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Choi HS, Yoon S, Kim J, Seo H, Choi JK. Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure. SENSORS (BASEL, SWITZERLAND) 2024; 24:4765. [PMID: 39123811 PMCID: PMC11314829 DOI: 10.3390/s24154765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 06/21/2024] [Accepted: 06/26/2024] [Indexed: 08/12/2024]
Abstract
This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This approach can be utilized to improve a user's rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.
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Affiliation(s)
- Ho Seon Choi
- Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea;
| | - Seokjin Yoon
- Department of Software, Sejong University, Seoul 05006, Republic of Korea;
| | - Jangkyum Kim
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
| | - Hyeonseok Seo
- School of Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon 34141, Republic of Korea;
| | - Jun Kyun Choi
- School of Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon 34141, Republic of Korea;
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2
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Pazo M, Gerassis S, Araújo M, Margarida Antunes I, Rigueira X. Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172340. [PMID: 38608909 DOI: 10.1016/j.scitotenv.2024.172340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/05/2024] [Accepted: 04/07/2024] [Indexed: 04/14/2024]
Abstract
Tackling the impact of missing data in water management is crucial to ensure the reliability of scientific research that informs decision-making processes in public health. The goal of this study is to ascertain the root causes associated with cyanobacteria proliferation under major missing data scenarios. For this purpose, a dynamic missing data management methodology is proposed using Bayesian Machine Learning for accurate surface water quality prediction of a river from Limia basin (Spain). The methodology used entails a sequence of analytical steps, starting with data pre-processing, followed by the selection of a reliable dynamic Bayesian missing value prediction system, leading finally to a supervised analysis of the behavioral patterns exhibited by cyanobacteria. For that, a total of 2,118,844 data points were used, with 205,316 (9.69 %) missing values identified. The machine learning testing showed the iterative structural expectation maximization (SEM) as the best performing algorithm, above the dynamic imputation (DI) and entropy-based dynamic imputation methods (EBDI), enhancing in some cases the accuracy of imputations by approximately 50 % in R2, RMSE, NRMSE, and logarithmic loss values. These findings can impact how data on water quality is being processed and studied, thus, opening the door for more reliable water management strategies that better inform public health decisions.
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Affiliation(s)
- M Pazo
- CINTECX, Universidade de Vigo, Grupo de Xestión Segura e Sostible de Recursos Minerais, Dpto. De Enxeñaría dos Recursos Naturais e Medio Ambiente, 36310 Vigo, Spain.
| | - S Gerassis
- CINTECX, Universidade de Vigo, Grupo de Xestión Segura e Sostible de Recursos Minerais, Dpto. De Enxeñaría dos Recursos Naturais e Medio Ambiente, 36310 Vigo, Spain
| | - M Araújo
- CINTECX, Universidade de Vigo, Grupo de Xestión Segura e Sostible de Recursos Minerais, Dpto. De Enxeñaría dos Recursos Naturais e Medio Ambiente, 36310 Vigo, Spain
| | - I Margarida Antunes
- Institute of Earth Sciences (ICT), Pole of University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - X Rigueira
- CINTECX, Universidade de Vigo, Grupo de Xestión Segura e Sostible de Recursos Minerais, Dpto. De Enxeñaría dos Recursos Naturais e Medio Ambiente, 36310 Vigo, Spain
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3
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Yan R, Liu X, Dutcher JM, Tumminia MJ, Villalba D, Cohen S, Creswell JD, Creswell K, Mankoff J, Dey AK, Doryab A. Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study. JMIR AI 2024; 3:e47194. [PMID: 38875675 PMCID: PMC11066747 DOI: 10.2196/47194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 10/31/2023] [Accepted: 02/15/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Biobehavioral rhythms are biological, behavioral, and psychosocial processes with repeating cycles. Abnormal rhythms have been linked to various health issues, such as sleep disorders, obesity, and depression. OBJECTIVE This study aims to identify links between productivity and biobehavioral rhythms modeled from passively collected mobile data streams. METHODS In this study, we used a multimodal mobile sensing data set consisting of data collected from smartphones and Fitbits worn by 188 college students over a continuous period of 16 weeks. The participants reported their self-evaluated daily productivity score (ranging from 0 to 4) during weeks 1, 6, and 15. To analyze the data, we modeled cyclic human behavior patterns based on multimodal mobile sensing data gathered during weeks 1, 6, 15, and the adjacent weeks. Our methodology resulted in the creation of a rhythm model for each sensor feature. Additionally, we developed a correlation-based approach to identify connections between rhythm stability and high or low productivity levels. RESULTS Differences exist in the biobehavioral rhythms of high- and low-productivity students, with those demonstrating greater rhythm stability also exhibiting higher productivity levels. Notably, a negative correlation (C=-0.16) was observed between productivity and the SE of the phase for the 24-hour period during week 1, with a higher SE indicative of lower rhythm stability. CONCLUSIONS Modeling biobehavioral rhythms has the potential to quantify and forecast productivity. The findings have implications for building novel cyber-human systems that align with human beings' biobehavioral rhythms to improve health, well-being, and work performance.
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Affiliation(s)
- Runze Yan
- University of Virginia, Charlottesville, VA, United States
| | - Xinwen Liu
- Carnegie Mellon University, Pittsburgh, PA, United States
| | | | | | | | - Sheldon Cohen
- Carnegie Mellon University, Pittsburgh, PA, United States
| | | | - Kasey Creswell
- Carnegie Mellon University, Pittsburgh, PA, United States
| | | | - Anind K Dey
- University of Washington, Seattle, WA, United States
| | - Afsaneh Doryab
- University of Virginia, Charlottesville, VA, United States
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Muludi K, Setianingsih R, Sholehurrohman R, Junaidi A. Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification. PeerJ Comput Sci 2024; 10:e1968. [PMID: 38660203 PMCID: PMC11042039 DOI: 10.7717/peerj-cs.1968] [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: 11/01/2023] [Accepted: 03/07/2024] [Indexed: 04/26/2024]
Abstract
The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy membership function. This showed that the combination of nearest neighbor data and fuzzy membership function was more effective in handling missing values and improving classification accuracy.
