Big Data Analytics in Healthcare Temporal Clinical Events Patterns Mining Svetla Boytcheva 1, Galia Angelova 1, Zhivko Angelov 2, Dimitar Tcharaktchiev 3 1 Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria 2 Adiss Lab Ltd., Sofia, Bulgaria 3 Medical University Sofia, University Specialised Hospital for Active Treatment of Endocrinology, Bulgaria Emails:svetla.boytcheva@gmail.com, galia@lml.bas.bg, angelov@adiss bg.com dimitardt@gmail.com Keywords: Medical Informatics, Big Data, Text mining, Temporal Information, Data mining, MLCS. 1. Motivation Temporal events relations analysis of outpatient records has higher importance for proving different hypothesis: for risk factors analysis, treatment effect assessment, comparative analysis of treatment with different medications and dosage; monitoring of disease complications. Currently such analysis is also used in epidemiology for identifying complex relations between different unrelated diseases so called comorbidity and for research of rare diseases. A lot of efforts were reported in the area of electronic health records visualization and analysis of periodical data for single patient or searching patterns for a cohort of patients [4, 5, 14, 17, 19, 27]. In [19] is proposed method for temporal event matrix representation and learning framework that discovers complex latent event patterns or diabetes mellitus complications. Patnaik et al [4,5] report one of the first attempts for mining patients history data in Big data scope processing over 1.6 million of patient histories. They demonstrate a system called EMRView for mining the precedence relationships to identify and visualize partial order information. In the later researched are addressed three tasks: mining parallel episodes, tracking serial extensions, and learning partial orders. The task of searching for patterns in sequences of events is one of the fundamental tasks in computer science and dynamic programming, as well as its version the problem for searching of the longest common subsequence (LCS) of two strings [20, 21, 25]. This task is important for medical informatics, because in bioinformatics is used for multi omics data analysis [24, 28]. It has also a lot of other applications like data compression, file comparison, syntactic pattern recognition, and etc. Unfortunately the classical algorithms are mainly developed for two strings only and some generalizations are considered for 3 or more strings. The problem for searching of the LCS of two strings has a solution with time complexity [25], where and are lengths of both strings. Hunt and Szymanski [21] improve the solution with time complexity where the length of both strings is and is the number of possible common pairs of symbols in them. Masek and Peterson [30] present solution inspired by four Russians method for speeding up algorithms and report complexity. Recently several slight improvements of LCS were reported for some specific applications in bioinformatics [23]. The multiple longest common subsequence (MLCS) problem is NP complete task. Wang et al [15] propose a parallel algorithm based on dominant points approach for solving MLSC problem, which limits the search for small subset of dominant points rather that searching the whole set. Chen et al [23] describe a method based on successor tables, in 2010 Wang et 1
al [16] explore different heuristics in A* search model for solving MLSC problem. Yang et al [12] use beam search model and propose anytime algorithm which can solve the problem in reasonable time for massive data, but also addressing such issues like memory constraints for data tabulation. Han et al [29] propose one of the fundamental approaches for searching frequent patterns in time series based on statistical methods and construction of extended prefix tree frequent patterns tree (FP tree). Karthik et al [22] study periodicity in sequences using methods based on FP tree. In temporal events frequent pattern mining is important to filter events with similar importance and features, this relationship can be specified by temporal constraints There are different mining approaches, for instance we can interest in sequences leading to certain target event [6]. Temporal events frequent pattern mining is a major task in temporal data mining with several applications in telecommunications, social networks, climate changes prediction, earthquake prediction and etc. Gyet and Quiniou propose and recursive depthfirst algorithm QTIPrefixSpan that explores the extensions of a temporal patterns. They continue their research in the area of for extracting temporal sequences with quantitative temporal intervals with different models using hyper cube presentation and develop a version of EM algorithm for candidates generation [8]. Patnaik et al present the streaming algorithm for mining frequent episodes over a window of recent events in the stream [5]. Monroe et al [18] presents a systems that allows the user by using visual tools to narrow iterative the process for mining patterns to the desired target with application in electronic health records (EHR). Yang et al [10] describes another application of temporal events sequence mining in medical informatics for mining patient histories. They develop a modelbased method for discovering common progression stages in general event sequences. 2. Project Setup The main goal of our research is to examine comorbidity of diseases and their relationship/causality with different treatment, i.e. how the treatment of a disease can affect the co existence of other diseases. This is quite challenging task, because the number of diagnosis (more than 10,000) and of medications (approx. 