Pub Date : 2022-12-06DOI: 10.1109/bibm55620.2022.9995572
{"title":"Precision Medicine Research Dimensions Made Accessible by Electronic Health Records","authors":"","doi":"10.1109/bibm55620.2022.9995572","DOIUrl":"https://doi.org/10.1109/bibm55620.2022.9995572","url":null,"abstract":"","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126087448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995675
Yu Zhou, Yinxian He, Kyungtae Kang
Obstructive sleep apnea (OSA) is a common sleeping issue that makes it difficult to breathe while you sleep and is linked to a number of other disorders, including cardiovascular conditions, such as hypertension and coronary heart disease. Nocturnal polysomnography (PSG) is one of the clinical diagnostic criteria for OSA, which is a painful and expensive form of diagnosis as it requires manual interpretation by experts and takes a lot of time. ECG-based techniques for diagnosing OSA have been introduced to alleviate these problems, but the most of solutions that have been put up thus far rely on feature engineering, which requires substantial specialist knowledge and expertise. In this study, we present a novel approach for classifying OSA based on a single-lead ECG signal conversion and a composite deep convolutional neural network model. The ECG signal is transformed into scalogram images with heart rate variability (HRV) characteristics and Gramian Angular Field (GAF) matrix images with temporal characteristics, incorporating the temporal properties of the ECG, to create the hybrid image dataset. The composite model contains three sub-convolutional neural networks, two of which utilize fine-tuned AlexNet and ResNet models, the third is a convolutional neural network with five residual blocks that are evaluated by a voting mechanism. The PhysioNet Apnea-ECG database was used to train and evaluate the proposed model. The results show that the proposed classifier achieved 90.93% accuracy, 83.86% sensitivity, 95.29% specificity, and 0.89 AUC on hybrid image datasets.
{"title":"OSA-CCNN: Obstructive Sleep Apnea Detection Based on a Composite Deep Convolution Neural Network Model using Single-Lead ECG signal","authors":"Yu Zhou, Yinxian He, Kyungtae Kang","doi":"10.1109/BIBM55620.2022.9995675","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995675","url":null,"abstract":"Obstructive sleep apnea (OSA) is a common sleeping issue that makes it difficult to breathe while you sleep and is linked to a number of other disorders, including cardiovascular conditions, such as hypertension and coronary heart disease. Nocturnal polysomnography (PSG) is one of the clinical diagnostic criteria for OSA, which is a painful and expensive form of diagnosis as it requires manual interpretation by experts and takes a lot of time. ECG-based techniques for diagnosing OSA have been introduced to alleviate these problems, but the most of solutions that have been put up thus far rely on feature engineering, which requires substantial specialist knowledge and expertise. In this study, we present a novel approach for classifying OSA based on a single-lead ECG signal conversion and a composite deep convolutional neural network model. The ECG signal is transformed into scalogram images with heart rate variability (HRV) characteristics and Gramian Angular Field (GAF) matrix images with temporal characteristics, incorporating the temporal properties of the ECG, to create the hybrid image dataset. The composite model contains three sub-convolutional neural networks, two of which utilize fine-tuned AlexNet and ResNet models, the third is a convolutional neural network with five residual blocks that are evaluated by a voting mechanism. The PhysioNet Apnea-ECG database was used to train and evaluate the proposed model. The results show that the proposed classifier achieved 90.93% accuracy, 83.86% sensitivity, 95.29% specificity, and 0.89 AUC on hybrid image datasets.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125424678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995344
Tianyu Wang, Wenming Yang, Jie Chen, Yonghong Tian, Dongqing Wei
Drug-target interaction(DTI) prediction is one of the most important topics in drug design and drug development, and deep learning approaches have achieved state-of-the-art performance in this field. However, the current methods are difficult to successfully combine the local and global features of drug molecules and protein sequences, while ignoring the modeling of complicated interaction mechanisms, which leads to a certain limitation of prediction performance. To overcome this barrier, we propose an end-to-end method based on Convolutional Neural Network (CNN) and Transformer to predict DTI problems, named ConformerDTI. The CNN and Transformer branches extract features from the simplified molecular input line entry system (SMILES) string of drugs and the amino acid sequence of proteins, respectively. The local and global features are coupled by the mutual transfer of the two branches through cross attention. Decoupling of local and global features in parallel leverages CNN’s power in extracting local features as well as the efficiency of Transformer at global processing. I n addition, ConformerDTI exploits the convolutional interaction network to model the interaction mechanism, both drugs and targets are convoluted by dynamic filters generated based on each other. Experimental results demonstrate that our model has better prediction performance than the most advanced deep learning methods on three different datasets. Furthermore, this performance improvement was validated by ablation experiments.
