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Yasin P, Yimit Y, Cai X, Aimaiti A, Sheng W, Mamat M, Nijiati M. Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI). Eur J Med Res 2024; 29:383. [PMID: 39054495 PMCID: PMC11270948 DOI: 10.1186/s40001-024-01988-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 07/18/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Tuberculosis spondylitis (TS), commonly known as Pott's disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolonged hospital stays (PLOS). Therefore, identifying risk factors associated with extended PLOS is necessary. In this research, we intended to develop an interpretable machine learning model that could predict extended PLOS, which can provide valuable insights for treatments and a web-based application was implemented. METHODS We obtained patient data from the spine surgery department at our hospital. Extended postoperative length of stay (PLOS) refers to a hospitalization duration equal to or exceeding the 75th percentile following spine surgery. To identify relevant variables, we employed several approaches, such as the least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) based on support vector machine classification (SVC), correlation analysis, and permutation importance value. Several models using implemented and some of them are ensembled using soft voting techniques. Models were constructed using grid search with nested cross-validation. The performance of each algorithm was assessed through various metrics, including the AUC value (area under the curve of receiver operating characteristics) and the Brier Score. Model interpretation involved utilizing methods such as Shapley additive explanations (SHAP), the Gini Impurity Index, permutation importance, and local interpretable model-agnostic explanations (LIME). Furthermore, to facilitate the practical application of the model, a web-based interface was developed and deployed. RESULTS The study included a cohort of 580 patients and 11 features include (CRP, transfusions, infusion volume, blood loss, X-ray bone bridge, X-ray osteophyte, CT-vertebral destruction, CT-paravertebral abscess, MRI-paravertebral abscess, MRI-epidural abscess, postoperative drainage) were selected. Most of the classifiers showed better performance, where the XGBoost model has a higher AUC value (0.86) and lower Brier Score (0.126). The XGBoost model was chosen as the optimal model. The results obtained from the calibration and decision curve analysis (DCA) plots demonstrate that XGBoost has achieved promising performance. After conducting tenfold cross-validation, the XGBoost model demonstrated a mean AUC of 0.85 ± 0.09. SHAP and LIME were used to display the variables' contributions to the predicted value. The stacked bar plots indicated that infusion volume was the primary contributor, as determined by Gini, permutation importance (PFI), and the LIME algorithm. CONCLUSIONS Our methods not only effectively predicted extended PLOS but also identified risk factors that can be utilized for future treatments. The XGBoost model developed in this study is easily accessible through the deployed web application and can aid in clinical research.
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Affiliation(s)
- Parhat Yasin
- Department of Spine Surgery, The Sixth Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, Xinjiang, People's Republic of China
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, People's Republic of China
| | - Yasen Yimit
- Department of Radiology, The First People's Hospital of Kashi Prefecture, Kashi, 844000, Xinjiang, People's Republic of China
| | - Xiaoyu Cai
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, People's Republic of China
| | - Abasi Aimaiti
- Department of Anesthesiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, People's Republic of China
| | - Weibin Sheng
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, People's Republic of China
| | - Mardan Mamat
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, People's Republic of China.
| | - Mayidili Nijiati
- Department of Radiology, The Fourth Affiliated Hospital of Xinjiang Medical University(Xinjiang Hospital of Traditional Chinese Medicine), Urumqi, 830002, Xinjiang, People's Republic of China.
- Xinjiang Key Laboratory of Artificial Intelligence Assisted Imaging Diagnosis, Kashi, 844000, Xinjiang, People's Republic of China.
