Name matching is a key component in various applications in our life like record linkage and data mining applications. This process suffers from multiple complexities such as matching data from different languages or data written by people from different cultures. In this paper, we present a new modified Cross-Language Levenshtein Distance (CLLD) algorithm that supports matching names across different writing scripts and with many-to-many characters mapping. In addition, we present a hybrid cross-language name matching technique that uses phonetic matching technique mixed with our proposed CLLD algorithm to improve the overall f-measure and speed up the matching process. Our experiments demonstrate that this method substantially outperforms a number of well-known standard phonetic and approximate string similarity methods in terms of precision, recall, and f-measure.

A hybrid cross-language name matching technique using novel modified Levenshtein Distance

We present an algorithm to generate unique identifiers to be used for patient identification in the Electronic Health Record (EHR) system for India. A robust EHR system is essential for ubiquitous provision of technology-enabled health-care services. Conventional EHR systems use identifiers such as Social Security Numbers to identify patients. However, in developing nations, adoption of such systems are in its infancy, leading towards the need of an independent identifier generation mechanism to enable patients without conventional IDs to register and avail health-care services. In this paper, we present an approach to generate Unique Health IDentifiers (UHID) for patients to register with EHR systems in the Indian scenario. The approach uses a combination of patient attributes to generate the identifier, and is non-obvious to a naive user. It is capable of generating upto 46; 656 unique identifiers for similar attributes, and offers the added advantage of efficient duplicate detection by using Soundex codes. The effectiveness of the proposed approach has been benchmarked on an actual patient dataset of 1; 992; 102 records, drawn from C-DAC's Hospital Management Information System (HMIS).

Unique Health Identifier for India: An algorithm and feasibility analysis on patient data

Background: A universal health care identifier (UHID) facilitates the development of longitudinal medical records in health care settings where follow up and tracking of persons across health care sectors are needed. HIV case-based surveillance (CBS) entails longitudinal follow up of HIV cases from diagnosis, linkage to care and treatment, and is recommended for second generation HIV surveillance. In the absence of a UHID, records matching, linking, and deduplication may be done using score-based persons matching algorithms. We present a stepwise process of score-based persons matching algorithms based on demographic data to improve HIV CBS and other longitudinal data systems. Objective: The aim of this study is to compare deterministic and score-based persons matching algorithms in records linkage and matching using demographic data in settings without a UHID. Methods: We used HIV CBS pilot data from 124 facilities in 2 high HIV-burden counties (Siaya and Kisumu) in western Kenya. For efficient processing, data were grouped into 3 scenarios within (1) HIV testing services (HTS), (2) HTS-care, and (3) within care. In deterministic matching, we directly compared identifiers and pseudo-identifiers from medical records to determine matches. We used R stringdist package for Jaro, Jaro-Winkler score-based matching and Levenshtein, and Damerau-Levenshtein string edit distance calculation methods. For the Jaro-Winkler method, we used a penalty (р)=0.1 and applied 4 weights (ω) to Levenshtein and Damerau-Levenshtein: deletion ω=0.8, insertion ω=0.8, substitutions ω=1, and transposition ω=0.5. Results: We abstracted 12,157 cases of which 4073/12,157 (33.5%) were from HTS, 1091/12,157 (9.0%) from HTS-care, and 6993/12,157 (57.5%) within care. Using the deterministic process 435/12,157 (3.6%) duplicate records were identified, yielding 96.4% (11,722/12,157) unique cases. Overall, of the score-based methods, Jaro-Winkler yielded the most duplicate records (686/12,157, 5.6%) while Jaro yielded the least duplicates (546/12,157, 4.5%), and Levenshtein and Damerau-Levenshtein yielded 4.6% (563/12,157) duplicates. Specifically, duplicate records yielded by method were: (1) Jaro 5.7% (234/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.4% (308/6993) within care, (2) Jaro-Winkler 7.4% (302/4073) within HTS, 0.5% (6/1091) in HTS-care, and 5.4% (378/6993) within care, (3) Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care, and (4) Damerau-Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care. Conclusions: Without deduplication, over reporting occurs across the care and treatment cascade. Jaro-Winkler score-based matching performed the best in identifying matches. A pragmatic estimate of duplicates in health care settings can provide a corrective factor for modeled estimates, for targeting and program planning. We propose that even without a UHID, standard national deduplication and persons-matching algorithm that utilizes demographic data would improve accuracy in monitoring HIV care clinical cascades.

Where No Universal Health Care Identifier Exists: Comparison and Determination of the Utility of Score-Based Persons Matching Algorithms Using Demographic Data.

Differently written words may have similar pronunciations. In computerized textual analysis for English vocabularies, a code mapping is available, accessible in terms of a database function named ‘Soundex’. Depending on pronunciation, codes are generated which are utilized for word suggestion in searching, similarity detection, text mining etc. However, this Soundex is not multilingual and supports English language only. This paper describes designing and development of Soundex for Assamese language.

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Design and Development of Soundex for Assamese Language

Writing a noun word with different spellings leads to inconsistency in the database while retrieving the records. This discrepancy occurs because the retrieval of records is done through exact match of the spelling of words. Thus, to improve this, phonetic algorithms were introduced to retrieve the records from the database where matching is done through phonetics of the word instead of spelling match. In this paper, we have tried to implement the SoundEx phonetic algorithm for Hindi language to retrieve the noun words from the database. Here, we have tried to include all the vowels and consonants of Hindi language which were not included in the earlier version of the similar algorithm.

SoundEx Algorithm Revisited for Indian Language

In a system with a large database, there always has been a problem that names may not be spe lled well or might not be spelled in a way thatone expected. So, data in the database gets degrad ed. In this case it is required to search the duplicates and merge them in the single entity. In doing so, one problem is that the way in which the strings would be compared. In such cases rather than looking for exact match, approximate string matching would be appreciable . One of the string matching techniques isPhonetic matching which is used to compare the name based on the pronunciation of the words. The similar sounding words could be retrieved from the large database using different phonetic matching algorithm and best known algorithm is Soundex algorithm. Phonetic matching is needed when many people from different culture come together. They either spe ak with different pronunciation or their writing habits are different. This scenario is very common in India, as we have many different languages like Hindi, Gujarati, Marathi, Tamil etc. In this research work Soundex algorithm is used for Hindi and Gujara ti language and applied on the names along with their variations in order to retrieve the output with minimum false hits.

Improvement of soundex algorithm for indian language based on phonetic m atching

for names in large databases containing spelling variations has always been a problem. The first solution to the problem was proposed by Robert Russell in 1912 as he proposed SoundEx algorithm. SoundEx algorithm matches the names based on the sound of the words. The technique which is based on the pronunciation of the word is known as phonetic matching. SoundEx algorithm is one of the phonetic matching algorithms. We have discussed the other phonetic matching algorithm like edit distance algorithm, K-String and Q gram algorithm, Guth algorithm, Daitch Mokotoff algorithm and Metaphone coding algorithm. Our main focus is on the SoundEx algorithm and this paper also describes the issues with SoundEx algorithm.

https://research.ijcaonline.org/volume87/number9/pxc3893771.pdf

Rima Shah

Papers

Improvement of soundex algorithm for indian language based on phonetic m atching

Analysis and Comparative Study on Phonetic Matching Techniques