{"title":"ConformerDTI: Local Features Coupling Global Representations for Drug–Target Interaction Prediction","authors":"Tianyu Wang, Wenming Yang, Jie Chen, Yonghong Tian, Dongqing Wei","doi":"10.1109/BIBM55620.2022.9995344","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995344","url":null,"abstract":"Drug-target interaction(DTI) prediction is one of the most important topics in drug design and drug development, and deep learning approaches have achieved state-of-the-art performance in this field. However, the current methods are difficult to successfully combine the local and global features of drug molecules and protein sequences, while ignoring the modeling of complicated interaction mechanisms, which leads to a certain limitation of prediction performance. To overcome this barrier, we propose an end-to-end method based on Convolutional Neural Network (CNN) and Transformer to predict DTI problems, named ConformerDTI. The CNN and Transformer branches extract features from the simplified molecular input line entry system (SMILES) string of drugs and the amino acid sequence of proteins, respectively. The local and global features are coupled by the mutual transfer of the two branches through cross attention. Decoupling of local and global features in parallel leverages CNN’s power in extracting local features as well as the efficiency of Transformer at global processing. I n addition, ConformerDTI exploits the convolutional interaction network to model the interaction mechanism, both drugs and targets are convoluted by dynamic filters generated based on each other. Experimental results demonstrate that our model has better prediction performance than the most advanced deep learning methods on three different datasets. Furthermore, this performance improvement was validated by ablation experiments.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125578281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9994893
Qiuxia Shi, Bin Hu, Fuze Tian, Qinglin Zhao
In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.
{"title":"Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC","authors":"Qiuxia Shi, Bin Hu, Fuze Tian, Qinglin Zhao","doi":"10.1109/BIBM55620.2022.9994893","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9994893","url":null,"abstract":"In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115999029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995512
Justin Q Chen, Kevin Qi, Aaron Zhang, M. Shalaginov, TingyingHelen Zeng
As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians.
{"title":"COVID-19 Impact on Mental Health Analysis based on Reddit Comments","authors":"Justin Q Chen, Kevin Qi, Aaron Zhang, M. Shalaginov, TingyingHelen Zeng","doi":"10.1109/BIBM55620.2022.9995512","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995512","url":null,"abstract":"As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122314295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995548
Jie Liu, Hong Lai, Jinshu Ma, Shuchao Pang
In recent years, deep convolutional neural networks (CNNs) have been spectacularly successful in the classification and diagnosis of breast cancer and its histopathological images. However, for CNNs, the whole learning process requires high computational complexity, a large number of parameters, and loss of certain global feature information. Meanwhile, the flexibility of tensor networks (TNs) algorithms to machine learning leads to creativity in devising new approaches. In this paper, we propose a novel framework named ConTenNet based on the pre-trained CNNs and quantum TNs (QTNs) to address the weaknesses in CNNs. We propose ConTenNet on the BreakHis dataset, and the experiments show that our model competes with the state-of-the-art methods on both original and normalized images with lower computational complexity, a less number of parameters, and global feature information. Moreover, we adopt the color normalization method to avoid the interference of color in model learning, using the gradient-weighted class activation mapping (Grad-CAM) to prove the necessity of color normalization and the reliability of model learning.
{"title":"ConTenNet: Quantum Tensor-augmented Convolutional Representations for Breast Cancer Histopathological Image Classification","authors":"Jie Liu, Hong Lai, Jinshu Ma, Shuchao Pang","doi":"10.1109/BIBM55620.2022.9995548","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995548","url":null,"abstract":"In recent years, deep convolutional neural networks (CNNs) have been spectacularly successful in the classification and diagnosis of breast cancer and its histopathological images. However, for CNNs, the whole learning process requires high computational complexity, a large number of parameters, and loss of certain global feature information. Meanwhile, the flexibility of tensor networks (TNs) algorithms to machine learning leads to creativity in devising new approaches. In this paper, we propose a novel framework named ConTenNet based on the pre-trained CNNs and quantum TNs (QTNs) to address the weaknesses in CNNs. We propose ConTenNet on the BreakHis dataset, and the experiments show that our model competes with the state-of-the-art methods on both original and normalized images with lower computational complexity, a less number of parameters, and global feature information. Moreover, we adopt the color normalization method to avoid the interference of color in model learning, using the gradient-weighted class activation mapping (Grad-CAM) to prove the necessity of color normalization and the reliability of model learning.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122494133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995101
Yi Lin, Jingchi Jiang, Dongxin Chen, Zhaoyang Ma, Yi Guan, Xiguang Liu, Haiyan You, Jing Yang, Xue Cheng
Acne Vulgaris seriously affects people’s daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.