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Hosseini M, Rasekh AH, Keshavarzi A. Improving clinical abbreviation sense disambiguation using attention-based Bi-LSTM and hybrid balancing techniques in imbalanced datasets. J Eval Clin Pract 2024. [PMID: 39031903 DOI: 10.1111/jep.14041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/29/2024] [Accepted: 05/21/2024] [Indexed: 07/22/2024]
Abstract
RATIONALE Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to classifiers with low accuracy and high error rates. Traditional feature-engineered models struggle with this task, and class imbalance is a known factor that reduces the performance of neural network techniques. AIMS AND OBJECTIVES This study proposes an attention-based bidirectional long short-term memory (Bi-LSTM) model to improve clinical abbreviation disambiguation in clinical documents. We aim to address the challenges of limited training data and class imbalance by employing data generation techniques like reverse substitution and data augmentation with synonym substitution. METHOD We utilise a Bi-LSTM classification model with an attention mechanism to disambiguate each abbreviation. The model's performance is evaluated based on accuracy for each abbreviation. To address the limitations of imbalanced data, we employ data generation techniques to create a more balanced dataset. RESULTS The evaluation results demonstrate that our data balancing technique significantly improves the model's accuracy by 2.08%. Furthermore, the proposed attention-based Bi-LSTM model achieves an accuracy of 96.09% on the UMN dataset, outperforming state-of-the-art results. CONCLUSION Deep neural network methods, particularly Bi-LSTM, offer promising alternatives to traditional feature-engineered models for clinical abbreviation disambiguation. By employing data generation techniques, we can address the challenges posed by limited-resource and imbalanced clinical datasets. This approach leads to a significant improvement in model accuracy for clinical abbreviation disambiguation tasks.
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Affiliation(s)
- Manda Hosseini
- Department of Computer Engineering, Zand Institute of Higher Education, Shiraz, Iran
| | - Amir Hossein Rasekh
- Department of Computer Engineering, Zand Institute of Higher Education, Shiraz, Iran
| | - Amin Keshavarzi
- Department of Computer Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
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Yang J, Hussein Kadir D. Data mining techniques in breast cancer diagnosis at the cellular-molecular level. J Cancer Res Clin Oncol 2023; 149:12605-12620. [PMID: 37442866 DOI: 10.1007/s00432-023-05090-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023]
Abstract
INTRODUCTION Studies in the field of better diagnosis of breast cancer using machine learning and data mining techniques have always been promising. A new diagnostic method can detect the characteristics of breast cancer in the early stages and help in better treatment. The aim of this study is to provide a method for early detection of breast cancer by reducing human errors based on data mining techniques in medicine using accurate and rapid screening. METHODOLOGY The proposed method includes data pre-processing and image quality improvement in the first step. The second step consists of separating cancer cells from healthy breast tissue and removing outliers using image segmentation. Finally, a classification model is configured by combining deep neural networks in the third phase. The proposed ensemble classification model uses several effective features extracted from images and is based on majority vote. This model can be used as a screening system to diagnose the grade of invasive ductal carcinoma of the breast. RESULTS Evaluations have been done using two histopathological microscopic datasets including patients with invasive ductal carcinoma of the breast. With extracting high-level features with average accuracies of 92.65% and 93.34% in these two datasets, the proposed method has succeeded in quickly diagnosing and classifying breast cancer with high performance. CONCLUSION By combining deep neural networks and extracting features affecting breast cancer, the ability to diagnose with the highest accuracy is provided, and this is a step toward helping specialists and increasing the chances of patients' survival.
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Affiliation(s)
- Jian Yang
- General Office of China Science and Technology Development Center for Chinese Medicine, Chaoyang District, Beijing, 100020, China.
| | - Dler Hussein Kadir
- Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University, Erbil, Iraq
- Department of Business Administration, Cihan University-Erbil, Erbil, Iraq
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Sahu A, Das PK, Meher S. Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms. Phys Med 2023; 114:103138. [PMID: 37914431 DOI: 10.1016/j.ejmp.2023.103138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 07/22/2023] [Accepted: 09/14/2023] [Indexed: 11/03/2023] Open
Abstract
OBJECTIVE Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms. In this article, our objective is to give an overview of recent advancements in machine learning (ML) and deep learning (DL)-based breast cancer detection systems. METHODS We give a structured framework to categorize mammogram-based breast cancer detection techniques. Several publicly available mammogram databases and different performance measures are also mentioned. RESULTS After deliberate investigation, we find most of the works classify breast tumors either as normal-abnormal or malignant-benign rather than classifying them into three classes. Furthermore, DL-based features are more significant than hand-crafted features. However, transfer learning is preferred over others as it yields better performance in small datasets, unlike classical DL techniques. SIGNIFICANCE AND CONCLUSION In this article, we have made an attempt to give recent advancements in artificial intelligence (AI)-based breast cancer detection systems. Furthermore, a number of challenging issues and possible research directions are mentioned, which will help researchers in further scopes of research in this field.