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Affiliation(s)
- Kurnia Muludi
- Informatics and Business Institute Darmajaya, Bandar Lampung, Lampung Province, Indonesia
| | - Revita Setianingsih
- Computer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia
| | - Ridho Sholehurrohman
- Computer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia
| | - Akmal Junaidi
- Computer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia
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Zheng H, Sherazi SWA, Lee JY. A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data. Front Cardiovasc Med 2024; 11:1276608. [PMID: 38566962 PMCID: PMC10986180 DOI: 10.3389/fcvm.2024.1276608] [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: 09/12/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Background and objectives Hypertension is one of the most serious risk factors and the leading cause of mortality in patients with cardiovascular diseases (CVDs). It is necessary to accurately predict the mortality of patients suffering from CVDs with hypertension. Therefore, this paper proposes a novel cost-sensitive deep neural network (CSDNN)-based mortality prediction model for out-of-hospital acute myocardial infarction (AMI) patients with hypertension on imbalanced data. Methods The synopsis of our research is as follows. First, the experimental data is extracted from the Korea Acute Myocardial Infarction Registry-National Institutes of Health (KAMIR-NIH) and preprocessed with several approaches. Then the imbalanced experimental dataset is divided into training data (80%) and test data (20%). After that, we design the proposed CSDNN-based mortality prediction model, which can solve the skewed class distribution between the majority and minority classes in the training data. The threshold moving technique is also employed to enhance the performance of the proposed model. Finally, we evaluate the performance of the proposed model using the test data and compare it with other commonly used machine learning (ML) and data sampling-based ensemble models. Moreover, the hyperparameters of all models are optimized through random search strategies with a 5-fold cross-validation approach. Results and discussion In the result, the proposed CSDNN model with the threshold moving technique yielded the best results on imbalanced data. Additionally, our proposed model outperformed the best ML model and the classic data sampling-based ensemble model with an AUC of 2.58% and 2.55% improvement, respectively. It aids in decision-making and offers a precise mortality prediction for AMI patients with hypertension.
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Affiliation(s)
- Huilin Zheng
- Department of Computer Science, Chungbuk National University, Cheongju, Republic of Korea
- College of Computer Science and Engineering, Guilin University of Technology, Guilin, China
| | | | - Jong Yun Lee
- Department of Computer Science, Chungbuk National University, Cheongju, Republic of Korea
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Wang M, Ye XW, Ying XH, Jia JD, Ding Y, Zhang D, Sun F. Data Imputation of Soil Pressure on Shield Tunnel Lining Based on Random Forest Model. SENSORS (BASEL, SWITZERLAND) 2024; 24:1560. [PMID: 38475093 DOI: 10.3390/s24051560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 02/24/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
With the advancement of engineering techniques, underground shield tunneling projects have also started incorporating emerging technologies to monitor the forces and displacements during the construction and operation phases of shield tunnels. Monitoring devices installed on the tunnel segment components generate a large amount of data. However, due to various factors, data may be missing. Hence, the completion of the incomplete data is imperative to ensure the utmost safety of the engineering project. In this research, a missing data imputation technique utilizing Random Forest (RF) is introduced. The optimal combination of the number of decision trees, maximum depth, and number of features in the RF is determined by minimizing the Mean Squared Error (MSE). Subsequently, complete soil pressure data are artificially manipulated to create incomplete datasets with missing rates of 20%, 40%, and 60%. A comparative analysis of the imputation results using three methods-median, mean, and RF-reveals that this proposed method has the smallest imputation error. As the missing rate increases, the mean squared error of the Random Forest method and the other two methods also increases, with a maximum difference of about 70%. This indicates that the random forest method is suitable for imputing monitoring data.
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Affiliation(s)
- Min Wang
- Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Xiao-Wei Ye
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
| | - Xin-Hong Ying
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
| | - Jin-Dian Jia
- Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yang Ding
- Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China
| | - Di Zhang
- China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China
| | - Feng Sun
- China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China
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Alcazar O, Chuang ST, Ren G, Ogihara M, Webb-Robertson BJM, Nakayasu ES, Buchwald P, Abdulreda MH. A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579673. [PMID: 38405796 PMCID: PMC10888829 DOI: 10.1101/2024.02.09.579673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk. Methods Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA. Results All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated. Conclusions Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.
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8
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Ou H, Yao Y, He Y. Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network. SENSORS (BASEL, SWITZERLAND) 2024; 24:1112. [PMID: 38400270 PMCID: PMC10893362 DOI: 10.3390/s24041112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/27/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling.
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Affiliation(s)
| | - Yunan Yao
- School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China; (H.O.); (Y.H.)
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Cruse K, Baibakova V, Abdelsamie M, Hong K, Bartel CJ, Trewartha A, Jain A, Sutter-Fella CM, Ceder G. Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO 3. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2024; 36:772-785. [PMID: 38282687 PMCID: PMC10809418 DOI: 10.1021/acs.chemmater.3c02203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/08/2023] [Accepted: 12/08/2023] [Indexed: 01/30/2024]
Abstract
We used data-driven methods to understand the formation of impurity phases in BiFeO3 thin-film synthesis through the sol-gel technique. Using a high-quality dataset of 331 synthesis procedures and outcomes extracted manually from 177 scientific articles, we trained decision tree models that reinforce important experimental heuristics for the avoidance of phase impurities but ultimately show limited predictive capability. We find that several important synthesis features, identified by our model, are often not reported in the literature. To test our ability to correctly impute missing synthesis parameters, we attempted to reproduce nine syntheses from the literature with varying degrees of "missingness". We demonstrate how a text-mined dataset can be made useful by informing new controlled experiments and forming a better understanding for impurity phase formation in this complex oxide system.
