Digitization of health records in rural villages

Vaish, Rajan; Ishikawa, Sascha T.; Liu, Jing; Berkey, Stephanie C.; Strong, Philip; Davis, James

doi:10.1109/ghtc.2013.6713682

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Data that are meant for a single cell may be written outside the cell's border, individual symbols within the cells may or may not overlap (e.g. print vs. cursive handwriting) and different agents may utilize different fonts [10], ink colors, or emphasize a special status of data by circling, drawing arrows, underlining or crossing out entries [11]. Many of these specific challenges, such as handwriting translation or object recognition under non-uniform illumination, are areas of research in their own right.…”

Section: Introductionmentioning

confidence: 99%

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An open-source tool for the transcription of paper-spreadsheet data: Code and supplemental materials available online: Https://github.com/deskool/images to spreadsheets

Ghassemi¹,

Jarvis²,

Alhanai³

et al. 2017

2017 IEEE International Conference on Big Data (Big Data)

165

116

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Clinical researchers, historians, educators and field researchers alike still regularly capture data on paper spreadsheets. In the case of health care and education, data will often contain sensitive personal information, further complicating the process of transcribing paper-based archives into digital form. In this work, we describe a tool that utilizes machine learning and crowd intelligence to automatically transcribe images of paper-based spreadsheets into electronic form while protecting sensitive personal information. Our solution consists of four high-level stages: (1) the extraction of cell-level images from the spreadsheet grid, (2) machine recognition of digits within the cells, (3) human transcription of cell contents that the machine was uncertain of and (4) feedback of human transcription results to the machine to improve future classification performance. We test the algorithm on a novel data-set of 135 heterogeneous clinical flow-sheet images collected from the Massachusetts General Hospital (MGH), 2 hand-drawn spreadsheets, one chalkboard drawing, and one printed table. we demonstrate that our algorithm provides a generalized solution for spreadsheet transcription that maintains privacy, is up to 10 times faster and twice as cost effective than existing alternatives. Our work is valuable both as a tool and as a starting point for the development of better algorithms.

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Section: Introductionmentioning

confidence: 99%

“…In 2014, Vaish et al performed a more targeted study comparing human transcription strategies for the digitization of medical records. The authors reported that although crowd-sourced transcription was less expensive than its alternatives, it was also less accurate [11].…”

Section: Introductionmentioning

confidence: 99%

An open-source tool for the transcription of paper-spreadsheet data: Code and supplemental materials available online: Https://github.com/deskool/images to spreadsheets

Ghassemi¹,

Jarvis²,

Alhanai³

et al. 2017

2017 IEEE International Conference on Big Data (Big Data)

165

116

View full text Add to dashboard Cite

show abstract

“…Bu tarz sistemler eğitilmiş veriler üzerinde çalıştığından, eğitim veri setinde var olmayan bir yazı tipine rastlanılması durumunda başarılı sonuçlar elde edilememektedir. Bu nedenle eğitim verileri çeşitli yazı tiplerini tanıyacak şekilde hazırlanmalıdır [14], [15]. Geliştirmiş olduğumuz sistemde tüm başlıklarda "Cambria" yazı tipi kullanılmıştır.…”

Section: Form Tasarımıunclassified