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Affiliation(s)
- Adyasha Sahu
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India.
| | - Pradeep Kumar Das
- School of Electronics Engineering (SENSE), VIT Vellore, Tamil Nadu, 632014, India.
| | - Sukadev Meher
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India.
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Li X, Chen X, Rezaeipanah A. Automatic breast cancer diagnosis based on hybrid dimensionality reduction technique and ensemble classification. J Cancer Res Clin Oncol 2023; 149:7609-7627. [PMID: 36995408 DOI: 10.1007/s00432-023-04699-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/17/2023] [Indexed: 03/31/2023]
Abstract
INTRODUCTION Feature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the accuracy and efficiency of the system. Since there are many irrelevant and redundant features in breast cancer diagnosis, removing such features leads to more accurate prediction and reduced decision time when dealing with large-scale data. Meanwhile, ensemble classifiers are powerful techniques to improve the prediction performance of classification models, where several individual classifier models are combined to achieve higher accuracy. METHODS In this paper, an ensemble classifier algorithm based on multilayer perceptron neural network is proposed for the classification task, in which the parameters (e.g., number of hidden layers, number of neurons in each hidden layer, and weights of links) are adjusted based on an evolutionary approach. Meanwhile, this paper uses a hybrid dimensionality reduction technique based on principal component analysis and information gain to address this problem. RESULTS The effectiveness of the proposed algorithm was evaluated based on the Wisconsin breast cancer database. In particular, the proposed algorithm provides an average of 17% better accuracy compared to the best results obtained from the existing state-of-the-art methods. CONCLUSION Experimental results show that the proposed algorithm can be used as an intelligent medical assistant system for breast cancer diagnosis.
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Affiliation(s)
- Xingyuan Li
- Depiecement of Oncology, The PLA Navy Anqing Hospital, Anqing, 246000, Anhui, China
| | - Xi Chen
- Department of Thyroid and Breast Surgery, Anqing Municipal Hospital, Anqing, 246000, Anhui, China.
| | - Amin Rezaeipanah
- Department of Computer Engineering, Persian Gulf University, Bushehr, Iran.
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An Ensemble of Transfer Learning Models for the Prediction of Skin Cancers with Conditional Generative Adversarial Networks. Diagnostics (Basel) 2022; 12:diagnostics12123145. [PMID: 36553152 PMCID: PMC9777332 DOI: 10.3390/diagnostics12123145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022] Open
Abstract
Skin cancer is one of the most severe forms of the disease, and it can spread to other parts of the body if not detected early. Therefore, diagnosing and treating skin cancer patients at an early stage is crucial. Since a manual skin cancer diagnosis is both time-consuming and expensive, an incorrect diagnosis is made due to the high similarity between the various skin cancers. Improved categorization of multiclass skin cancers requires the development of automated diagnostic systems. Herein, we propose a fully automatic method for classifying several skin cancers by fine-tuning the deep learning models VGG16, ResNet50, and ResNet101. Prior to model creation, the training dataset should undergo data augmentation using traditional image transformation techniques and Generative Adversarial Networks (GANs) to prevent class imbalance issues that may lead to model overfitting. In this study, we investigate the feasibility of creating dermoscopic images that have a realistic appearance using Conditional Generative Adversarial Network (CGAN) techniques. Thereafter, the traditional augmentation methods are used to augment our existing training set to improve the performance of pre-trained deep models on the skin cancer classification task. This improved performance is then compared to the models developed using the unbalanced dataset. In addition, we formed an ensemble of finely tuned transfer learning models, which we trained on balanced and unbalanced datasets. These models were used to make predictions about the data. With appropriate data augmentation, the proposed models attained an accuracy of 92% for VGG16, 92% for ResNet50, and 92.25% for ResNet101, respectively. The ensemble of these models increased the accuracy to 93.5%. A comprehensive discussion on the performance of the models concluded that using this method possibly leads to enhanced performance in skin cancer categorization compared to the efforts made in the past.