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Affiliation(s)
- Kevin Cruse
- Department
of Materials Science & Engineering, University of California, Berkeley, California 94720, United States
- Materials
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
| | - Viktoriia Baibakova
- Energy
Technologies Area, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
| | - Maged Abdelsamie
- Material
Science and Engineering Department, King
Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia
- Interdisciplinary
Research Center for Intelligent Manufacturing and Robotics, KFUPM, Dhahran 31261, Saudi Arabia
| | - Kootak Hong
- Chemical
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
- Department
of Materials Science and Engineering, Chonnam
National University, Gwangju 61186, Republic
of Korea
| | - Christopher J. Bartel
- Department
of Materials Science & Engineering, University of California, Berkeley, California 94720, United States
- Materials
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
- Department
of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Amalie Trewartha
- Department
of Materials Science & Engineering, University of California, Berkeley, California 94720, United States
- Materials
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
- Energy
and Materials, Toyota Research Institute, Los Altos, California 94022, United States
| | - Anubhav Jain
- Energy
Technologies Area, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
| | - Carolin M. Sutter-Fella
- Molecular
Foundry Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
| | - Gerbrand Ceder
- Department
of Materials Science & Engineering, University of California, Berkeley, California 94720, United States
- Materials
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
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Jacob Junior AFL, do Carmo FA, de Santana AL, Santana EEC, Lobato FMF. EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. PLoS One 2024; 19:e0297147. [PMID: 38241256 PMCID: PMC10798481 DOI: 10.1371/journal.pone.0297147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 12/28/2023] [Indexed: 01/21/2024] Open
Abstract
Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes. Movie classification is a didactic example since it can be "drama" and "bibliography" simultaneously. One of the most usual missing data treatment methods is data imputation, which seeks plausible values to fill in the missing ones. In this scenario, we propose a novel imputation method based on a multi-objective genetic algorithm for optimizing multiple data imputations called Multiple Imputation of Multi-label Classification data with a genetic algorithm, or simply EvoImp. We applied the proposed method in multi-label learning and evaluated its performance using six synthetic databases, considering various missing values distribution scenarios. The method was compared with other state-of-the-art imputation strategies, such as K-Means Imputation (KMI) and weighted K-Nearest Neighbors Imputation (WKNNI). The results proved that the proposed method outperformed the baseline in all the scenarios by achieving the best evaluation measures considering the Exact Match, Accuracy, and Hamming Loss. The superior results were constant in different dataset domains and sizes, demonstrating the EvoImp robustness. Thus, EvoImp represents a feasible solution to missing data treatment for multi-label learning.
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Affiliation(s)
- Antonio Fernando Lavareda Jacob Junior
- Graduate Program in Electrical Engineering (PPGEE), Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil
- Graduate Program in Computer Engineering and Systems (PECS), State University of Maranhão (UEMA), São Luís, Maranhão, Brazil
| | - Fabricio Almeida do Carmo
- Graduate Program in Computer Engineering and Systems (PECS), State University of Maranhão (UEMA), São Luís, Maranhão, Brazil
| | | | - Ewaldo Eder Carvalho Santana
- Graduate Program in Electrical Engineering (PPGEE), Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil
- Graduate Program in Computer Engineering and Systems (PECS), State University of Maranhão (UEMA), São Luís, Maranhão, Brazil
| | - Fabio Manoel Franca Lobato
- Graduate Program in Computer Engineering and Systems (PECS), State University of Maranhão (UEMA), São Luís, Maranhão, Brazil
- Institute of Engineering and Geosciences, Federal University of Western Pará (UFOPA), Santarém, Pará, Brazil
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Ogasawara T, Mukaino M, Matsunaga K, Wada Y, Suzuki T, Aoshima Y, Furuzawa S, Kono Y, Saitoh E, Yamaguchi M, Otaka Y, Tsukada S. Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data. Front Bioeng Biotechnol 2024; 11:1285945. [PMID: 38234303 PMCID: PMC10791943 DOI: 10.3389/fbioe.2023.1285945] [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: 08/30/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices. Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies. Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman's rank correlation coefficient (R) of 0.56 and -0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics. Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.
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Affiliation(s)
- Takayuki Ogasawara
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Masahiko Mukaino
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
- Department of Rehabilitation Medicine, Hokkaido University Hospital, Sapporo, Japan
| | | | - Yoshitaka Wada
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Takuya Suzuki
- Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan
| | - Yasushi Aoshima
- Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan
| | - Shotaro Furuzawa
- Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan
| | - Yuji Kono
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
- Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan
| | - Eiichi Saitoh
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Masumi Yamaguchi
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
| | - Yohei Otaka
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Shingo Tsukada
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
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12
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Weng X, Song H, Lin Y, Wu Y, Zhang X, Liu B, Yang J. A joint learning method for incomplete and imbalanced data in electronic health record based on generative adversarial networks. Comput Biol Med 2024; 168:107687. [PMID: 38007974 DOI: 10.1016/j.compbiomed.2023.107687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/07/2023] [Accepted: 11/06/2023] [Indexed: 11/28/2023]
Abstract
Electronic health records (EHR), present challenges of incomplete and imbalanced data in clinical predictions. Previous studies addressed these two issues with two-step separately, which caused the decrease in the performance of prediction tasks. In this paper, we propose a unified framework to simultaneously addresses the challenges of incomplete and imbalanced data in EHR. Based on the framework, we develop a model called Missing Value Imputation and Imbalanced Learning Generative Adversarial Network (MVIIL-GAN). We use MVIIL-GAN to perform joint learning on the imputation process of high missing rate data and the conditional generation process of EHR data. The joint learning is achieved by introducing two discriminators to distinguish the fake data from the generated data at sample-level and variable-level. MVIIL-GAN integrate the missing values imputation and data generation in one step, improving the consistency of parameter optimization and the performance of prediction tasks. We evaluate our framework using the public dataset MIMIC-IV with high missing rates data and imbalanced data. Experimental results show that MVIIL-GAN outperforms existing methods in prediction performance. The implementation of MVIIL-GAN can be found at https://github.com/Peroxidess/MVIIL-GAN.
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Affiliation(s)
- Xutao Weng
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Hong Song
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yucong Lin
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - You Wu
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Xi Zhang
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Bowen Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Jian Yang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
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13
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Chiu CH, Ke SW, Tsai CF, Lin WC, Huang MW, Ko YH. Deep learning based decision tree ensembles for incomplete medical datasets. Technol Health Care 2024; 32:75-87. [PMID: 37248924 DOI: 10.3233/thc-220514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
BACKGROUND In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose. OBJECTIVE To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles. METHOD Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison. RESULTS The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method. CONCLUSION The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates.
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Affiliation(s)
- Chien-Hung Chiu
- Division of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Shih-Wen Ke
- Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Chih-Fong Tsai
- Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Wei-Chao Lin
- Division of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- Department of Information Management, Chang Gung University, Taoyuan, Taiwan
| | - Min-Wei Huang
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan
- Graduate Institute of Rehabilitation Science, Department of Physical Therapy, China Medical University, Taichung, Taiwan
| | - Yi-Hsiu Ko
- Department of Information Management, National Central University, Taoyuan, Taiwan
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14
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Huang MW, Tsai CF, Tsui SC, Lin WC. Combining data discretization and missing value imputation for incomplete medical datasets. PLoS One 2023; 18:e0295032. [PMID: 38033140 PMCID: PMC10688879 DOI: 10.1371/journal.pone.0295032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 11/14/2023] [Indexed: 12/02/2023] Open
Abstract
Data discretization aims to transform a set of continuous features into discrete features, thus simplifying the representation of information and making it easier to understand, use, and explain. In practice, users can take advantage of the discretization process to improve knowledge discovery and data analysis on medical domain problem datasets containing continuous features. However, certain feature values were frequently missing. Many data-mining algorithms cannot handle incomplete datasets. In this study, we considered the use of both discretization and missing-value imputation to process incomplete medical datasets, examining how the order of discretization and missing-value imputation combined influenced performance. The experimental results were obtained using seven different medical domain problem datasets: two discretizers, including the minimum description length principle (MDLP) and ChiMerge; three imputation methods, including the mean/mode, classification and regression tree (CART), and k-nearest neighbor (KNN) methods; and two classifiers, including support vector machines (SVM) and the C4.5 decision tree. The results show that a better performance can be obtained by first performing discretization followed by imputation, rather than vice versa. Furthermore, the highest classification accuracy rate was achieved by combining ChiMerge and KNN with SVM.