{"title":"Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge","authors":"Yi Lin, Jingchi Jiang, Dongxin Chen, Zhaoyang Ma, Yi Guan, Xiguang Liu, Haiyan You, Jing Yang, Xue Cheng","doi":"10.1109/BIBM55620.2022.9995101","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995101","url":null,"abstract":"Acne Vulgaris seriously affects people’s daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122962196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995305
Tianqi Yang, Shimin Zhang, Yuqing Li
This paper counts the origin of plant herbs recorded in the Compendium of Materia Medica (hereinafter referred to as Compendium), and intends to reveal the geographical distribution features of plant herbs in the Ming Dynasty and the factors that led to its establishment. We provide reference and basis for finding suitable producing areas of plant herbs. Using excel table, we entered and counted the origin of plant herbs. ArcGIS software was used to mark the frequency results of the statistical origin on the map to present the origin distribution results. Maximum Entropy Model (MAXENT) was used to predict the different effects of natural environmental factors on the growth period of Evodiae fructus. In the Ming Dynasty, the number and variety of plant herbs produced increased compared to the previous dynasties, and the geographic scope of the origin dispersion also expanded. The dominant environmental factors play a decisive role in the quantity and quality of plant herbs produced, nevertheless, economic, demographic, political, and other human factors also have an impact on the actual situation of plant herbs grown. It can provide a reference for the division of acceptable herbal origin when the MAXENT prediction findings and the historical origin are combined.
{"title":"A Study on the Distribution and Influencing Factors of the Origin of Plant Herbs in Compendium of Materia Medica","authors":"Tianqi Yang, Shimin Zhang, Yuqing Li","doi":"10.1109/BIBM55620.2022.9995305","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995305","url":null,"abstract":"This paper counts the origin of plant herbs recorded in the Compendium of Materia Medica (hereinafter referred to as Compendium), and intends to reveal the geographical distribution features of plant herbs in the Ming Dynasty and the factors that led to its establishment. We provide reference and basis for finding suitable producing areas of plant herbs. Using excel table, we entered and counted the origin of plant herbs. ArcGIS software was used to mark the frequency results of the statistical origin on the map to present the origin distribution results. Maximum Entropy Model (MAXENT) was used to predict the different effects of natural environmental factors on the growth period of Evodiae fructus. In the Ming Dynasty, the number and variety of plant herbs produced increased compared to the previous dynasties, and the geographic scope of the origin dispersion also expanded. The dominant environmental factors play a decisive role in the quantity and quality of plant herbs produced, nevertheless, economic, demographic, political, and other human factors also have an impact on the actual situation of plant herbs grown. It can provide a reference for the division of acceptable herbal origin when the MAXENT prediction findings and the historical origin are combined.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114431778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995269
A. Bomgni, Ernest Basile Fotseu Fotseu, Daril Raoul Kengne Wambo, R. Sani, C. Lushbough, Etienne Z. Gnimpieba
Nowadays, online databases such as PUBMED and PMC are experiencing an explosion of publications in the field of biomedical sciences. With so much information available online, one of the biggest challenges is managing all that raw, unstructured data and making it machine-readable. Name entity recognition is nowadays a prerequisite for data identification and extraction in biosciences. One of the areas that allows automatic extraction of information from biomedical literature today is Name Entity Recognition. Indeed, it makes it possible to simplify the workflow analysis and automatic extraction of name entities, thus improving the various existing models. There is in the literature a lot of tools for this purpose, but they are unable to extract microbial genes accurately. Moreover, current goal standard corpora such as BIOCREATIVE I to IV have limited representation of microbial knowledge. In this paper, we proposed a new method to recognize biofilm gene mentions from free text. This method relies on a context-specific dictionary to annotate a consistent corpus necessary to train an efficient recognition model. Indeed, this method provides a new workflow for dataset collection generation for microbial biofilm gene. Trained on a set of biofilm organisms our method achieves a score of up to 94%, outperforming state-of-the-art frameworks.
