Automatic Discovery of Hypotheses in Nuclear Cardiology

Transcription

1 POSTER'2017,'PRAGUE'MAY'23' 1' Automatic Discovery of Hypotheses in Nuclear Cardiology Ondřej Klempíř 1, Vojtěch Kaláb 1 1 Dept. of Biomedical Informatics, Czech Technical University, Nam. Sitna 3105, Kladno, Czech Republic ondrej.klempir@fbmi.cvut.cz, vojtech.kalab@fbmi.cvut.cz Abstract. Current data mining technologies lead to the boost of data processing with thousands of features measured in various medical disciplines. GUHA is a robust method for exploratory data analysis and it allows to discover all valid hypotheses in the data. The aim of our study is to investigate whether GUHA method is usable in nuclear cardiology for discovering well-known and possibly new medical knowledge. Nuclear cardiology dataset consists of approximately 10,000 observations. In this paper we present approach for generating hypotheses in medical domain with subsequent confirmation by methods of biomedical statistics. GUHA method in nuclear cardiology is unique and was used for the definition of clinical hypotheses that were subsequently tested by standard statistical methods, without domain expert. Keywords Medical data mining, biomedical statistics, nuclear cardiology, GUHA, ejection fraction, IHD. 1.! Introduction Heart failure (HF), diabetes mellitus and high blood pressure belong to the epidemics of the third millennium. Chronic HF affects about inhabitans of the Czech Republic. The most common cause of HF are conditions after a heart attack, cardiac muscle lesion and untreated or poorly treated high blood pressure. The aim of specialized centers is to diagnose a disorder and start the treatment earlier [1]. This enables better quality of life and its prolongation. Application of radionuclide examinations for diagnostics and prognosis in cardiology is increasing. Physician monitors the basic functional and spatial parameters of the heart. However, the long-term data collection produces a vast amount of knowledge hidden in electronic medical records. Using data mining (DM) methods in heart disease studies is one of the best ways how to extract knowledge and valid hypotheses. Follow-up hypotheses testing using standard statistical methods can demonstrate the importance in medical decision making and can have economic benefits. Current research uncovers findings about many applications of data mining in cardiology and nuclear cardiology. For example, Verma et al., designed a hybrid model to predict coronary artery disease, based on combination of many supervised DM methods (e.g. multiple layer perceptron, fuzzy rules algorithms, multinomial logistic regression) [2]. Podgorelec et al. proposed algorithm for automatic rules induction called AREX, using evolutionary induction of decision trees and automatic programming in the field of pediatric cardiology [3]. Especially, leading position in automatic association rules mining in cardiology for a long time held the EuroMISE research group in Prague. In nuclear cardiology, Ikuta et al. developed an open-source toolkit for enhancing patient safety through an automated data mining of nuclear medicine reports [4]. The issue is related to the machine processing of reports, not discovering valid hypotheses. About 26 % of the papers in cardiology used prediction techniques, 6 % dealt with a clustering task and only 4 % used association techniques between 2000 and 2015 [5]. DM in cardiology reacts very quickly to current knowledge in the theory of statistics, such as for censored time-to-event data [6]. Discover all valid hypotheses in the data is not a simple task. There are a huge number of possible combinations in which we want to find a valid subset. The general unary hypotheses automation (GUHA) is a robust, originally Czech, method for exploratory data analysis. It was developed in Prague from the mid-1960s several decades before the Apriori algorithm [7]. GUHA is based on a combination of mathematical logic and statistics. The result of a comprehensive long-term development is the theory of automatic creation of a hypothesis supported by input data. The method is implemented in the form of GUHA procedures [8]. GUHA is currently successfully developed in the LISp-Miner platform [9]. It includes e.g., GUHA procedures for clustering, decision trees and genetic algorithms. The aim of this study is to investigate the latest version of GUHA association rules to automatically discover hypotheses in nuclear cardiology. Some of the most important and clinically interesting hypotheses are then further tested using standard statistical methods.