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Affiliation(s)
- Min-Wei Huang
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan
- Department of Physical Therapy and Graduate Institute of Rehabilitation Science, China Medical University, Taichung, Taiwan
| | - Chih-Fong Tsai
- Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Shu-Ching Tsui
- Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Wei-Chao Lin
- Department of Digital Financial Technology, Chang Gung University, Taoyuan, Taiwan
- Department of Information Management, Chang Gung University, Taoyuan, Taiwan
- Division of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
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15
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Gan Q, Gong L, Hu D, Jiang Y, Ding X. A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset. SENSORS (BASEL, SWITZERLAND) 2023; 23:8678. [PMID: 37960379 PMCID: PMC10650138 DOI: 10.3390/s23218678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/07/2023] [Accepted: 10/18/2023] [Indexed: 11/15/2023]
Abstract
Batch process monitoring datasets usually contain missing data, which decreases the performance of data-driven modeling for fault identification and optimal control. Many methods have been proposed to impute missing data; however, they do not fulfill the need for data quality, especially in sensor datasets with different types of missing data. We propose a hybrid missing data imputation method for batch process monitoring datasets with multi-type missing data. In this method, the missing data is first classified into five categories based on the continuous missing duration and the number of variables missing simultaneously. Then, different categories of missing data are step-by-step imputed considering their unique characteristics. A combination of three single-dimensional interpolation models is employed to impute transient isolated missing values. An iterative imputation based on a multivariate regression model is designed for imputing long-term missing variables, and a combination model based on single-dimensional interpolation and multivariate regression is proposed for imputing short-term missing variables. The Long Short-Term Memory (LSTM) model is utilized to impute both short-term and long-term missing samples. Finally, a series of experiments for different categories of missing data were conducted based on a real-world batch process monitoring dataset. The results demonstrate that the proposed method achieves higher imputation accuracy than other comparative methods.
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Affiliation(s)
- Qihong Gan
- Informatization Construction and Management Office, Sichuan University, Chengdu 610065, China;
- Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province, Chengdu 610065, China; (L.G.); (D.H.); (Y.J.)
| | - Lang Gong
- Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province, Chengdu 610065, China; (L.G.); (D.H.); (Y.J.)
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Dasha Hu
- Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province, Chengdu 610065, China; (L.G.); (D.H.); (Y.J.)
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Yuming Jiang
- Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province, Chengdu 610065, China; (L.G.); (D.H.); (Y.J.)
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Xuefeng Ding
- Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province, Chengdu 610065, China; (L.G.); (D.H.); (Y.J.)
- College of Computer Science, Sichuan University, Chengdu 610065, China
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16
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Shadbahr T, Roberts M, Stanczuk J, Gilbey J, Teare P, Dittmer S, Thorpe M, Torné RV, Sala E, Lió P, Patel M, Preller J, Rudd JHF, Mirtti T, Rannikko AS, Aston JAD, Tang J, Schönlieb CB. The impact of imputation quality on machine learning classifiers for datasets with missing values. COMMUNICATIONS MEDICINE 2023; 3:139. [PMID: 37803172 PMCID: PMC10558448 DOI: 10.1038/s43856-023-00356-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/13/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance. METHODS We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. RESULTS The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. CONCLUSIONS It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.
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Affiliation(s)
- Tolou Shadbahr
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK.
| | - Jan Stanczuk
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Julian Gilbey
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Philip Teare
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK
| | - Sören Dittmer
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- ZeTeM, University of Bremen, Bremen, Germany
| | - Matthew Thorpe
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Ramon Viñas Torné
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Pietro Lió
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Mishal Patel
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK
- Clinical Pharmacology & Safety Sciences, AstraZeneca, Cambridge, UK
| | - Jacobus Preller
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - James H F Rudd
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Tuomas Mirtti
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Antti Sakari Rannikko
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Department of Urology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - John A D Aston
- Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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17
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Jiang Z, Han Y, Xu L, Shi D, Liu R, Ouyang J, Cai F. The NEAT Equating Via Chaining Random Forests in the Context of Small Sample Sizes: A Machine-Learning Method. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT 2023; 83:984-1006. [PMID: 37663533 PMCID: PMC10470159 DOI: 10.1177/00131644221120899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
The part of responses that is absent in the nonequivalent groups with anchor test (NEAT) design can be managed to a planned missing scenario. In the context of small sample sizes, we present a machine learning (ML)-based imputation technique called chaining random forests (CRF) to perform equating tasks within the NEAT design. Specifically, seven CRF-based imputation equating methods are proposed based on different data augmentation methods. The equating performance of the proposed methods is examined through a simulation study. Five factors are considered: (a) test length (20, 30, 40, 50), (b) sample size per test form (50 versus 100), (c) ratio of common/anchor items (0.2 versus 0.3), and (d) equivalent versus nonequivalent groups taking the two forms (no mean difference versus a mean difference of 0.5), and (e) three different types of anchors (random, easy, and hard), resulting in 96 conditions. In addition, five traditional equating methods, (1) Tucker method; (2) Levine observed score method; (3) equipercentile equating method; (4) circle-arc method; and (5) concurrent calibration based on Rasch model, were also considered, plus seven CRF-based imputation equating methods for a total of 12 methods in this study. The findings suggest that benefiting from the advantages of ML techniques, CRF-based methods that incorporate the equating result of the Tucker method, such as IMP_total_Tucker, IMP_pair_Tucker, and IMP_Tucker_cirlce methods, can yield more robust and trustable estimates for the "missingness" in an equating task and therefore result in more accurate equated scores than other counterparts in short-length tests with small samples.
