Similarity Analysis of Patients Data: Bangladesh Perspective
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1 Bangladesh University of Engineering and Technology From the SelectedWorks of Shahidul Islam Khan December 17, 2016 Similarity Analysis of Patients Data: Bangladesh Perspective Shahidul Islam Khan, Bangladesh University of Engineering and Technology Abu Sayed Md. Latiful Hoque, Bangladesh University of Engineering and Technology Available at:
2 International Conference on Medical Engineering, Health Informatics and Technology (MediTec 2016) Similarity Analysis of Patients Data: Bangladesh Perspective Shahidul Islam Khan, Abu Sayed Md. Latiful Hoque Department of Computer Science and Engineering (CSE) Bangladesh University of Engineering and Technology (BUET) Dhaka, Bangladesh Abstract Misspelling of names is a major problem of real world datasets and a single person is identified differently as its consequence. In Bangladesh, it is common that many people, in real, do not know their full name and many of Bangladeshi citizens are unable to pronounce their name correctly, even in the mother tongue. The Same person provides a different version of their name during taking a public service e.g., treatment in hospital. In almost all healthcare centers, a patient is asked and he reports his demographic data i.e. name, age, etc. orally. This creates ambiguity with misspelled names. In this paper, we have provided an algorithm to identify the same person correctly from the variation of names. Experimental results show that our proposed technique can successfully link records with high accuracy for noisy data like misspelled patient names etc. Keywords Record Linkage; Name; Bangladesh; Phonetic Analysis; Health Data; I. INTRODUCTION Data and information have changed our lives and society. Nowadays around the world tremendous amount of data are collected from different aspects. Attitudes of peoples and instruments are also documented. Lots of hidden knowledge are waiting to be discovered from these data. This is the challenge of big data era. Record linkage (also known as identity resolution, data matching, etc.) refers to the task of finding records in a data set refer to the same entity from various data sources such as computer databases, data files, books, and websites. This linkage or data matching is essential when joining datasets based on entities that may or may not share a common identifier such as passport number, health card number, insurance number, national identity, smart card, or social security number [1] - [3]. Name of a person plays an important role in identifying the person and genealogical investigation. On contrary, name disparity may be a prime predicament for identifying and searching for people such as web search, security, health research etc. Variations in names create great difficulties in identifying people as it is not easy to resolve whether a name deviation is a different spelling of the same name or a name for a different person. Variations can be categorized primarily as a character, spelling, and phonetic variation. There are nearly 160 million people live in Bangladesh and 230 million people speak in Bengali in the world. Names of Bangladeshi persons have characteristics different from European or American names. So separate algorithms should be developed to address name matching problem of Bangladeshi citizen. In this paper, we propose an algorithm that can analyze the similarities among Bangladeshi names. Experimental results show that our presented algorithm can successfully identify the similarity of patients names in the presence of typical practical noise e.g., misspelled names. For a noisy health dataset of patient records, we achieved 87% correct name matching. II. A MOTIVATIONAL EXAMPLE Bangladesh government took an initiative to develop National Health Data Warehouse (NHDW) in 2009 with the help of German Donor GIZ. The objective of the warehouse is to build an electronic data repository which bridged the gaps between the various available digital health recordsets and made them interoperable. Currently, medical data from different healthcare organizations under Directorate General of Health Services (DGHS) of Bangladesh Government are being collected through two open source software: DHIS2 and OpenMRS. [4]- [7]. A block diagram of the overall system is depicted in Fig. 1. Fig. 1. Block Diagram of National Health Data Cloud This research is supported by the ICT Division, Ministry of Posts, Telecommunications and Information Technology, Government of the People's Republic of Bangladesh. Author /16/$ Version IEEE