6,500) is huge. Thus the possible variations of diagnosis and the corresponding treatment are above 10 500 for one patient for one year period. That is why we will examine separately chronic vs acute diseases [9] and afterword the patterns will be combined. Chronic diseases constitute a major cause of mortality according to the World Health Organization reports and their study is with higher importance for medicine. In order to solve this task we split it down into several subtasks: to find the most frequent patterns in chronic diseases; for each frequent pattern to find patterns of treatment and explore their relationship with the periodic occurrence of various acute conditions. Since the task is to apply the method to big data some scarifications is necessary to be made. We propose method based on FP trees and dominant points, mining separately candidate frequent patterns for prefixes and suffixes of dominant points. We explore different approaches for assigning scores for dominant points and candidate generation and compare the results for time and space in all versions of the algorithm. We apply the proposed algorithm for time sequence mining with qualitative and quantitative time intervals. In the first case we use only the order of the events, and in the second version we take into account also the distance between different consecutive events. 2
3. Materials We deal with a repository of anonymous outpatient records (AOR) provided by the Bulgarian National Health Insurance Fund (NHIF) in XML format in Bulgarian language. The majority of data necessary for health management are structured in XML tags, but still there are some fields that contain in free text formats important explanations about the patient Anamnesis, Status, Clinical examinations and Therapy. From XML tags we extract (Error! Reference source not found.) Patient ID, code of doctors medical specialty from 00 to 56 (SimpCode), region of practice (RZOK). Date/Time and ID of the outpatient record (NoAL). XML tags also contain information for the main Figure 1 Structured Event Data diagnose and additional diagnoses with their codes according to the International Classification of Diseases, 10th Revision (ICD 10) [3]. For treatment information extraction we use Text mining tool, because drugs, dosage, frequency and route, mainly included in the Therapy section but sometimes the Anamnesis contains sentences that discuss the current or previous treatment. We developed a drug extractor, based on text mining algorithms using regular expressions to describe linguistic patterns [2]. There are more than 80 different patterns for matching text units to ATC drug names/codes [1] and NHIF drug codes, medication name, dosage and frequency. Currently, the extractor is elaborated and handles 2,239 drug names included in the NHIF nomenclatures. Error! Reference source not found. presents the three collections of clinical texts used for training and tests in our experiments, containing data for patients suffering from Schizophrenia (ICD 10 code F20), Hyperprolactinaemia (ICD 10 code E22.1), and Diabetes Mellitus (ICD 10 codes E10 E15). Table 1 Clinical Text Collections Set Outpatient Records Patients Avg AOR per Patient per year Diagnose ICD 10 Period Size (GB) S1 1,682,429 114,945 4.879 F20 3 years 4 S2 288,977 9,777 6.744 E22.1 3 years 1 S3 6,327,503 435,953 14.514 E10 E15 1 year 18 4. Problem definition Each collection is processed independently from the other two collections. For each collection all extracted events are stored in comma separated format. For collection the set of all different patients is extracted. For each patient events sequence of tuples is generated:. where. The empty sequence of events will be denoted as the empty set 3
We define the set of all possible event sequences in each collection Then from diagnosis sequence of tuples is extracted, and A sequence is said to be event subsequence of the event sequence if there exists sequence such that the times and events, match. It will be denote by. Let is event subsequence and The cardinality is called the support of event subsequence in, i.e.. Length of event sequence is the number of tuples in it. It will be denote by, i.e.. Frequent pattern (FP) in the set is such subsequence that has support above the given threshold, i.e.. Multiple longest common subsequence (MLCS) of the set is such subsequence that satisfies all of the following conditions: (i) is a frequent pattern of ; (ii), is a frequent pattern of,. Sequence split by event of the sequence generates two subsequences: prefix subsequence and sufix subsequence for,and. We define set of all events in the set :, respectively we define the set of all diagnoses in the : References 1) Anatomical Therapeutic Chemical (ATC) Classification System, http://atc.thedrugsinfo.com/ 2) Svetla Boytcheva. Shallow Medication Extraction from Hospital Patient Records. In: Koutkias, V., J. Nies, S. Jensen, N. Maglaveras, and R. Beuscart (Eds.), Studies in Health Technology and Informatics, Vol. 166, IOS Press, pp. 119 128. 3) International Classification of Diseases and Related Health Problems 10th Revision. http://apps.who.int/classifications/icd10/browse/2015/en 4) Debprakash Patnaik, Laxmi Parida, Patrick Butler, Benjamin J. Keller, Naren Ramakrishnan, David A. Hanauer. Experiences with Mining Temporal Event Sequences from Electronic Medical 4
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