Post-correction of OCR Errors Using PyEnchant Spelling Suggestions Selected Through a Modified Needleman–Wunsch Algorithm

Cappelatti, Ewerton; Heidrich, Regina de Oliveira; Oliveira, Ricardo Augusto Rabelo; Monticelli, Cintia; Rodrigues, Ronaldo; Goulart, Rodrigo; Velho, Eduardo

doi:10.1007/978-3-319-92270-6_1

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…The Python PyEnchant package was then used to generate a list of all abbreviations present in the processed diagnosis statements. 34 A pair of clinical experts manually generated a dictionary containing each abbreviation and its expanded form for the 100 most common abbreviations (Table S2). Each instance of these abbreviations in the diagnosis statement was then replaced with its expansion.…”

Section: Diagnostic Statement Preprocessing: Cardiologist-confirmed D...mentioning

confidence: 99%

Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care

Khunte,

Sangha,

Oikonomou

et al. 2024

Preprint

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Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows rely on a computerized ECG interpretation using rule-based tools built into the ECG signal acquisition systems with limited accuracy and flexibility. In low-resource settings, specialists must review every single ECG for such decisions, as these computerized interpretations are not available. Additionally, high-quality interpretations are even more essential in such low-resource settings as there is a higher burden of accuracy for automated reads when access to experts is limited. Artificial Intelligence (AI)-based systems have the prospect of greater accuracy yet are frequently limited to a narrow range of conditions and do not replicate the full diagnostic range. Moreover, these models often require raw signal data, which are unavailable to physicians and necessitate costly technical integrations that are currently limited. To overcome these challenges, we developed and validated a format-independent vision encoder-decoder model - ECG-GPT - that can generate free-text, expert-level diagnosis statements directly from ECG images. The model shows robust performance, validated on 2.6 million ECGs across 6 geographically distinct health settings: (1) 2 large and diverse US health systems- Yale-New Haven and Mount Sinai Health Systems, (2) a consecutive ECG dataset from a central ECG repository from Minas Gerais, Brazil, (3) the prospective cohort study, UK Biobank, (4) a Germany-based, publicly available repository, PTB-XL, and (5) a community hospital in Missouri. The model demonstrated consistently high performance (AUROC≤0.81) across a wide range of rhythm and conduction disorders. This can be easily accessed via a web-based application capable of receiving ECG images and represents a scalable and accessible strategy for generating accurate, expert-level reports from images of ECGs, enabling accurate triage of patients globally, especially in low-resource settings.

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Section: Diagnostic Statement Preprocessing: Cardiologist-confirmed D...mentioning

confidence: 99%

Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care

Khunte,

Sangha,

Oikonomou

et al. 2024

Preprint

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“…This implementation only returns the optimal alignment score using the SW algorithm. Cappelatti et al 54 presented an approach to improve the results of OCR using the modified NW algorithm. Authors used customized scoring scheme based on the frequency and occurrence of letters.…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

A scalable parallel algorithm for global sequence alignment with customizable scoring scheme

Sadiq,

Yousaf

2023

Concurrency and Computation

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SummarySequence alignment is a critical computational problem in various domains, including genomics, proteomics, and natural language processing. The Needleman‐Wunsch (NW) algorithm is a classical dynamic programming approach for finding the optimal global alignment between two sequences. However, its quadratic time and space complexity make it impractical for aligning large‐scale sequences, which are increasingly common in modern applications. In this article, we propose a parallel variation of the NW algorithm that enables scalable global sequence alignment with customizable scoring schemes. Our approach re‐formulates the dependencies in the NW algorithm to enable parallel execution, thereby leveraging the computational power of modern parallel architectures, such as graphics processing unit (GPU). Furthermore, our algorithm supports arbitrary linear scoring schemes, which allows us to use domain‐specific knowledge to improve alignment accuracy. We establish the correctness of our algorithm and evaluate its performance using real DNA and user trajectory sequences on GPUs. Our parallel algorithm has shown impressive results in our experiments, with a peak performance of 27.99 GCUPS (giga cell updates per second) and a maximum speedup of 48.18 times compared to the traditional sequential implementation. Additionally, our algorithm demonstrates remarkable scalability, enabling the alignment of sequences of any length while ensuring balanced work distribution and optimal utilization of resources. Our primary objective is to harness the computational capabilities of a single GPU and fully utilize the processing power of multi‐core CPUs.

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Generating Correction Candidates for OCR Errors using BERT Language Model and FastText SubWord Embeddings

Hajiali

Cacho

Taghva

2021

Lecture Notes in Networks and Systems

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Post-correction of OCR Errors Using PyEnchant Spelling Suggestions Selected Through a Modified Needleman–Wunsch Algorithm

Cited by 4 publications

References 4 publications

Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care

Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care

A scalable parallel algorithm for global sequence alignment with customizable scoring scheme

Generating Correction Candidates for OCR Errors using BERT Language Model and FastText SubWord Embeddings

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