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Affiliation(s)
- Zhehan Jiang
- Peking University Health Science Center, Beijing, China
| | - Yuting Han
- Peking University Health Science Center, Beijing, China
| | - Lingling Xu
- Peking University Health Science Center, Beijing, China
| | - Dexin Shi
- University of South Carolina, Columbia, USA
| | - Ren Liu
- University of California, Merced, USA
| | | | - Fen Cai
- Peking University Health Science Center, Beijing, China
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18
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Goh WWB, Hui HWH, Wong L. How missing value imputation is confounded with batch effects and what you can do about it. Drug Discov Today 2023; 28:103661. [PMID: 37301250 DOI: 10.1016/j.drudis.2023.103661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/12/2023]
Abstract
In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.
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Affiliation(s)
- Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore; Center for Biomedical Informatics, Nanyang Technological University, Singapore.
| | - Harvard Wai Hann Hui
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore; Department of Pathology, National University of Singapore, Singapore.
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19
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Senadheera I, Larssen BC, Mak-Yuen YYK, Steinfort S, Carey LM, Alahakoon D. Profiling Somatosensory Impairment after Stroke: Characterizing Common "Fingerprints" of Impairment Using Unsupervised Machine Learning-Based Cluster Analysis of Quantitative Measures of the Upper Limb. Brain Sci 2023; 13:1253. [PMID: 37759854 PMCID: PMC10526214 DOI: 10.3390/brainsci13091253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Altered somatosensory function is common among stroke survivors, yet is often poorly characterized. Methods of profiling somatosensation that illustrate the variability in impairment within and across different modalities remain limited. We aimed to characterize post-stroke somatosensation profiles ("fingerprints") of the upper limb using an unsupervised machine learning cluster analysis to capture hidden relationships between measures of touch, proprioception, and haptic object recognition. Raw data were pooled from six studies where multiple quantitative measures of upper limb somatosensation were collected from stroke survivors (n = 207) using the Tactile Discrimination Test (TDT), Wrist Position Sense Test (WPST) and functional Tactile Object Recognition Test (fTORT) on the contralesional and ipsilesional upper limbs. The Growing Self Organizing Map (GSOM) unsupervised machine learning algorithm was used to generate a topology-preserving two-dimensional mapping of the pooled data and then separate it into clusters. Signature profiles of somatosensory impairment across two modalities (TDT and WPST; n = 203) and three modalities (TDT, WPST, and fTORT; n = 141) were characterized for both hands. Distinct impairment subgroups were identified. The influence of background and clinical variables was also modelled. The study provided evidence of the utility of unsupervised cluster analysis that can profile stroke survivor signatures of somatosensory impairment, which may inform improved diagnosis and characterization of impairment patterns.
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Affiliation(s)
- Isuru Senadheera
- Centre for Data Analytics and Cognition, La Trobe Business School, La Trobe University, Melbourne, VIC 3086, Australia;
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
| | - Beverley C. Larssen
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
- Department of Physical Therapy, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Yvonne Y. K. Mak-Yuen
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3086, Australia
- Department of Occupational Therapy, St. Vincent’s Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Sarah Steinfort
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3086, Australia
| | - Leeanne M. Carey
- Occupational Therapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, VIC 3086, Australia; (B.C.L.); (Y.Y.K.M.-Y.); (S.S.); (L.M.C.)
- Neurorehabilitation and Recovery, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3086, Australia
| | - Damminda Alahakoon
- Centre for Data Analytics and Cognition, La Trobe Business School, La Trobe University, Melbourne, VIC 3086, Australia;
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20
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Zuber S, Bechtiger L, Bodelet JS, Golin M, Heumann J, Kim JH, Klee M, Mur J, Noll J, Voll S, O’Keefe P, Steinhoff A, Zölitz U, Muniz-Terrera G, Shanahan L, Shanahan MJ, Hofer SM. An integrative approach for the analysis of risk and health across the life course: challenges, innovations, and opportunities for life course research. DISCOVER SOCIAL SCIENCE AND HEALTH 2023; 3:14. [PMID: 37469576 PMCID: PMC10352429 DOI: 10.1007/s44155-023-00044-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/26/2023] [Indexed: 07/21/2023]
Abstract
Life course epidemiology seeks to understand the intricate relationships between risk factors and health outcomes across different stages of life to inform prevention and intervention strategies to optimize health throughout the lifespan. However, extant evidence has predominantly been based on separate analyses of data from individual birth cohorts or panel studies, which may not be sufficient to unravel the complex interplay of risk and health across different contexts. We highlight the importance of a multi-study perspective that enables researchers to: (a) Compare and contrast findings from different contexts and populations, which can help identify generalizable patterns and context-specific factors; (b) Examine the robustness of associations and the potential for effect modification by factors such as age, sex, and socioeconomic status; and (c) Improve statistical power and precision by pooling data from multiple studies, thereby allowing for the investigation of rare exposures and outcomes. This integrative framework combines the advantages of multi-study data with a life course perspective to guide research in understanding life course risk and resilience on adult health outcomes by: (a) Encouraging the use of harmonized measures across studies to facilitate comparisons and synthesis of findings; (b) Promoting the adoption of advanced analytical techniques that can accommodate the complexities of multi-study, longitudinal data; and (c) Fostering collaboration between researchers, data repositories, and funding agencies to support the integration of longitudinal data from diverse sources. An integrative approach can help inform the development of individualized risk scores and personalized interventions to promote health and well-being at various life stages.