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Azevedo V, Silva C, Dutra I. Quantum transfer learning for breast cancer detection. QUANTUM MACHINE INTELLIGENCE 2022; 4:5. [PMID: 35252762 PMCID: PMC8883238 DOI: 10.1007/s42484-022-00062-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Abstract
One of the areas with the potential to be explored in quantum computing (QC) is machine learning (ML), giving rise to quantum machine learning (QML). In an era when there is so much data, ML may benefit from either speed, complexity or smaller amounts of storage. In this work, we explore a quantum approach to a machine learning problem. Based on the work of Mari et al., we train a set of hybrid classical-quantum neural networks using transfer learning (TL). Our task was to solve the problem of classifying full-image mammograms into malignant and benign, provided by BCDR. Throughout the course of our work, heatmaps were used to highlight the parts of the mammograms that were being targeted by the networks while evaluating different performance metrics. Our work shows that this method may hold benefits regarding the generalization of complex data; however, further tests are needed. We also show that, depending on the task, some architectures perform better than others. Nonetheless, our results were superior to those reported in the state-of-the-art (accuracy of 84% against 76.9%, respectively). In addition, experiments were conducted in a real quantum device, and results were compared with the classical and simulator.
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Affiliation(s)
- Vanda Azevedo
- Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Carla Silva
- Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Inês Dutra
- CINTESIS and Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
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Thani I, Kasbe T. Expert system based on fuzzy rules for diagnosing breast cancer. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00643-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study. SENSORS 2021; 22:s22010203. [PMID: 35009746 PMCID: PMC8749541 DOI: 10.3390/s22010203] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/22/2021] [Accepted: 12/24/2021] [Indexed: 02/08/2023]
Abstract
A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes.
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Li YX, Shen XP, Yang C, Cao ZZ, Du R, Yu MD, Wang JP, Wang M. Novelelectronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms. Pregnancy Hypertens 2021; 26:102-109. [PMID: 34739939 DOI: 10.1016/j.preghy.2021.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/16/2021] [Accepted: 10/22/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE To predict risk of pre-eclampsia (PE) in women using machine learning (ML) algorithms, based on electronic health records (EHR) collected at the early second trimester. STUDY DESIGN A total of 3759 cases of pregnancy who received antenatal care at Xinhua hospital Chongming branch Affiliated to Shanghai Jiaotong University were included in this retrospective EHR-based study. Thirty-eight candidate clinical parameters routinely available at the first visit in antenatal care were collected by manual chart review. Logistic regression (LR), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) were used to construct the prediction model. Features that contributed to the model predictions were identified using XGBoost. OUTCOME MEASURES The performance of ML models to predict women at risk of PE was quantified in terms of accuracy, precision, recall, false negative score, f1_score, brier score and the area under the receiver operating curve (auROC). RESULTS The XGboost model had the best prediction performance (accuracy = 0.920, precision = 0.447, recall = 0.789, f1_score = 0.571, auROC = 0.955). The most predictive feature of PE development was fasting plasma glucose, followed by mean blood pressure and body mass index. An easy-to-use model that a patient could answer independently still enabled accurate prediction, with auROC of 0.83. CONCLUSION risk of PE development can be predicted with excellent discriminative ability using ML algorithms based on EHR collected at the early second trimester. Future studies are needed to assess the real-world clinical utility of the model.
