1
|
Cheng N, Guo M, Yan F, Guo Z, Meng J, Ning K, Zhang Y, Duan Z, Han Y, Wang C. Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia. Front Psychiatry 2023; 14:1016586. [PMID: 37020730 PMCID: PMC10067917 DOI: 10.3389/fpsyt.2023.1016586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 03/01/2023] [Indexed: 04/07/2023] Open
Abstract
Objective To establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors. Methods The cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool. Results The area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877-0.926), 0.901 (95% CI: 0.874-0.923), 0.902 (95% CI: 0.876-0.924), and 0.955 (95% CI: 0.935-0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5). Conclusion Machine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application.
Collapse
Affiliation(s)
- Nuo Cheng
- Department of Clinical Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Meihao Guo
- Department of Infection Prevention and Control, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Fang Yan
- Department of Infection Prevention and Control, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Zhengjun Guo
- Henan Mental Disease Prevention and Control Center, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Jun Meng
- Editorial Department of Journal of Clinical Psychosomatic Diseases, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Kui Ning
- Department of Medical Administration, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Yanping Zhang
- Department of Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Zitian Duan
- The Seventh Psychiatric Department, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Yong Han
- Henan Key Laboratory of Biological Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- *Correspondence: Han Yong,
| | - Changhong Wang
- Department of Clinical Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Wang Changhong,
| |
Collapse
|
2
|
Sayilgan E, Yuce Y, Isler Y. Investigating the Effect of Flickering Frequency Pair and Mother Wavelet Selection in Steady-State Visually-Evoked Potentials on Two-Command Brain-Computer Interfaces. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.04.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
3
|
Accuracy versus reliability-based modelling approaches for medical decision making. Comput Biol Med 2021; 141:105138. [PMID: 34929467 DOI: 10.1016/j.compbiomed.2021.105138] [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: 11/02/2021] [Revised: 12/11/2021] [Accepted: 12/11/2021] [Indexed: 11/21/2022]
Abstract
Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.
Collapse
|
4
|
Surucu M, Isler Y, Perc M, Kara R. Convolutional neural networks predict the onset of paroxysmal atrial fibrillation: Theory and applications. CHAOS (WOODBURY, N.Y.) 2021; 31:113119. [PMID: 34881615 DOI: 10.1063/5.0069272] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
In this study, we aimed to detect paroxysmal atrial fibrillation episodes before they occur so that patients can take precautions before putting their and others' lives in potentially life-threatening danger. We used the atrial fibrillation prediction database, open data from PhysioNet, and assembled our process based on convolutional neural networks. Conventional heart rate variability features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations, time-frequency-domain measures using wavelet transform, and nonlinear Poincaré plot measures. In addition, we also applied an alternative heart rate normalization, which gave promising results only in a few studies, before calculating these heart rate variability features. We used these features directly and their normalized versions using min-max normalization and z-score normalization methods. Thus, heart rate variability features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural network classifier. We tuned the classifiers' hyperparameters using 90% of feature sets and tested the classifiers' performances using 10% of feature sets. The proposed approach resulted in 87.76% accuracy, 91.30% precision, 80.04% recall, and 87.50% f1-score in heart rate variability with z-score feature normalization. When the heart rate normalization was also utilized, the suggested method gave 100% accuracy, 100% precision, 100% recall, and 100% f1-score in heart rate variability with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. By comparing the existing studies, we conclude that our approach provides a much better tool to determine a near-future paroxysmal atrial fibrillation episode. However, although the achieved benchmarks are impressive, we note that the approach needs to be supported by other studies and on other datasets before clinical trials.