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Affiliation(s)
- Sascha Zuber
- Institute On Aging & Lifelong Health, University of Victoria, Victoria, BC Canada
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Geneva, Switzerland
| | - Laura Bechtiger
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
| | | | - Marta Golin
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
| | - Jens Heumann
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
| | - Jung Hyun Kim
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Matthias Klee
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jure Mur
- University of Edinburgh, Edinburgh, Scotland
| | - Jennie Noll
- Pennsylvania State University, State College, PA USA
| | - Stacey Voll
- Institute On Aging & Lifelong Health, University of Victoria, Victoria, BC Canada
| | - Patrick O’Keefe
- Department of Neurology, Oregon Health & Science University, Portland, OR USA
| | - Annekatrin Steinhoff
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Ulf Zölitz
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
| | | | - Lilly Shanahan
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
- Department of Psychology, University of Zürich, Zürich, Switzerland
| | - Michael J. Shanahan
- Jacobs Center for Productive Youth Development, University of Zürich, Zürich, Switzerland
- Department of Sociology, University of Zürich, Zürich, Switzerland
| | - Scott M. Hofer
- Institute On Aging & Lifelong Health, University of Victoria, Victoria, BC Canada
- Department of Neurology, Oregon Health & Science University, Portland, OR USA
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21
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Kong W, Wong BJH, Hui HWH, Lim KP, Wang Y, Wong L, Goh WWB. ProJect: a powerful mixed-model missing value imputation method. Brief Bioinform 2023:bbad233. [PMID: 37419612 DOI: 10.1093/bib/bbad233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/24/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023] Open
Abstract
Missing values (MVs) can adversely impact data analysis and machine-learning model development. We propose a novel mixed-model method for missing value imputation (MVI). This method, ProJect (short for Protein inJection), is a powerful and meaningful improvement over existing MVI methods such as Bayesian principal component analysis (PCA), probabilistic PCA, local least squares and quantile regression imputation of left-censored data. We rigorously tested ProJect on various high-throughput data types, including genomics and mass spectrometry (MS)-based proteomics. Specifically, we utilized renal cancer (RC) data acquired using DIA-SWATH, ovarian cancer (OC) data acquired using DIA-MS, bladder (BladderBatch) and glioblastoma (GBM) microarray gene expression dataset. Our results demonstrate that ProJect consistently performs better than other referenced MVI methods. It achieves the lowest normalized root mean square error (on average, scoring 45.92% less error in RC_C, 27.37% in RC_full, 29.22% in OC, 23.65% in BladderBatch and 20.20% in GBM relative to the closest competing method) and the Procrustes sum of squared error (Procrustes SS) (exhibits 79.71% less error in RC_C, 38.36% in RC full, 18.13% in OC, 74.74% in BladderBatch and 30.79% in GBM compared to the next best method). ProJect also leads with the highest correlation coefficient among all types of MV combinations (0.64% higher in RC_C, 0.24% in RC full, 0.55% in OC, 0.39% in BladderBatch and 0.27% in GBM versus the second-best performing method). ProJect's key strength is its ability to handle different types of MVs commonly found in real-world data. Unlike most MVI methods that are designed to handle only one type of MV, ProJect employs a decision-making algorithm that first determines if an MV is missing at random or missing not at random. It then employs targeted imputation strategies for each MV type, resulting in more accurate and reliable imputation outcomes. An R implementation of ProJect is available at https://github.com/miaomiao6606/ProJect.
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Affiliation(s)
- Weijia Kong
- School of Biological Sciences, Nanyang Technological University, Singapore
- Department of Computer Science, National University of Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | | | | | - Kai Peng Lim
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Yulan Wang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore
| | - Wilson Wen Bin Goh
- School of Biological Sciences, Nanyang Technological University, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore
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22
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Yalezo N, Musee N. Meta-analysis of engineered nanoparticles dynamic aggregation in freshwater-like systems using machine learning techniques. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 337:117739. [PMID: 36934506 DOI: 10.1016/j.jenvman.2023.117739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/17/2023] [Accepted: 03/12/2023] [Indexed: 06/18/2023]
Abstract
Predictive algorithms for exposure characterization of engineered nanoparticles (ENPs) in the ecosystems are essential to improve the development of robust nano-safety frameworks. Here, machine learning (ML) techniques were utilised for data mining and prediction of the dynamic aggregation transformation process in aqueous environments using case studies of nZnO and nTiO2. Supervised ML models using input variables of natural organic matter, ionic strength, size, and ENPs concentration showed poor prediction performance based on statistical metric values of root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash-Sutcliffe efficiency (NSE) for both types of ENP. On the contrary, algorithms developed using model input parameters of zeta potential, pH, and time had good generalisation and high prediction accuracy. Among the five developed ML algorithms, random forest regression, support vector regression, and artificial neural network generated good prediction accuracy for both data sets. Therefore, the use of ML can be valuable in the development of robust nano-safety frameworks to optimise societal benefits, and for proactive long-term ecological protection.
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Affiliation(s)
- Ntsikelelo Yalezo
- Emerging Contaminants Ecological and Risk Assessment (ECERA) Group, Department of Chemical Engineering, University of Pretoria, Private Bag X20, Hatfield 0028, Pretoria, South Africa
| | - Ndeke Musee
- Emerging Contaminants Ecological and Risk Assessment (ECERA) Group, Department of Chemical Engineering, University of Pretoria, Private Bag X20, Hatfield 0028, Pretoria, South Africa.
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23
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Gomez LA, Toye AA, Hum RS, Kleinberg S. Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation. J Diabetes Sci Technol 2023:19322968231181138. [PMID: 37350111 DOI: 10.1177/19322968231181138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
BACKGROUND Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance. METHODS To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model). RESULTS Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets. CONCLUSIONS We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.
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Affiliation(s)
| | | | - R Stanley Hum
- The Montreal Children's Hospital, McGill University Health Centre, Montreal, QC, Canada
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24
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Ren Y, Wu D, Tong Y, López-DeFede A, Gareau S. Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies. J Med Internet Res 2023; 25:e44081. [PMID: 37256674 DOI: 10.2196/44081] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 03/02/2023] [Accepted: 04/04/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models' actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML models in maternal health; thus, it is critical to establish benchmarks to advance ML use to subsequently improve birth outcomes. OBJECTIVE This study aimed to establish several key benchmarking ML models to predict LBW and systematically apply different rebalancing optimization methods to a large-scale and extremely imbalanced all-payer hospital record data set that connects mother and baby data at a state level in the United States. We also performed feature importance analysis to identify the most contributing features in the LBW classification task, which can aid in targeted intervention. METHODS Our large data set consisted of 266,687 birth records across 6 years, and 8.63% (n=23,019) of records were labeled as LBW. To set up benchmarking ML models to predict LBW, we applied 7 classic ML models (ie, logistic regression, naive Bayes, random forest, extreme gradient boosting, adaptive boosting, multilayer perceptron, and sequential artificial neural network) while using 4 different data rebalancing methods: random undersampling, random oversampling, synthetic minority oversampling technique, and weight rebalancing. Owing to ethical considerations, in addition to ML evaluation metrics, we primarily used recall to evaluate model performance, indicating the number of correctly predicted LBW cases out of all actual LBW cases, as false negative health care outcomes could be fatal. We further analyzed feature importance to explore the degree to which each feature contributed to ML model prediction among our best-performing models. RESULTS We found that extreme gradient boosting achieved the highest recall score-0.70-using the weight rebalancing method. Our results showed that various data rebalancing methods improved the prediction performance of the LBW group substantially. From the feature importance analysis, maternal race, age, payment source, sum of predelivery emergency department and inpatient hospitalizations, predelivery disease profile, and different social vulnerability index components were important risk factors associated with LBW. CONCLUSIONS Our findings establish useful ML benchmarks to improve birth outcomes in the maternal health domain. They are informative to identify the minority class (ie, LBW) based on an extremely imbalanced data set, which may guide the development of personalized LBW early prevention, clinical interventions, and statewide maternal and infant health policy changes.