3 For exaction of fruitful knowledge from health data, it is the first requirement to accumulate health records from widely variable sources. While accumulation, these records cannot be mapped with the patients because of storing patient records every time with different identities. Record linkage problem is to find an optimum reliable mapping of each patient to his/her health record throughout the lifespan. Based on the patient cycles as described in [8], different cases arise in the context of Bangladesh. A patient may visit a hospital, diagnostic center, or personal chamber of a doctor for treatment or diagnosis. We have to find out, how many possible records will be produced in the lifetime of a patient in Bangladesh. Let THL is the total health records for the lifetime of a single patient. We can estimate THL as follows: Let life span of a person = y years Average visit to any health care facility per year= m Total health care visits in life span of a person T= ym Average Life expectancy in Bangladesh: Male-70 years and Female-72 years [9]. If we consider y=71 and m=20/year So T= 20x71=1420 times If one visit creates at least two records, THL 2840 Medical records of a person are stored either in electronic form or hard copy format in Bangladesh. Approximately 2840 different records of the same person are stored with thousands of different identities. These healthcare records of the patients are highly distributed in terms of time (e.g., doing pathological tests in different times), space (e.g., outdoor, indoor or lab), and locations (e.g., different hospitals, clinics, diagnostic centers). In many cases, the same patient is registered with different names in different or even in the same health center. III. PROBLEM DEFINITION Many people in Bangladesh do not know their full name and unable to pronounce their name correctly even in the mother tongue. The Same person provides a different version of their name in the health care facilities or other service providing places. The problem can be understood from the Table I below. TABLE I. Actual Patient Name Sajib Mazumdar DIFFERENT NAMES ARE STORED AGAINST SAME PATIENT S HEALTH RECORD Inputted Name Sajib Majumdar Mr. Sajib Mazumdar Md. Sajib Mazumdar Sajib Mozumder Shajib Mozumdar Mr. Sajib Md. Sajeeb So same person is identified as the different person in different records. So integrating these types of records cannot be useful for proper knowledge discovery. IV. RELATED ALGORITHMS There are several algorithms available in the literature for phonetic matching of names. They have been developed in different contexts and for different goals. Some important algorithms are briefly presented below. Levenshtein algorithm: The principle of this algorithm is based on alphabetic techniques of edit distance metrics depicted by Levenshtein [10]. The least possible number of insertions, deletions or substitutions that are needed to transform one word into the other is defined as the Levenshtein distance between two words. Guth algorithm: It first tries for the situation that two names are same, by considering individual name as a distinct string. If this process fails, the algorithm proceeds to examine in contrast the surnames character by character. When the algorithm finds different letters in the same position, it then searches for matching characters in other positions [11]. The Guth method is left to right sequence driven and free from language and ethnic problems. It is easy to code and also provides reliable results. In comparing short names, the algorithm seems feeble. Soundex algorithm: The algorithm, presented by Odel and Russell [12], is a commonly used phonetic matching technique. It is also adapted for some other languages. It is used, directly or after modification, in popular commercially used database management systems and other software. Metaphone algorithm: It is published by Lawrence Philips. Many variations of this algorithm are found such as Double Metaphone or Metaphone 3. The method implemented assumes English phonetics but works equally well for forenames and surnames [13]. NYSIIS algorithm: It is an easy to implement algorithm that produces a canonical index code as the output. The difference between NYSIIS and Soundex is that NYSIIS retains information of the position of vowels of the inputted name in the encoded word by converting all vowels to the character A [14]. NYSIIS returns a purely alphabetic code. It has been adapted in various record linkage research and also used in the pre-processing step of entity resolution system. Phonex algorithm: It is basically a combination of Soundex and Metaphone algorithms. The Phonex is demonstrated to show a better overall performance when it is applied to the English language names [15]. The above-mentioned algorithms were developed basically for European or American names and that s why they are not suitable for Bangladeshi names. So separate algorithms should be developed to address name matching problem of Bangladeshi citizen.