2 2' O.'KLEMPÍŘ,'V.'KALÁB,'AUTOMATIC'DISCOVERY'OF'HYPOTHESES'IN'NUCLEAR'CARDIOLOGY' 2.! Methods Individual examinations were performed in the Department of Nuclear Medicine, Královské Vinohrady University Hospital, in the period between Data was measured at the specialized Institute of Nuclear Cardiology, which mainly deal with scintigraphic diagnostics. Dataset consists of approximately 10,000 observations (mean age: 62.2 ± (SD) 10.3 y.) within 45 attributes grouped into a structural scheme (see appendix). The dataset was provided to the Department of Biomedical Informatics, FBME CTU. It shows a typical problem of large biomedical data files. Many of the values of each parameter were incomplete or contained unrealistic values. The study was conducted in accordance with the CRISP-DM methodology. Among others, the data were preprocessed, refined and divided into intervals. GUHA association procedures implemented in LISp-Miner system were used for modelling phase. Statistical tests of hypotheses were computed at! = ! Myocardial perfusion scintigraphy Nuclear cardiology methods have evolved considerably in the last 30 years a huge growth. Today it represents the largest number of examinations in the field of nuclear medicine. Of the newer methods of data processing, it clearly outweighs tomographic imaging using photon radionuclides which belongs to the standard in cardiology. Myocardial perfusion scintigraphy (MPS) is a method for monitoring radiopharmaceuticals distribution in a tissue. Higher activity corresponds to a better circulation in the monitored area. MPS is able to display only the myocardium of the left ventricle. Perfusion distribution in the myocardium of a healthy individual is homogeneous, physiologically may be reduced in the apex. The absence of radioactivity (defect) in a particular portion of the myocardium is a sign of reduced or absent perfusion in this region. Activity can be affected by the way of radiopharmaceutical administration (e.g. stress versus relaxation state). Extensive clinical studies have demonstrated high sensitivity and specificity of MPS for the detection of coronary artery disease. The most important clinically measured parameters in this study are: Risk factors (binary features): hypertension, cholesterol, smoking, diabetes, family medical history. Ischemic heart disease (IHD) conclusion - positive, negative, boundary. Defects perfusion defect sizes (%) - anterior interventricular branch (RIA), left bent artery (LCX), right coronary artery (ACD), total perfusion defect (TOT). Left ventricular ejection fraction (EF) (%) - EF_SCIR (relax) vs. EF_SCIS (stress). End-diastolic left ventricle volume (EDV) (ml) relax vs. stress. 2.2! GUHA procedures GUHA procedure is an algorithm that has on the input an analyzed data and several parameters defining the wide set of interesting relationships. The output is suitable representation of all the interesting relationships existing in the given data. The most widely used GUHA procedure is the ASSOC procedure for discovering association rules. GUHA association rules in their ability to surpass classical association rules (eg. Apriori or FP-growth). GUHA association rules can generally be written as " $/&, where " is the antecedent, $ is the consequent and & represents some condition. Symbol means a contingent relationship between antecedent and consequent. Compared to the association rules obtained by Apriori algorithm, GUHA association rules differ in richer antecedent and consequent semantics (individual attributes can be associated not only in conjunction, it is possible to use expressions with disjunctions and negation of antecedent and consequent). The sign & symbolizes a selection condition to limit set for search. The symbol represents a placeholder for one of supported LISp-Miner quantifiers. Association rule φ ψ is true in the data matrix)*, if they satisfy the appropriate quantifiers ( ) in the four-fold table φ and ψ (Tab. 1): M! +! +! -! a! b! -! c! d! Tab. 1. Four-fold table. a, b, c and d represent numbers of rows fulfilling or not fulfilling the antecedent and consequent. Characterizes the relationship of antecedent and consequent of the association rule. LISp-Miner implements a rich set of quantifiers, for example: /011234,436/7289 )founded implication asserts a minimum support and a threshold of the relative count of data items satisfying the succedent among those which satisfy the antecedent: a support) a) threshold a + b K ~ /011234,4736/7289 )above average dependence asserts a minimum support and a factor expressing how many times data items satisfying the succedent have to be more frequent among those satisfying the antecedent than among all (it has a similar meaning as the measure "lift"): a a + b 1 + MNOOPQR a + c ) a RhQUMhPVW)) a + b + c + d Discovered GUHA assoc. rule is typically in the form: age<30;50> ( sex(male) BMI(higher)) state(diseased) This rule can be interpreted as: If the age of the person is in the interval <30; 50> and a person is a female or has higher BMI, then evaluate the state as disease.

