A Classification Model for Infant Cries with Hearing Impairment and Unilateral Cleft Lip and Palate

Objective: To present the tympanometric findings in 552 patients (115 over 10 years of age) with unrepaired cleft palate (256 had audiologic findings) and to show the natural history and outcome of these cases. Setting: The cleft lip and palate clinic for the Division of Cleft Lip and Palate Surgery at the West China College of Stomatology, Sichuan University, Chengdu, People's Republic of China. Design: Pure-tone audiometric and tympanometric evaluations were performed on 552 patients with an unrepaired cleft palate. Results were analyzed by looking at the patient's age and cleft palate type. Results: This study demonstrated an age-related decrease in the frequency of hearing impairment and abnormal tympanometry. The frequency of hearing impairment and abnormal tympanometry in patients with submucous cleft palate was significantly lower than in patients from the other four major cleft palate categories (p = .001, p = .006, respectively). Conclusions: The middle ear function and hearing levels of unrepaired cleft palate patients improved with age, but at least 30% of the patients’ ears demonstrated a hearing loss and abnormal tympanometry in each age group, including those over 19 years of age. In the crucial language-learning stage, the frequency of hearing impairment and abnormal tympanometry was as high as 60%. Considering these results, palate repair and surgical intervention, such as tube insertion, for otological problems should be considered at an early age.

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Comparison of Supervised-learning Models for Infant Cry Classification / Vergleich von Klassifikationsmodellen zur Säuglingsschreianalyse

International Journal of Health Professions ◽

10.1515/ijhp-2015-0005 ◽

2015 ◽

Vol 2 (1) ◽

pp. 4-15 ◽

Cited By ~ 6

Author(s):

Tanja Fuhr ◽

Henning Reetz ◽

Carla Wegener

Keyword(s):

Cleft Lip ◽

Cleft Lip And Palate ◽

Diagnostic Purpose ◽

Classification Models ◽

Damage Classification ◽

Infant Cry ◽

The Past ◽

Healthy Infants ◽

Infant Cries ◽

Accuracy Degree

AbstractCries of infants can be seen as an indicator for several developmental diseases. Different types of classification algorithms have been used in the past to classify infant cries of healthy infants and those with developmental diseases. To determine the ability of classification models to discriminate between healthy infant cries and various cries of infants suffering from several diseases, a literature search for infant cry classification models was performed; 9 classification models were identified that have been used for infant cry classification in the past. These classification models, as well as 3 new approaches were applied to a reference dataset containing cries of healthy infants and cries of infants suffering from laryngomalacia, cleft lip and palate, hearing impairment, asphyxia and brain damage. Classification models were evaluated according to a rating schema, considering the aspects accuracy, degree of overfitting and conformability. Results indicate that many models have issues with accuracy and conformability. However, some of the models, like C5.0 decision trees and J48 classification trees provide promising results in infant cry classification for diagnostic purpose.

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ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data

BMC Bioinformatics ◽

10.1186/s12859-021-04443-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Kun Yu ◽

Weidong Xie ◽

Linjie Wang ◽

Wei Li

Keyword(s):

Feature Selection ◽

Microarray Data ◽

Cleft Lip ◽

Biomarker Discovery ◽

Cleft Lip And Palate ◽

Feature Selection Method ◽

Disease Diagnosis ◽

Selection Method ◽

Classification Model ◽

L1 Regularization

Abstract Background Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task. However, the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model. Results The proposed method has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the biomarkers selected using our method show a match to those provided by the cooperative hospital in a set of clinical cleft lip and palate data. Method In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. The features are firstly clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR. The final result is given according to the cumulative weight reordering. Conclusion The proposed method can effectively remove redundant features. The algorithm’s output has high stability and classification accuracy, which can potentially select potential biomarkers.

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ILRC : A Hybrid Biomarker Discovering Algorithm Based on Improved L1 Regularization and Clustering in Microarray Data

10.21203/rs.3.rs-572610/v1 ◽

2021 ◽

Author(s):

Kun Yu ◽

Weidong Xie ◽

Linjie Wang ◽

Wei Li

Keyword(s):

Feature Selection ◽

Microarray Data ◽

Cleft Lip ◽

Cleft Lip And Palate ◽

Feature Selection Method ◽

Disease Diagnosis ◽

Selection Method ◽

Classification Model ◽

Data Set ◽

L1 Regularization

Abstract Background: Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task, and the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an accurate classification model. Results: The proposed Mehtod has proved superior to the published advanced hybrid feature selection method and traditional feature selection method on different public microarray data sets. In addition, the results on the cleft lip and palate data set with known biomarkers provided by the cooperative hospital show that compared with other methods, our method can preferentially select these biomarkers. Method: In this paper, a feature selection algorithm ILRC based on clustering and improved L1 regularization is proposed. In this method, the features are first clustered, and the redundant features in the sub-clusters are deleted. Then all the remaining features are iteratively evaluated using ILR, and the final result is output according to the cumulative weight reordering. Conclusion: The proposed method can effectively remove redundant features. The algorithm’s output has high stability and classification accuracy and can potentially select potential biomarkers.

