Estimating the severity of dental and oral problems via sentiment classification over clinical reports

• URL: http://arxiv.org/abs/2401.12993v1
• Date: Wed, 17 Jan 2024 14:33:13 GMT
• Title: Estimating the severity of dental and oral problems via sentiment classification over clinical reports
• Authors: Sare Mahdavifar, Seyed Mostafa Fakhrahmad, Elham Ansarifard
• Abstract summary: Analyzing authors' sentiments in texts can be practical and useful in various fields, including medicine and dentistry. Deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect severity level of patient's problem.
• Score: 0.8287206589886879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analyzing authors' sentiments in texts as a technique for identifying text polarity can be practical and useful in various fields, including medicine and dentistry. Currently, due to factors such as patients' limited knowledge about their condition, difficulties in accessing specialist doctors, or fear of illness, particularly in pandemic conditions, there might be a delay between receiving a radiology report and consulting a doctor. In some cases, this delay can pose significant risks to the patient, making timely decision-making crucial. Having an automatic system that can inform patients about the deterioration of their condition by analyzing the text of radiology reports could greatly impact timely decision-making. In this study, a dataset comprising 1,134 cone-beam computed tomography (CBCT) photo reports was collected from the Shiraz University of Medical Sciences. Each case was examined, and an expert labeled a severity level for the patient's condition on each document. After preprocessing all the text data, a deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect the severity level of the patient's problem based on sentiment analysis in the radiologist's report. The model's performance was evaluated on two datasets, each with two and four classes, in both imbalanced and balanced scenarios. Finally, to demonstrate the effectiveness of our model, we compared its performance with that of other classification models. The results, along with one-way ANOVA and Tukey's test, indicated that our proposed model (CNN-LSTM) performed the best according to precision, recall, and f-measure criteria. This suggests that it can be a reliable model for estimating the severity of oral and dental diseases, thereby assisting patients.

Related papers

• Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation [32.26270073540666]
  The worldwide shortage of radiologists restricts access to expert care and imposes heavy workloads. Recent progress in automated report generation with vision-language models offer clear potential in ameliorating the situation. We build a state-of-the-art report generation system for chest radiographs, $textitFlamingo-CXR, by fine-tuning a well-known vision-language foundation model on radiology data.
  arXiv  Detail & Related papers  (2023-11-30T05:38:34Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning [48.29204631769816]
  We re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-06T19:19:41Z)
• AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach [0.44998333629984877]
  We develop a methodology for balancing and augmenting data for more accurate prediction when the data is imbalanced and the sample size is small. The experimental results revealed that the average accuracy of our proposed method for CAD prediction was 95.36, and was higher than random forest (RF), decision tree (DT), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN)
  arXiv  Detail & Related papers  (2023-08-29T14:33:38Z)
• Modelling Patient Trajectories Using Multimodal Information [0.0]
  We propose a solution to model patient trajectories that combines different types of information and considers the temporal aspect of clinical data. The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression.
  arXiv  Detail & Related papers  (2022-09-09T10:20:54Z)
• An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data [0.0]
  We propose a multimodal network that ensembles deep multi-task logistic regression (MTLR), Cox proportional hazard (CoxPH) and CNN models to predict prognostic outcomes for patients with head and neck tumors. Our proposed ensemble solution achieves a C-index of 0.72 on The HECKTOR test set that saved us the first place in prognosis task of the HECKTOR challenge.
  arXiv  Detail & Related papers  (2022-02-25T07:50:59Z)
• Development of patients triage algorithm from nationwide COVID-19 registry data based on machine learning [1.0323063834827415]
  This paper provides the development processes of the severity assessment model using machine learning techniques. Model only requires basic patients' basic personal data, allowing for them to judge their own severity. We aim to establish a medical system that allows patients to check their own severity and informs them to visit the appropriate clinic center based on the past treatment details of other patients with similar severity.
  arXiv  Detail & Related papers  (2021-09-18T19:56:27Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.