Identifying Anatomical Phrases in Clinical Reports by Shallow Semantic Parsing Methods

The current manual reading of substation pointer meters wastes human resources, and existing algorithms have limitations in accuracy and robustness for detecting various pointer meters. This paper proposes a method for recognizing pointer meters based on Yolov7 and Hough transform to improve their automatic readability. The proposed method consists of three main contributions: (1) Using Yolov7 object detection technology, which is the latest Yolo technology, to enhance instrument recognition accuracy. (2) Providing a formula for calculating the angle of a square pointer meter after Hough transformation. (3) Applying OCR recognition to the instrument dial to obtain the model and scale value. This information helps differentiate between meter models and determine the measuring range. Test results demonstrate that the proposed algorithm achieves high accuracy and robustness in detecting different types and ranges of instruments. The map of the Yolov7 model on the instrument dataset is as high as 99.8%. Additionally, the accuracy of pointer readings obtained using this method exceeds 95%, indicating promising applications for a wide range of scenarios.

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“…The Hmean is the harmonic mean of precision and recall. Their calculation formulas are in Formulas ( 14)-( 16) [41]:…”

Section: Ocr Evaluation Indexmentioning

confidence: 99%

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

et al. 2023

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“…Examples of techniques often used in such tasks include the use of numeric patterns and attribute labels to capture diabetes metrics, 19 regular expressions for identifying blood pressure and antihypertensive treatment intensification, 20 and statistical machine-learning techniques such as support vector machines and entropy-based approaches to identify key clinical findings. 21,22 The level of customization involved in such techniques can lead to high levels of performance, but can also result in systems that do not port well across medical subdomains. A third approach that has recently gained traction in the medical informatics community is the development of hybrid systems capable of applying several different extraction and natural language processing approaches, often in combination, to abstract targeted clinical information.…”

Section: Clinical Information Extractionmentioning

confidence: 99%

“…SVMs have been used in the clinical domain for several NLP-related tasks including document classification 32 and complex concept identification in radiology reports. 22 To train the classifier, automatically extracted margin sentences were taken from a separate random sample of 782 pathology reports from the combined BWH/ UCLA collection. A total of 851 extracted sentences in the training set were manually categorized by the author (LWD) into one of the three categories: positive, negative, and not applicable.…”

Section: Gleason Score Tumor Stage and Margin Status Extractionmentioning

confidence: 99%

Facilitating Clinical Outcomes Assessment through the Automated Identification of Quality Measures for Prostate Cancer Surgery

D’Avolio

Litwin

Rogers

et al. 2008

Journal of the American Medical Informatics Association

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“…In cases in which targeted diagnoses appear within narrative free text, statistical approaches such as support vector machines and entropybased techniques have proven useful. 21,22 In attempting to extract values that appear in predictable patterns, such as patient demographics and certain laboratory results, researchers have had success in using predetermined patterns of strings or regular expressions. 23,24 In several cases, robust rules and grammar-based natural language processing (NLP) systems such as MedLEE 25 and the National Library of Medicine's MetaMap Transfer (MMTx) 26 have been used as the foundation for clinical information extraction efforts.…”

Section: Introductionmentioning

confidence: 99%

The Clinical Outcomes Assessment Toolkit: A Framework to Support Automated Clinical Records-based Outcomes Assessment and Performance Measurement Research

D’Avolio

Bui

2008

Journal of the American Medical Informatics Association

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The Clinical Outcomes Assessment Toolkit (COAT) was created through a collaboration between the University of California, Los Angeles and Brigham and Women's Hospital to address the challenge of gathering, formatting, and abstracting data for clinical outcomes and performance measurement research. COAT provides a framework for the development of information pipelines to transform clinical data from its original structured, semi-structured, and unstructured forms to a standardized format amenable to statistical analysis. This system includes a collection of clinical data structures, reusable utilities for information analysis and transformation, and a graphical user interface through which pipelines can be controlled and their results audited by nontechnical users. The COAT architecture is presented, as well as two case studies of current implementations in the domain of prostate cancer outcomes assessment.

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Identifying Anatomical Phrases in Clinical Reports by Shallow Semantic Parsing Methods

Cited by 4 publications

References 19 publications

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

Facilitating Clinical Outcomes Assessment through the Automated Identification of Quality Measures for Prostate Cancer Surgery

The Clinical Outcomes Assessment Toolkit: A Framework to Support Automated Clinical Records-based Outcomes Assessment and Performance Measurement Research

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