{"title":"Attention model-based and multi-organism driven gene recognition from text: application to a microbial biofilm organism set.","authors":"A. Bomgni, Ernest Basile Fotseu Fotseu, Daril Raoul Kengne Wambo, R. Sani, C. Lushbough, Etienne Z. Gnimpieba","doi":"10.1109/BIBM55620.2022.9995269","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995269","url":null,"abstract":"Nowadays, online databases such as PUBMED and PMC are experiencing an explosion of publications in the field of biomedical sciences. With so much information available online, one of the biggest challenges is managing all that raw, unstructured data and making it machine-readable. Name entity recognition is nowadays a prerequisite for data identification and extraction in biosciences. One of the areas that allows automatic extraction of information from biomedical literature today is Name Entity Recognition. Indeed, it makes it possible to simplify the workflow analysis and automatic extraction of name entities, thus improving the various existing models. There is in the literature a lot of tools for this purpose, but they are unable to extract microbial genes accurately. Moreover, current goal standard corpora such as BIOCREATIVE I to IV have limited representation of microbial knowledge. In this paper, we proposed a new method to recognize biofilm gene mentions from free text. This method relies on a context-specific dictionary to annotate a consistent corpus necessary to train an efficient recognition model. Indeed, this method provides a new workflow for dataset collection generation for microbial biofilm gene. Trained on a set of biofilm organisms our method achieves a score of up to 94%, outperforming state-of-the-art frameworks.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122034136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.1109/BIBM55620.2022.9995321
Carlos Arias-Alcaide, C. Soguero-Ruíz, Paloma Santos-Alvarez, José F. Varona Arche, I. Mora-Jiménez
The most recent Clinical Decision Support Systems use the potential of Machine Learning techniques to target clinical problems, avoiding the use of explicit rules. In this paper, a model to monitor and predict the risk of unfavourable evolution (UE) during hospitalization of COVID-19 patients is proposed. It combines Self Organizing Maps and local Naïve Bayes (NB) classifiers because of interpretation purposes. We used the results of six blood tests (leukocytes, D-dimer, among others) provided by a Spanish hospital group. The probabilistic approach allows us to get the daily risk of UE for each patient in an interpretable way. Several variants of the NB classifiers family have been explored, mainly weighting and likelihood estimation (parametric and nonparametric). Despite the over-simplified assumptions of the NB classifiers, they provided good predictive results in terms of sensitivity and specificity. The model with nonparametric likelihood estimation provided the best risk prediction over time even when designed with a limited number of samples. Specifically, the median value and interquartil range for the risk prediction were quite reliable even 10 days before the event day for patients hospitalized longer than 7 days. The risk median values also agree with the gold-standard for patients with a hospital stay shorter than 7 days, though the interquartil range can be too wide (probably because of the variability in the inpatient days - sometimes, just 2 days). Though a deepest analysis considering more patients and features would be convenient, our results show the potential of the proposed approach, both from a technical and clinical viewpoint.
{"title":"Local Naïve Bayes for Predicting Evolution of COVID-19 Patients on Self Organizing Maps","authors":"Carlos Arias-Alcaide, C. Soguero-Ruíz, Paloma Santos-Alvarez, José F. Varona Arche, I. Mora-Jiménez","doi":"10.1109/BIBM55620.2022.9995321","DOIUrl":"https://doi.org/10.1109/BIBM55620.2022.9995321","url":null,"abstract":"The most recent Clinical Decision Support Systems use the potential of Machine Learning techniques to target clinical problems, avoiding the use of explicit rules. In this paper, a model to monitor and predict the risk of unfavourable evolution (UE) during hospitalization of COVID-19 patients is proposed. It combines Self Organizing Maps and local Naïve Bayes (NB) classifiers because of interpretation purposes. We used the results of six blood tests (leukocytes, D-dimer, among others) provided by a Spanish hospital group. The probabilistic approach allows us to get the daily risk of UE for each patient in an interpretable way. Several variants of the NB classifiers family have been explored, mainly weighting and likelihood estimation (parametric and nonparametric). Despite the over-simplified assumptions of the NB classifiers, they provided good predictive results in terms of sensitivity and specificity. The model with nonparametric likelihood estimation provided the best risk prediction over time even when designed with a limited number of samples. Specifically, the median value and interquartil range for the risk prediction were quite reliable even 10 days before the event day for patients hospitalized longer than 7 days. The risk median values also agree with the gold-standard for patients with a hospital stay shorter than 7 days, though the interquartil range can be too wide (probably because of the variability in the inpatient days - sometimes, just 2 days). Though a deepest analysis considering more patients and features would be convenient, our results show the potential of the proposed approach, both from a technical and clinical viewpoint.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122225762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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