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Affiliation(s)
- Yang Ren
- Department of Computer Science, University of South Carolina, Columbia, SC, United States
| | - Dezhi Wu
- Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States
| | - Yan Tong
- Department of Computer Science, University of South Carolina, Columbia, SC, United States
| | - Ana López-DeFede
- The Institute of Families in Society, University of South Carolina, Columbia, SC, United States
| | - Sarah Gareau
- The Institute of Families in Society, University of South Carolina, Columbia, SC, United States
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25
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Ahlquist KD, Sugden LA, Ramachandran S. Enabling interpretable machine learning for biological data with reliability scores. PLoS Comput Biol 2023; 19:e1011175. [PMID: 37235578 PMCID: PMC10249903 DOI: 10.1371/journal.pcbi.1011175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/08/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Machine learning tools have proven useful across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some models that appear to perform well have later been revealed to rely on features of the data that are artifactual or biased; this feeds into the general criticism that machine learning models are designed to optimize model performance over the creation of new biological insights. A natural question arises: how do we develop machine learning models that are inherently interpretable or explainable? In this manuscript, we describe the SWIF(r) reliability score (SRS), a method building on the SWIF(r) generative framework that reflects the trustworthiness of the classification of a specific instance. The concept of the reliability score has the potential to generalize to other machine learning methods. We demonstrate the utility of the SRS when faced with common challenges in machine learning including: 1) an unknown class present in testing data that was not present in training data, 2) systemic mismatch between training and testing data, and 3) instances of testing data that have missing values for some attributes. We explore these applications of the SRS using a range of biological datasets, from agricultural data on seed morphology, to 22 quantitative traits in the UK Biobank, and population genetic simulations and 1000 Genomes Project data. With each of these examples, we demonstrate how the SRS can allow researchers to interrogate their data and training approach thoroughly, and to pair their domain-specific knowledge with powerful machine-learning frameworks. We also compare the SRS to related tools for outlier and novelty detection, and find that it has comparable performance, with the advantage of being able to operate when some data are missing. The SRS, and the broader discussion of interpretable scientific machine learning, will aid researchers in the biological machine learning space as they seek to harness the power of machine learning without sacrificing rigor and biological insight.
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Affiliation(s)
- K. D. Ahlquist
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, Rhode Island, United States of America
| | - Lauren A. Sugden
- Department of Mathematics and Computer Science, Duquesne University, Pittsburgh, Pennsylvania, United States of America
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, Rhode Island, United States of America
- Data Science Initiative, Brown University, Providence, Rhode Island, United States of America
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26
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Wu Z, Jiang R. Time-series benchmarks based on frequency features for fair comparative evaluation. Neural Comput Appl 2023; 35:1-13. [PMID: 37362566 PMCID: PMC10122570 DOI: 10.1007/s00521-023-08562-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 03/28/2023] [Indexed: 06/28/2023]
Abstract
Time-series prediction and imputation receive lots of attention in academic and industrial areas. Machine learning methods have been developed for specific time-series scenarios; however, it is difficult to evaluate the effectiveness of a certain method on other new cases. In the perspective of frequency features, a comprehensive benchmark for time-series prediction is designed for fair evaluation. A prediction problem generation process, composed of the finite impulse response filter-based approach and problem setting module, is adopted to generate the NCAA2022 dataset, which includes 16 prediction problems. To reduce the computational burden, the filter parameters matrix is divided into sub-matrices. The discrete Fourier transform is introduced to analyze the frequency distribution of transformed results. In addition, a baseline experiment further reflects the benchmarking capability of NCAA2022 dataset.
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Affiliation(s)
- Zhou Wu
- School of Automation, Chongqing University, Shazheng Street, Chongqing, 400044 China
| | - Ruiqi Jiang
- School of Automation, Chongqing University, Shazheng Street, Chongqing, 400044 China
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27
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Meng J, Li C, Tao J, Li Y, Tong Y, Wang Y, Zhang L, Dong Y, Du J. RNN-LSTM-Based Model Predictive Control for a Corn-to-Sugar Process. Processes (Basel) 2023. [DOI: 10.3390/pr11041080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
The corn-to-sugar process is difficult to control automatically because of the complex physical and chemical phenomena involved. Because the RNN-LSTN model has been shown to handle long-term time dependencies well, this article focused on the design of a model predictive control system based on this machine learning model. Based on the historical data, we first reduced the input variable dimension through data preprocessing, data dimension reduction, sensitivity analysis, etc., and then the RNN-LSTM model, with these identified key sites as inputs, and the dextrose equivalent value as the output, was constructed. Then, through model predictive control using the locally linearized RNN-LSTM as the predictive model, the objective value of the dextrose equivalent was successfully controlled at the target value by our simulation study, in different situations of setpoint changes and disturbances. This showed the potential of applying RNN-LSTM-Based model predictive control in a corn-to-sugar process.