4 Zaman and Khan proposed a partial algorithm to for phonetic matching of names written in the Bengali language [16], [17]. Their encoding stands on the Soundex algorithm, which is modified to match Bengali phonetics. The motivation of their work was to support spell checkers to offer better recommendations for misspelled words. However, they only discuss theoretical aspects and did not provide any implementation details. To the best of our knowledge, our presented algorithm is the first of its kind in the context of Bangladesh. It considers the characteristics of Bangladeshi names in the English language for wider usability which is presented in the next section. V. PROPOSED ALGORITHM: NAMESIGNIFICANCE NameSignificance is the masked string produced by our developed NameSig Algorithm. NameSig finds significant and unambiguous letters contained in a person s name as its result. NameSig treats different salutations and titles contained in a name (e.g., Mr., Mrs., Dr., Md., Capt., Advocate, Miss, Ms., Sree, etc.) as insignificant. In the practical situations at maximum healthcare centers, a patient is asked and he reports his demographic data i.e. name, age, etc. orally. From pronouncing a patient name to write it up, vowels are extremely ambiguous. A vowel can be written in many ways. We can see (Table I) that doctors or computer operator can write or entry a patient in six or more different ways. To remove ambiguity, vowels are ignored from the significant part of a name, if they are not appeared at the beginning of the name or after white space. The letters with similar pronunciation such as g, j, z or k, q, c are treated with the same value to reduce the effect of a typing error. After that, the unambiguous significant portion of the name is masked so that real name of a patient cannot be understood by any health data warehouse users. A predefined code table is used for data masking. The process of NameSignificance generation is shown in Table II. Our algorithm NameSig is presented below: Input: Name of a patient Output: NameSignificance code of the inputted name Steps: 1. Delete Title and Salutation 2. Delete a/a, e/e, i/i, o/o, u/u unless beginning of name or after whitespace 3. Delete whitespace 4. Convert g/g/j/j/z/z to g 5. Convert k/k/q/q/c/c to k 6. Mask unambiguous and significant characters using Code Table TABLE II. Patient Name Mr. Abdul Haque Mr. Md. Abdul Hoque Mohammad Abdul Haq Mr. Sobuj Sree Sobuz Sree Sabuj SELECTION OF SIGNIFICANT, UNAMBIGUOUS NAMESIGNIFICANCE Significant portion Unambiguous significant portion abdl hq Abdul tafsemi Haque Abdul abdl hq tafsemi Hoque Abdul Haq abdl hq tafsemi Sobuj sbj sh malsem Sobuz sbj sh malsem Sabuj sbj sh malsem VI. DATA COLLECTION Masked NameSignificance For the validation of our proposed algorithm, we have used real healthcare dataset consisting of patient records. Among these records, records contain a null, 0, 00, / in the patient phone/mobile attribute. 6173records contains invalid strings in phone/mobile attribute. Date of Birth or Age of records are missing (see Fig. 2). Fig. 2. Quality of test dataset VII. RESULTS AND DISCUSSION We have implemented the NameSig algorithm in java and run in an HP brand computer of our graduate research lab. The Computer has Intel core i GHZ Processor and 4 Gigabyte RAM. The operating system of the machine is Windows 10 Pro. The simulation was run ten times. The program processed the dataset of patients in an average of 7 Seconds and 60 milliseconds. Input file size was 24.1MB and output file size was 14.4MB. It was found that for each run our system generates the same NameSignificance for the same patient. In 87% cases, patient names are long enough to decide uniqueness. NameSignificance generated by NameSig algorithm, for 13% records, were insignificant and could not be used for proper record linkage (Fig. 3). This is because the presence of noise in practical healthcare data e.g., the name of a child is written as baby, or baby of X ; or date of birth is absent etc.
