov models for up to 100 days after surgery showed that having lumpectomy saved hospital related costs every day with a minimum saving of $33 on day 10. In terms of long-term outcomes, the use of decision analysis methods on the literature review data revealed that, 10-years after surgery, 739 recurrences and 84 deaths were prevented among 10,000 women who had mastectomy instead of lumpectomy. Factoring patients\u27 preferences in the comparison of the two procedures, it was found that patients who undergo lumpectomy are non-significantly more satisfied than their peers who undergo mastectomy. In terms of cost, it was found that lumpectomy saves$517 for each satisfied individual in comparison to mastectomy. In conclusion, the current project showed how to use data mining, decision analysis and cost effectiveness methods to supplement statistical analysis when using real world nonclinical trial data for a more complete analysis. The application of this combination of methods on the comparative effectiveness of lumpectomy and mastectomy showed that in terms of cost and patients\u27 quality of life measured as satisfaction, lumpectomy was found to be the better choice