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Affiliation(s)
- Yi-Xin Li
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiao-Ping Shen
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chao Yang
- Department of Scientific Research Centre, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zuo-Zeng Cao
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Rui Du
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Min-da Yu
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jun-Ping Wang
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mei Wang
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Esmaeili M, Ayyoubzadeh SM, Ahmadinejad N, Ghazisaeedi M, Nahvijou A, Maghooli K. A decision support system for mammography reports interpretation. Health Inf Sci Syst 2020; 8:17. [PMID: 32257128 PMCID: PMC7113352 DOI: 10.1007/s13755-020-00109-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 03/30/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Mammography plays a key role in the diagnosis of breast cancer; however, decision-making based on mammography reports is still challenging. This paper aims to addresses the challenges regarding decision-making based on mammography reports and propose a Clinical Decision Support System (CDSS) using data mining methods to help clinicians to interpret mammography reports. METHODS For this purpose, 2441 mammography reports were collected from Imam Khomeini Hospital from March 21, 2018, to March 20, 2019. In the first step, these mammography reports are analyzed and program code is developed to transform the reports into a dataset. Then, the weight of every feature of the dataset is calculated. Random Forest, Naïve Bayes, K-nearest neighbor (K-NN), Deep Learning classifiers are applied to the dataset to build a model capable of predicting the need for referral to biopsy. Afterward, the models are evaluated using cross-validation with measuring Area Under Curve (AUC), accuracy, sensitivity, specificity indices. RESULTS The mammography type (diagnostic or screening), mass and calcification features mentioned in the reports are the most important features for decision-making. Results reveal that the K-NN model is the most accurate and specific classifier with the accuracy and specificity values of 84.06% and 84.72% respectively. The Random Forest classifier has the best sensitivity and AUC with the sensitivity and AUC values of 87.74% and 0.905 respectively. CONCLUSIONS Accordingly, data mining approaches are proved to be a helpful tool to make the final decision as to whether patients should be referred to biopsy or not based on mammography reports. The developed CDSS may also be helpful especially for less experienced radiologists.
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Affiliation(s)
- Marzieh Esmaeili
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, 3rd Floor, No #17, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran
- Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, 3rd Floor, No #17, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran
- Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Nasrin Ahmadinejad
- Medical Imaging Cancer, Imam Khomeini Hospital, Cancer Research Institute, Tehran, Iran
- Advanced Diagnostic and Interventional Radiology Research Cancer (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | - Marjan Ghazisaeedi
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, 3rd Floor, No #17, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran
| | - Azin Nahvijou
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Altıntop ÇG, Latifoğlu F, Akın AK, İleri R, Yazar MA. Analysis of Consciousness Level Using Galvanic Skin Response during Therapeutic Effect. J Med Syst 2020; 45:1. [PMID: 33236166 DOI: 10.1007/s10916-020-01677-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 11/17/2020] [Indexed: 11/25/2022]
Abstract
The neurological status of patients in the Intensive Care Units (ICU) is determined by the Glasgow Coma Scale (GCS). Patients in coma are thought to be unaware of what is happening around them. However, many studies show that the family plays an important role in the recovery of the patient and is a great emotional resource. In this study, Galvanic Skin Response (GSR) signals were analyzed from 31 patients with low consciousness levels between GCS 3 and 8 to determine relationship between consciousness level and GSR signals as a new approach. The effect of family and nurse on unconscious patients was investigated by GSR signals recorded with a new proposed protocol. The signals were recorded during conversation and touching of the patient by the nurse and their families. According to numerical results, the level of consciousness can be separated using GSR signals. Also, it was found that family and nurse had statistically significant effects on the patient. Patients with GCS 3,4, and 5 were considered to have low level of consciousness, while patients with GCS 6,7, and 8 were considered to have high level of consciousness. According to our results, it is obtained lower GSR amplitude in low GCS (3, 4, 5) compared to high GCS (7, 8). It was concluded that these patients were aware of therapeutic affect although they were unconscious. During the classification stage of this study, the class imbalance problem, which is common in medical diagnosis, was solved using Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN) and random oversampling methods. In addition, level of consciousness was classified with 92.7% success using various decision tree algorithms. Random Forest was the method which provides higher accuracy compared to all other methods. The obtained results showed that GSR signal analysis recorded in different stages gives very successful GCS score classification performance according to literature studies.