5 Fig. 3. Performance of NameSignificance in presence of noisy data To verify whether NameSignificance can identify same patients from the integration of different healthcare data, we have used a training dataset of hundred-ten patient data where there were seventy unique patients data. This dataset was complete that is it did not contain any missing data, false data, and also proper attribute formats were used. The data repetition status (multiple records of same patients) was shown in Table III: We have inputted the dataset to our system and found that the system generated 70 unique NameSignificance with 100% accuracy. That is, every patient was identified correctly and the same patient with multiple records was also tracked by our system. From this result, we can easily understand the importance of noise-free data. Complete and good quality data is very important because utilization of this data will improve the service delivery. Policy makers can be benefited by using quality data for taking better decisions. For future data collection, data entry personnel at the lowest level (e.g., Upazila/Union/Ward) should be given more training. We also found that our system generated the same key for same patients with slightly misspelled named and different registration number. That is, the system is capable of handling small typing errors. Overall system performance with the real dataset is presented in Fig. 4 and Fig. 5. Repetition occurs when different patients have the same name. For example, from Fig. 5 we can see that, our system found 88 different health records of the single patient among 0.6 million data. TABLE III. ANALYSIS OF UNIQUE PATIENT DATA Number of Repetition No. of Patients No. of Records Same patient with 5 records 1 5 Same patient with 4 records 2 8 Same patient with 3 records 9 27 Same patient with 2 records Patients with single health record Total 110 Fig. 4. NameSignificance from health recordset
6 Fig. 5. NameSignificance repetition scenario VIII. CONCLUSIONS In Bangladesh it common that many people in real do not know their full name and unable to pronounce their name correctly even in the mother tongue. The Same person provides a different version of their name during taking a public service e.g., treatment in a hospital which creates ambiguity. In this paper, we have provided an algorithm NameSignificance. It was seen from experimental results that our developed technique can successfully match names in in the presence of noisy data e.g., misspelled patient names. For a noisy health dataset of patients, we achieved 87% correct NameSignificance. For the complete dataset of 110 patient records, our algorithm achieved 100% accuracy. REFERENCES [1] J. Liang, L. Chen, S. Mehrotra, Efficient Record Linkage in Large Data Sets, In Proc. Eighth International Conference on Database Systems for Advanced Applications (DASFAA), pp IEEE, [2] S. I. Khan and A.S.M.L. Hoque, Development of national health data warehouse Bangladesh: Privacy issues and a practical solution, In Proc. 18th International Conference on Computer and Information Technology (ICCIT), IEEE, [3] S. I. Khan and A.S.M.L. Hoque, Health Data Integration with Secured Record Linkage A Practical Solution for Bangladesh and Other Developing Countries, accepted in 3nd International Conference on Networking systems and Security (NSysS), IEEE, [4] A Quiet Revolution: Strengthening the Routine Health Information System in Bangladesh, published by giz, accessed from [5] S. I. Khan, A.S.M.L. Hoque, M. Ullah, "National Health Data Warehouse Bangladesh for Remote Health Monitoring: Features, Problems and Privacy Issues," Remote Health Monitoring Workshop, [6] S.I. Khan and A.S.M.L. Hoque, Towards development of health data warehouse: Bangladesh perspective, In Proc. 2 nd International Conference on Electrical Engineering and Information Communication Technology (iceeict), IEEE, [7] S. I. Khan and A.S.M.L. Hoque, Privacy and security problems of national health data warehouse: a convenient solution for developing countries, In Proc. 2nd International Conference on Networking systems and Security, IEEE, 2016, pp [8] S. I. Khan and A.S.M.L. Hoque, An Analysis of the Problems for Health Data Integration in Bangladesh, In Proc. International Conference on Innovations in Science, Engineering and Technology (ICISET 2016), IEEE, [9] WHO, Global Health Observatory Data Repository: Life expectancy Data by country, [10] V. I. Levenshtein, Binary codes capable of correcting deletions, insertions and reversals, Sov. Phys. Dokl., vol. 6, pp , [11] G. J. A. Guth, Surname Spellings and Computerised Record Linkage, Historical Methods. Newsletter, vol. 10, no. 1, pp , [12] K. M. Odell and R. C. Russell, Soundex phonetic comparison system [cf. U.S. Patents (1918), (1922)]. [13] A. Binstock and J. Rex, Practical Algorithms for Programmers. Addison-Wesley, Reading, Mass., pp , [14] A. J. Lait and B. Randell, An Assessment of Name Matching Algorithm, Society of Indexers Genealogical Group, Newsletter Contents, SIGGNL issues 17, [15] C. Snae and B. M. Diaz, Name Matching for Linkage Among English Parish Register Records, in Proc. of the Human and Computer Conf, pp , Japan, [16] N. Zaman and M. Khan, A Bangla phonetic encoding for better spelling suggesions, Brac University, [17] N. Zaman and M. Khan, A double metaphone encoding for Bangla and its application in spelling checker, 2005 International Conference on Natural Language Processing and Knowledge Engineering, pp
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