Collapse
Affiliation(s)
- M Surucu
- Department of Computer Engineering, Duzce University, 81620 Duzce, Turkey
| | - Y Isler
- Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, 35620 Izmir, Turkey
| | - M Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia
| | - R Kara
- Department of Computer Engineering, Duzce University, 81620 Duzce, Turkey
| |
Collapse
|
5
|
Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:9950332. [PMID: 33995524 PMCID: PMC8096561 DOI: 10.1155/2021/9950332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 03/26/2021] [Accepted: 04/09/2021] [Indexed: 11/25/2022]
Abstract
Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals' lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whether they are depressed or not. Depression, most of the time, cooccurs with anxiety and leads to suicide in few cases, among the employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. This is why this work aims to analyze the IT employees who are mostly working with targets. The artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. This study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from IT professionals. This study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. The results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification.
Collapse
|
6
|
Çınar A, Tuncer SA. Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks. Comput Methods Biomech Biomed Engin 2020; 24:203-214. [PMID: 32955928 DOI: 10.1080/10255842.2020.1821192] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.
Collapse
Affiliation(s)
- Ahmet Çınar
- Faculty of Engineering, Computer Engineering, Fırat University, Elazığ, Turkey
| | - Seda Arslan Tuncer
- Faculty of Engineering, Software Engineering, Fırat University, Elazığ, Turkey
| |
Collapse
|
7
|
Tripoliti EE, Karanasiou GS, Kalatzis FG, Bechlioulis A, Goletsis Y, Naka K, Fotiadis DI. HEARTEN KMS - A knowledge management system targeting the management of patients with heart failure. J Biomed Inform 2019; 94:103203. [PMID: 31071455 DOI: 10.1016/j.jbi.2019.103203] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 05/03/2019] [Accepted: 05/04/2019] [Indexed: 11/19/2022]
Abstract
The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multi-parametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78% to 95% depending on the module/functionality.
Collapse
Affiliation(s)
- Evanthia E Tripoliti
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Georgia S Karanasiou
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Fanis G Kalatzis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece
| | - Aris Bechlioulis
- 2(nd) Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece.
| | - Yorgos Goletsis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece; Department of Economics, University of Ioannina, GR 45110 Ioannina, Greece.
| | - Katerina Naka
- 2(nd) Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece
| | - Dimitrios I Fotiadis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, GR 45110 Ioannina, Greece; Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece.
| |
Collapse
|
8
|
Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.10.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
|
9
|
Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification. Korean Circ J 2019; 49:629-639. [PMID: 31074221 PMCID: PMC6597456 DOI: 10.4070/kcj.2018.0446] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 01/28/2019] [Accepted: 02/19/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840-0.845) and 0.889 (0.887-0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797-0.803], 0.847 [0.844-0.850]) and RF (0.807 [0.804-0.810], 0.853 [0.850-0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819-0.823) and 0.850 (0.848-0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.
Collapse
Affiliation(s)
- Joon Myoung Kwon
- Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Korea
| | - Kyung Hee Kim
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea.
| | - Ki Hyun Jeon
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Hyue Mee Kim
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Min Jeong Kim
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Sung Min Lim
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Pil Sang Song
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Jinsik Park
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Rak Kyeong Choi
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| | - Byung Hee Oh
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea
| |
Collapse
|
10
|
|
11
|
|
12
|
Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI. Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques. Comput Struct Biotechnol J 2016; 15:26-47. [PMID: 27942354 PMCID: PMC5133661 DOI: 10.1016/j.csbj.2016.11.001] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 11/12/2016] [Accepted: 11/14/2016] [Indexed: 10/26/2022] Open
Abstract
Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.
Collapse
Affiliation(s)
- Evanthia E. Tripoliti
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece
| | - Theofilos G. Papadopoulos
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece
| | - Georgia S. Karanasiou
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece
| | - Katerina K. Naka
- Michaelidion Cardiac Center, University of Ioannina, GR 45110 Ioannina, Greece
- 2nd Department of Cardiology, University of Ioannina, GR 45110 Ioannina, Greece
| | - Dimitrios I. Fotiadis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, GR 45110 Ioannina, Greece
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece
| |
Collapse
|