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Affiliation(s)
| | - Fatma Latifoğlu
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey.
| | - Aynur Karayol Akın
- Department of Anesthesiology and Reanimation, Erciyes University, Kayseri, Turkey
| | - Ramis İleri
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
| | - Mehmet Akif Yazar
- Department of Anesthesiology and Reanimation, Konya Training and Research Hospital, Konya, Turkey
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Antaki F, Kahwati G, Sebag J, Coussa RG, Fanous A, Duval R, Sebag M. Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience. Sci Rep 2020; 10:19528. [PMID: 33177614 PMCID: PMC7658348 DOI: 10.1038/s41598-020-76665-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 11/01/2020] [Indexed: 11/23/2022] Open
Abstract
We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506 eyes who underwent pars plana vitrectomy for rhegmatogenous retinal detachment (RRD) by a single surgeon at a tertiary-care hospital between 2012 and 2019. Two ophthalmologists without coding experience used an interactive application in MATLAB to build and evaluate ML algorithms for the prediction of postoperative PVR using clinical data from the electronic health records. The clinical features associated with postoperative PVR were determined by univariate feature selection. The area under the curve (AUC) for predicting postoperative PVR was better for models that included pre-existing PVR as an input. The quadratic support vector machine (SVM) model built using all selected clinical features had an AUC of 0.90, a sensitivity of 63.0%, and a specificity of 97.8%. An optimized Naïve Bayes algorithm that did not include pre-existing PVR as an input feature had an AUC of 0.81, a sensitivity of 54.3%, and a specificity of 92.4%. In conclusion, the development of ML models for the prediction of PVR by ophthalmologists without coding experience is feasible. Input from a data scientist might still be needed to tackle class imbalance-a common challenge in ML classification using real-world clinical data.
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Affiliation(s)
- Fares Antaki
- Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada
- Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, QC, Canada
- Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de l'Est-de-l'Île-de-Montréal, Montreal, QC, Canada
| | - Ghofril Kahwati
- Institut National des Sciences Appliquées de Toulouse (INSA Toulouse), Toulouse, France
- École de Technologie Supérieure (ÉTS), Montreal, QC, Canada
| | - Julia Sebag
- Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada
| | - Razek Georges Coussa
- Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Anthony Fanous
- Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Renaud Duval
- Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada
- Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de l'Est-de-l'Île-de-Montréal, Montreal, QC, Canada
| | - Mikael Sebag
- Department of Ophthalmology, Université de Montréal, Montreal, QC, Canada.
- Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, QC, Canada.
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Son LH, Ciaramella A, Thu Huyen DT, Staiano A, Tuan TM, Van Hai P. Predictive reliability and validity of hospital cost analysis with dynamic neural network and genetic algorithm. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04876-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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15
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Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer. IRANIAN RED CRESCENT MEDICAL JOURNAL 2019. [DOI: 10.5812/ircmj.92077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Hosni M, Abnane I, Idri A, Carrillo de Gea JM, Fernández Alemán JL. Reviewing ensemble classification methods in breast cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:89-112. [PMID: 31319964 DOI: 10.1016/j.cmpb.2019.05.019] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/16/2019] [Accepted: 05/18/2019] [Indexed: 05/09/2023]
Abstract
CONTEXT Ensemble methods consist of combining more than one single technique to solve the same task. This approach was designed to overcome the weaknesses of single techniques and consolidate their strengths. Ensemble methods are now widely used to carry out prediction tasks (e.g. classification and regression) in several fields, including that of bioinformatics. Researchers have particularly begun to employ ensemble techniques to improve research into breast cancer, as this is the most frequent type of cancer and accounts for most of the deaths among women. OBJECTIVE AND METHOD The goal of this study is to analyse the state of the art in ensemble classification methods when applied to breast cancer as regards 9 aspects: publication venues, medical tasks tackled, empirical and research types adopted, types of ensembles proposed, single techniques used to construct the ensembles, validation framework adopted to evaluate the proposed ensembles, tools used to build the ensembles, and optimization methods used for the single techniques. This paper was undertaken as a systematic mapping study. RESULTS A total of 193 papers that were published from the year 2000 onwards, were selected from four online databases: IEEE Xplore, ACM digital library, Scopus and PubMed. This study found that of