3 POSTER'2017,'PRAGUE'MAY'23' 3' Fig. 1. Excitation has minimal impact to EF values. The difference between EF in relax (SCIR) and EF in stress (SCIS) is less than 1.5 %. Left: Group of non-diabetics. Center: Group of diabetics. Right: There is a strong correlation relationship between SCIS and SCIR. 3.! Results A lot of valid assoc. rules were discovered by GUHA. Some of them are well-known in nuclear cardiology, but some of them are potentially interesting for experts. 3.1! Relationships of EF and defects The first analytical issue is focused on the exploration of the relationship between perfusion defect occurrences and values of ejection fraction (stress state and relax state). Information about perfusion defects consisted of data on the size, severity and reversibility. Two groups were considered as condition χ, DIAM vs. NONDIAM. Antecedent (φ): RIA, LCX, ACD, TOT Consequent (ψ): EF_SCIS, EF_SCIR Condition (χ): risk factor DIAM/NONDIAM Several hypotheses with high support were discovered for NONDIAM group (min. support = 0.8 for founded implication, min. support = 0.3 for above average). The results showed that there is practically no difference between EF_SCIS and EF_SCIR (Fig. 1). Subsequently, this hypothesis was tested by paired t-test in both subsets. It was shown that the differences between SCIR and SCIS are less than 1.5 % (p < 0.001). Another discovered rule implies knowledge that "high value of EF decreases the risk of large defect". In other words, with EF > 40 % is a high probability that the defect is less than < 20%. The situation is illustrated through Multivariate kernel density estimation (Fig. 2). Results in the DIAM group were not fundamentally different in comparison with NONDIAM. Fig. 2. Figure demonstrates the area with the highest density and the weak relationship between defect and ejection fraction. 3.2! IHD effect on heart volume EDV An example of a specific, potentially clinically interesting hypothesis is the following rule. Patients with a negative conclusion (N = 395) and size of the defect around 25 % have significantly lower heart volume (EDV relax) than patients with a positive conclusion (N = 96). The hypothesis was verified by two sample t-test with consideration of the non-equal variances (Fig. 3).

4 4' O.'KLEMPÍŘ,'V.'KALÁB,'AUTOMATIC'DISCOVERY'OF'HYPOTHESES'IN'NUCLEAR'CARDIOLOGY' Fig. 3. IHD effect on heart volume EDV. 4.! Discussion DM methods have limitations. Researcher has to define in advance exactly what is he looking for. In addition, the results may not be reliable in terms of statistics. However, DM methods are to be used in medicine e.g. to confirm existing knowledge about some medical problem, and enable exploring new facts. Which should reveal some interesting patterns and possibly improve the existing medical knowledge. Discovered valid hypotheses in this study offer deployment - in nuclear cardiology is unnecessary to excite physically or pharmaceutically. Furthermore, it was found that patients with a negative conclusion and size of the defect around 25 % have significantly lower heart volume than patients with a positive conclusion. GUHA method allows the use of regression tree purely on binary regressors. It offers to continue examining in greater detail in the future. 3.3! Feature importance for EF GUHA method eases to answer analytical questions in terms of the most important explaining parameters. Analytical procedure ETree-Miner was implemented for this purpose. It is a tool for creating decision and explorative trees. The aim was to determine the importance of individual risk factors to explain the ejection fraction. It was obtained a decision rule that smoking and hypertension has the greatest importance for EF_SCIR. Hypothesis was verified by LASSO. Lasso is a regularization technique for performing linear regression. Lasso estimator can have smaller mean squared error than an ordinary least-squares estimator when it is applied to a new data. It produces a smaller model, with fewer predictors and is an alternative to stepwise regression and other model selection and dimensionality reduction technique. Dominant factor is not different in comparison with GUHA (Fig. 4). 5.! Conclusion This paper applies GUHA method to discover hypotheses in nuclear cardiology. Data are firstly preprocessed and then analyzed using logical GUHA procedures. GUHA method in nuclear cardiology is unique and it was used for the definition of clinical hypotheses that were subsequently tested by standard biostatistical methods, without knowledge of domain expert. This automatic hypotheses discovery can lead to improved diagnostic procedures and can change cardiology examinations. Recently, medical experts from Královské Vinohrady Hospital have confirmed that this analytical method can be suitable for real use in practice. However, our data mining research in the field of nuclear cardiology will continue in the near future. Acknowledgements The authors want to thank assoc. prof. Zoltan Szabo (Head of Department of Biomedical Informatics) for support during PhD study. Further thank goes to the Department of Information and Knowledge Engineering, University of Economics in Prague, for providing LISp-Miner system. Fig. 4. Relative importance of individual risk factors via LASSO.

