Using experts feedback in clinical case resolution and arbitration as accuracy diagnosis methodology

Rodríguez-González, Alejandro; Torres-Niño, Javier; Valencia‐García, Rafael; Mayer, Miguel Ángel; Alor-Hernández, Giner

doi:10.1016/j.compbiomed.2013.05.003

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…As described by Rodríguez-González [41], a more rigorous evaluation design would use a panel of assessors who are assigned a random subset of questions and a panel of referees who arbitrate among results returned by the assessors and by the system. Our evaluations were formative in nature, designed to demonstrate the feasibility and plausibility of the methods we developed.…”

Section: Discussionmentioning

confidence: 99%

Use of ontology structure and Bayesian models to aid the crowdsourcing of ICD-11 sanctioning rules

Yun

Nyulas

et al. 2017

Journal of Biomedical Informatics

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The International Classification of Diseases (ICD) is the de facto standard international classification for mortality reporting and for many epidemiological, clinical, and financial use cases. The next version of ICD, ICD-11, will be submitted for approval by the World Health Assembly in 2018. Unlike previous versions of ICD, where coders mostly select single codes from pre-enumerated disease and disorder codes, ICD-11 coding will allow extensive use of multiple codes to give more detailed disease descriptions. For example, “severe malignant neoplasms of left breast” may be coded using the combination of a “stem code” (e.g., code for malignant neoplasms of breast) with a variety of “extension codes” (e.g., codes for laterality and severity). The use of multiple codes (a process called post-coordination), while avoiding the pitfall of having to pre-enumerate vast number of possible disease and qualifier combinations, risks the creation of meaningless expressions that combine stem codes with inappropriate qualifiers. To prevent that from happening, “sanctioning rules” that define legal combinations are necessary. In this work, we developed a crowdsourcing method for obtaining sanctioning rules for the post-coordination of concepts in ICD-11. Our method utilized the hierarchical structures in the domain to improve the accuracy of the sanctioning rules and to lower the crowdsourcing cost. We used Bayesian networks to model crowd workers’ skills, the accuracy of their responses, and our confidence in the acquired sanctioning rules. We applied reinforcement learning to develop an agent that constantly adjusted the confidence cutoffs during the crowdsourcing process to maximize the overall quality of sanctioning rules under a fixed budget. Finally, we performed formative evaluations using a skin-disease branch of the draft ICD-11 and demonstrated that the crowd-sourced sanctioning rules replicated those defined by an expert dermatologist with high precision and recall. This work demonstrated that a crowdsourcing approach could offer a reasonably efficient method for generating a first draft of sanctioning rules that subject matter experts could verify and edit, thus relieving them of the tedium and cost of formulating the initial set of rules.

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Section: Discussionmentioning

confidence: 99%

Use of ontology structure and Bayesian models to aid the crowdsourcing of ICD-11 sanctioning rules

Yun

Nyulas

et al. 2017

Journal of Biomedical Informatics

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“…The performance of the final trained CNN model needed to be evaluated by corresponding metrics [ 47 ]. Common evaluation metrics for classification tasks are true positive rate ( TPR ) and false positive rate ( FPR ) [ 48 , 49 ], which have the following equations:

where TP indicates that a positive sample is correctly identified as a positive sample, TN indicates that a negative sample is correctly identified as a negative sample, FP indicates a false positive sample (which means that a negative sample is incorrectly identified as a positive sample), and FN indicates a false negative sample (which means that a positive sample is incorrectly identified as a negative sample).…”

Section: Methodsmentioning

confidence: 99%

A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN

Yang

Zhang

Zhou³

et al. 2023

Sensors

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In this study, a piezoelectric active sensing-based time reversal method was investigated for monitoring pipeline internal corrosion. An effective method that combines wavelet packet energy with a Convolutional Neural Network (CNN) was proposed to identify the internal corrosion status of pipelines. Two lead zirconate titanate (PZT) patches were pasted on the outer surface of the pipeline as actuators and sensors to generate and receive ultrasonic signals propagating through the inner wall of the pipeline. Then, the time reversal technique was employed to reverse the received response signal in the time domain, and then to retransmit it as an excitation signal to obtain the focused signal. Afterward, the wavelet packet transform was used to decompose the focused signal, and the wavelet packet energy (WPE) with large components was extracted as the input of the CNN model to rapidly identify the corrosion degree inside the pipeline. The corrosion experiments were conducted to verify the correctness of the proposed method. The occurrence and development of corrosion in pipelines were generated by electrochemical corrosion, and nine different depths of corrosion were imposed on the sample pipeline. The experimental results indicated that the classification accuracy exceeded 99.01%. Therefore, this method can quantitatively monitor the corrosion status of pipelines and can pinpoint the internal corrosion degree of pipelines promptly and accurately. The WPE-CNN model in combination with the proposed time reversal method has high application potential for monitoring pipeline internal corrosion.

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“…The performance of the final trained CNN model needed to be evaluated by corresponding metrics [36]. Common evaluation metrics for classification tasks are Precision, Recall, and F1-Measure [37,38], which have the following equations: Among them, the role of the convolutional layer was to perform adaptive feature extraction on the Mel spectrogram, which was achieved by convolutional operations of the convolutional kernel matrix [34].…”

Section: Cnn Model Evaluation Metricsmentioning

confidence: 99%

Section: Cnn Model Evaluation Metricsmentioning

confidence: 99%

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

et al. 2022

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Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases. A non-destructive method to detect pipeline ponding using percussion acoustic signals and a convolution neural network (CNN) is proposed in this paper. During the process of detection, a constant energy spring impact hammer is used to apply an impact on the pipeline, and the percussive acoustic signals are collected. A Mel spectrogram is used to extract the acoustic feature of the percussive acoustic signal with different ponding volumes in the pipeline. The Mel spectrogram is transferred to the input layer of the CNN and the convolutional kernel matrix of the CNN realizes the recognition of pipeline ponding volume. The recognition results show that the CNN can identify the amount of pipeline ponding with the percussive acoustic signals, which use the Mel spectrogram as the acoustic feature. Compared with the support vector machine (SVM) model and the decision tree model, the CNN model has better recognition performance. Therefore, the percussion-based pipeline ponding detection using the convolutional neural network method proposed in this paper has high application potential.

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Using experts feedback in clinical case resolution and arbitration as accuracy diagnosis methodology

Cited by 8 publications

References 29 publications

Use of ontology structure and Bayesian models to aid the crowdsourcing of ICD-11 sanctioning rules

Use of ontology structure and Bayesian models to aid the crowdsourcing of ICD-11 sanctioning rules

A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

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