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Comparison of Supervised-Learning Models and Auditory Discrimination of Infant Cries for the Early Detection of Developmental Disorders / Vergleich von Supervised-Learning Klassifikationsmodellen und menschlicher auditiver Diskriminationsfähigkeit zur Unterscheidung von Säuglingsschreien mit kongenitalen Entwicklungsstörungen

International Journal of Health Professions ◽

10.2478/ijhp-2019-0003 ◽

2019 ◽

Vol 6 (1) ◽

pp. 2-18

Author(s):

Tanja Fuhr ◽

Henning Reetz ◽

Carla Wegener

Keyword(s):

Supervised Learning ◽

Developmental Disorders ◽

Cleft Lip ◽

Cleft Lip And Palate ◽

Auditory Discrimination ◽

Acoustic Properties ◽

Infant Cry ◽

Kappa Value ◽

Listening Experiment ◽

Infant Cries

Abstract Infant cry classification can be performed in two ways: computational classification of cries or auditory discrimination by human listeners. This article compares both approaches. An auditory listening experiment was performed to examine if various listener groups (naive listeners, parents, nurses/midwives and therapists) were able to distinguish auditorily between healthy and pathological cries as well as to differentiate various pathologies from each other. Listeners were trained in hearing cries of healthy infants and cries of infants suffering from cleft-lip-and-palate, hearing impairment, laryngomalacia, asphyxia and brain damage. After training, a listening experiment was performed by allocating 18 infant cries to the cry groups. Multiple supervised-learning classifications models were calculated on the base of the cries’ acoustic properties. The accuracy of the models was compared to the accuracy of the human listeners. With a Kappa value of 0.491, listeners allocated the cries to the healthy and the five pathological groups with moderate performance. With a sensitivity of 0.64 and a specificity of 0.89, listeners were able to identify that a cry is a pathological one with higher confidence than separating between the single pathologies. Generalized linear mixed models found no significant differences between the classification accuracy of the listener groups. Significant differences between the pathological cry types were found. Supervised-learning classification models performed significantly better than the human listeners in classifying infant cries. The models reached an overall Kappa value of up to 0.837.

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Treating Velopharyngeal Inadequacy Using Bilateral Buccal Flap Revision Palatoplasty

Perspectives of the ASHA Special Interest Groups ◽

10.1044/2019_pers-sig5-2019-0005 ◽

2019 ◽

Vol 4 (5) ◽

pp. 878-892

Author(s):

Joseph A. Napoli ◽

Linda D. Vallino

Keyword(s):

Systematic Review ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Cleft Lip ◽

Cleft Lip And Palate ◽

A Priori ◽

Sufficient Evidence ◽

Obstructive Sleep ◽

Before And After ◽

Buccal Flap

Purpose The 2 most commonly used operations to treat velopharyngeal inadequacy (VPI) are superiorly based pharyngeal flap and sphincter pharyngoplasty, both of which may result in hyponasal speech and airway obstruction. The purpose of this article is to (a) describe the bilateral buccal flap revision palatoplasty (BBFRP) as an alternative technique to manage VPI while minimizing these risks and (b) conduct a systematic review of the evidence of BBFRP on speech and other clinical outcomes. A report comparing the speech of a child with hypernasality before and after BBFRP is presented. Method A review of databases was conducted for studies of buccal flaps to treat VPI. Using the principles of a systematic review, the articles were read, and data were abstracted for study characteristics that were developed a priori. With respect to the case report, speech and instrumental data from a child with repaired cleft lip and palate and hypernasal speech were collected and analyzed before and after surgery. Results Eight articles were included in the analysis. The results were positive, and the evidence is in favor of BBFRP in improving velopharyngeal function, while minimizing the risk of hyponasal speech and obstructive sleep apnea. Before surgery, the child's speech was characterized by moderate hypernasality, and after surgery, it was judged to be within normal limits. Conclusion Based on clinical experience and results from the systematic review, there is sufficient evidence that the buccal flap is effective in improving resonance and minimizing obstructive sleep apnea. We recommend BBFRP as another approach in selected patients to manage VPI. Supplemental Material https://doi.org/10.23641/asha.9919352

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