5 POSTER'2017,'PRAGUE'MAY'23' 5' Appendix - attributes structural scheme 1.! General patient data a.' Genetic (Family history, Gender) b.' Continuous change (Age, BMI) 2.! Cell-metabolic level a.' Internal factors (Cholesterol, Diabetes) b.' External factors (Smoking) 3.! State of the vascular system a.' Hypertension, Blood Pressure, Heart Rate b.' Load (Blood Pressure, Heart Rate) 4.! State of the heart a.' Anatomical pathology (localization, extent and reversibility of the defect) b.' Pathophysiology (diastolic and systolic volumes, ejection fraction) 5.! The clinical evaluation a.' Diagnostic (Symptoms, Diagnosis) b.' Prognostic (Conclusion) Vojtěch KALÁB was born in Prague in He obtained his bachelor degree in Biomedical Technician at Czech Technical University, Prague in 2007 and a master degree in Methods and Appliances for Biomedicine at the same university in He is now a PhD Student at the Department of Biomedical Informatics. His research interests include Data mining and Biological signal processing. References [1]' TÁBORSKÝ, M. Chronické srdeční selhání. Česká kardiologická společnost, Olomouc (Czech Republic), [2]' VERMA, L., SRIVASTAVA, S., NEGI, P. A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data. J Med Syst, 2016, vol. 40, 7 p. [3]' PODGORELEC, V., KOKOL, P., ROZMAN, I. Knowledge Discovery with Classification Rules in a Cardiovascular Dataset. Comput Methods Programs Biomed. 2005, Dec;80 Suppl 1: P [4]' IKUTA, I., SODICKSON, A., WASSER, E. Enhancing Patient Safety through Automated Data Mining of Nuclear Medicine Reports for Quality Assurance and Organ Dose Monitoring. Radiology, 2012, vol. 263, 8 p. [5]' KADI, I., IDRIA, A. Knowledge Discovery in Cardiology: a Systematic Literature Review. International Journal of Medical Informatics [6]' BANDYOPADHYAY, S., WOLFSON, J., VOCK, D. Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data. Journal of Data Mining and Knowledge Discovery, 2015, vol. 29. [7]' HÁJEK, P., HAVRÁNEK, T. Mechanizing Hypothesis Formation: Mathematical Foundations for a General Theory Berlin: Springer-Verlag, 1978, XV, 396 p. [8]' RAUCH, J., ŠIMŮNEK, M. Dobývání znalostí z databází, LISp- Miner a GUHA. 2014, Praha: Oeconomica, 461 p. [9]' BERKA P. Practical Aspects of Data Mining Using LISp-Miner. Computing & Informatics Jun 1;35(3). About Authors... Ondřej KLEMPÍŘ was born in Šumperk in He obtained his bachelor degree in Biomedical Informatics at Czech Technical University, Prague in 2014 and a master degree in Methods and Appliances for Biomedicine at the same university in He is now a PhD Student at the Department of Biomedical Informatics. His research interests include Data science and Neuroinformatics.