the six medical tasks that exist, the diagnosis medical task was that most frequently researched, and that the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted in the selected studies. The homogeneous type was that most widely used to perform the classification task. With regard to single techniques, this mapping study found that decision trees, support vector machines and artificial neural networks were those most frequently adopted to build ensemble classifiers. In the case of the evaluation framework, the Wisconsin Breast Cancer dataset was the most frequently used by researchers to perform their experiments, while the most noticeable validation method was k-fold cross-validation. Several tools are available to perform experiments related to ensemble classification methods, such as Weka and R Software. Few researchers took into account the optimisation of the single technique of which their proposed ensemble was composed, while the grid search method was that most frequently adopted to tune the parameter settings of a single classifier. CONCLUSION This paper reports an in-depth study of the application of ensemble methods as regards breast cancer. Our results show that there are several gaps and issues and we, therefore, provide researchers in the field of breast cancer research with recommendations. Moreover, after analysing the papers found in this systematic mapping study, we discovered that the majority report positive results concerning the accuracy of ensemble classifiers when compared to the single classifiers. In order to aggregate the evidence reported in literature, it will, therefore, be necessary to perform a systematic literature review and meta-analysis in which an in-depth analysis could be conducted so as to confirm the superiority of ensemble classifiers over the classical techniques.
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Affiliation(s)
- Mohamed Hosni
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - Ibtissam Abnane
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - Ali Idri
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - Juan M Carrillo de Gea
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain.
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Suresh A, Udendhran R, Balamurgan M, Varatharajan R. A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment. J Med Syst 2019; 43:165. [PMID: 31053963 DOI: 10.1007/s10916-019-1302-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 04/22/2019] [Indexed: 10/26/2022]
Abstract
During mammogram screening, there is a higher probability that detection of cancers is missed, and more than 16 percentage of breast cancer is not detected by radiologists. This problem can be solved by employing image processing algorithms which enhances the accuracy of the diagnostic through image segmentation which reduces the misclassified malignant cancers. By employing segmentation, the unnecessary regions in the breast close to the boundary between the breast tissue and segmented pectoral muscle can be removed, therefore enhancing the accuracy the calculation as well as feature estimation. In-order to enhance the accuracy of classification, the proposed classifier integrates the decision trees and neural network into a system to report the progress of the breast cancer patients in an appropriate manner with the help of technology used in healthcare system. The proposed classifier successfully demonstrated that it achieved more accurate prediction when compared with other widely used algorithms, namely, K-Nearest Neighbors, Support Vector Machine and Naive Bayes algorithm.
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18
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Fotouhi S, Asadi S, Kattan MW. A comprehensive data level analysis for cancer diagnosis on imbalanced data. J Biomed Inform 2019; 90:103089. [DOI: 10.1016/j.jbi.2018.12.003] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2017] [Revised: 11/02/2018] [Accepted: 12/21/2018] [Indexed: 11/15/2022]
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Yassin NIR, Omran S, El Houby EMF, Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:25-45. [PMID: 29428074 DOI: 10.1016/j.cmpb.2017.12.012] [Citation(s) in RCA: 120] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 11/26/2017] [Accepted: 12/11/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. METHODS The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. RESULTS This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.
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Affiliation(s)
- Nisreen I R Yassin
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Shaimaa Omran
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Enas M F El Houby
- Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
| | - Hemat Allam
- Anaesthesia & Pain, Medical Division, National Research Centre, Dokki, Cairo 12311, Egypt.
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Vijay SAA, GaneshKumar P. Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data. J Med Syst 2018; 42:61. [DOI: 10.1007/s10916-018-0910-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 02/01/2018] [Indexed: 10/18/2022]
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Cherif W. Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.procs.2018.01.125] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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