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Affiliation(s)
- Jiaqi Meng
- Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Chengbo Li
- Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Jin Tao
- COFCO Biotechnology Co., Ltd., Beijing 100005, China
| | - Yi Li
- COFCO Biotechnology Co., Ltd., Beijing 100005, China
| | - Yi Tong
- COFCO Biotechnology Co., Ltd., Beijing 100005, China
| | - Yu Wang
- COFCO Biotechnology Co., Ltd., Beijing 100005, China
| | - Lei Zhang
- Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Yachao Dong
- Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Jian Du
- Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
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28
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Rischke S, Hahnefeld L, Burla B, Behrens F, Gurke R, Garrett TJ. Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects. J Mass Spectrom Adv Clin Lab 2023; 28:47-55. [PMID: 36872952 PMCID: PMC9982001 DOI: 10.1016/j.jmsacl.2023.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
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Key Words
- (U)HPLC (Ultra-), High pressure liquid chromatography
- Biomarker Discovery Study
- HILIC, Hydrophilic interaction liquid chromatography
- HRMS, High resolution mass spectrometry
- LC-MS, Liquid chromatography – mass spectrometry
- LC-MS-Based Clinical Research
- Lipidomics
- MRM, Multiple reaction monitoring
- Metabolomics
- PCA, Principal component analysis
- QA, Quality assurance
- QC, Quality control
- RF, Random Forest
- RP, Reversed phase
- SVA, Support vector machine
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Affiliation(s)
- S Rischke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - L Hahnefeld
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - B Burla
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - F Behrens
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.,Division of Rheumatology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - R Gurke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - T J Garrett
- Department of Pathology, Immunology and Laboratory Medicine and Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL 32611, USA
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29
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Guillard R, Hessas A, Korczowski L, Londero A, Congedo M, Loche V. Comparing Clustering Methods Applied to Tinnitus within a Bootstrapped and Diagnostic-Driven Semi-Supervised Framework. Brain Sci 2023; 13:brainsci13040572. [PMID: 37190537 DOI: 10.3390/brainsci13040572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/12/2023] [Accepted: 03/16/2023] [Indexed: 03/31/2023] Open
Abstract
The understanding of tinnitus has always been elusive and is largely prevented by its intrinsic heterogeneity. To address this issue, scientific research has aimed at defining stable and easily identifiable subphenotypes of tinnitus. This would allow better disentangling the multiple underlying pathophysiological mechanisms of tinnitus. In this study, three-dimensionality reduction techniques and two clustering methods were benchmarked on a database of 2772 tinnitus patients in order to obtain a reliable segmentation of subphenotypes. In this database, tinnitus patients’ endotypes (i.e., parts of a population with a condition with distinct underlying mechanisms) are reported when diagnosed by an ENT expert in tinnitus management. This partial labeling of the dataset enabled the design of an original semi-supervised framework. The objective was to perform a benchmark of different clustering methods to get as close as possible to the initial ENT expert endotypes. To do so, two metrics were used: a primary one, the quality of the separation of the endotypes already identified in the database, as well as a secondary one, the stability of the obtained clusterings. The relevance of the results was finally reviewed by two ENT experts in tinnitus management. A 20-cluster clustering was selected as the best-performing, the most-clinically relevant, and the most-stable through bootstrapping. This clustering used a T-SNE method as the dimensionality reduction technique and a k-means algorithm as the clustering method. The characteristics of this clustering are presented in this article.
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30
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Li A, Xiong S, Li J, Mallik S, Liu Y, Fei R, Zhou H, Liu G. AngClust: Angle Feature-Based Clustering for Short Time Series Gene Expression Profiles. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1574-1580. [PMID: 35853049 DOI: 10.1109/tcbb.2022.3192306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
When clustering gene expression, it is expected that correlation coefficients of genes in the same clusters are high, and that gene ontology (GO) enrichment analysis of most clusters will be significant. However, existing short-term gene expression clustering algorithms have limitations. To address this problem, we proposed a novel clustering process based on angular features for short-term gene expression. Our method (named AngClust) uses angular features to indicate the change of trend in gene expression levels at two neighboring time points. The changes of angles at multiple time points reflects the change of trend of the overall expression levels. Such changes are used to measure whether the expression trends of different genes are similar. To obtain functionally significant clusters from the clustering results, we evaluated numbers of genes in clusters, average correlation coefficient, fluctuation, and their correlation with GO term enrichment. The efficacy of AngClust outperform two other measures, Euclidean distance (ED) and dynamic time warping of correlation (DTW), on a dataset of yeast gene expression. The ratios of GO and pathway term-enriched of clusters of AngClust is higher than or equal to that of STEM and TMixClust on human, mouse, and yeast time series of gene expression.
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31
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Investigation of Machine Learning Techniques for Disruption Prediction Using JET Data. PLASMA 2023. [DOI: 10.3390/plasma6010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared. A focus has been placed on the analysis of a transition to dimensionless input quantities. The methods used in this paper are the support vector machine, two-tiered support vector machine, random forest, gradient-boosted trees and long-short term memory. The performance between different models is remarkably similar, with the support vector machine attaining a slightly better accuracy score. The similarity could indicate issues with the dataset, but further study is required to confirm this. Both the two-tiered model and long-short term memory performed below expectations. The former could be attributed to an implementation which did not allow error propagation between tiers. The latter could be attributed to high noise and low frequency of the input signals. Dimensionless models experienced an expected decrease in performance, caused by a loss of information in the conversion. However, random forest and gradient boosted trees experienced a significantly lower decrease, making them more suitable for dimensionless predictors. From the disruption detection times, it was concluded that several disruptions could be predicted at more than 600ms in advance. A feature importance study using the random forest indicated the negative impact of high noise and missing data in the database, suggesting improvements in data preparation for future work and the potential reevaluation of some of the selected portable features due to poor performance.
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32
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Li D, Fu J, Zhao J, Qin J, Zhang L. A deep learning system for heart failure mortality prediction. PLoS One 2023; 18:e0276835. [PMID: 36827436 PMCID: PMC9956019 DOI: 10.1371/journal.pone.0276835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 10/17/2022] [Indexed: 02/26/2023] Open
Abstract
Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates of prognosis HF patients are highly variable, ranging from 5% to 75%. Evaluating the all-cause mortality of HF patients is an important means to avoid death and positively affect the health of patients. But in fact, machine learning models are difficult to gain good results on missing values, high dimensions, and imbalances HF data. Therefore, a deep learning system is proposed. In this system, we propose an indicator vector to indicate whether the value is true or be padded, which fast solves the missing values and helps expand data dimensions. Then, we use a convolutional neural network with different kernel sizes to obtain the features information. And a multi-head self-attention mechanism is applied to gain whole channel information, which is essential for the system to improve performance. Besides, the focal loss function is introduced to deal with the imbalanced problem better. The experimental data of the system are from the public database MIMIC-III, containing valid data for 10311 patients. The proposed system effectively and fast predicts four death types: death within 30 days, death within 180 days, death within 365 days and death after 365 days. Our study uses Deep SHAP to interpret the deep learning model and obtains the top 15 characteristics. These characteristics further confirm the effectiveness and rationality of the system and help provide a better medical service.
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Affiliation(s)
- Dengao Li
- College of Data Science, Taiyuan University of Technology, Taiyuan, China
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Jinzhong, China
- * E-mail:
| | - Jian Fu
- College of Data Science, Taiyuan University of Technology, Taiyuan, China
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Jinzhong, China
| | - Jumin Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junnan Qin
- Department of Cardiology, Shanxi Academy of Medical Sciences, Tongji Medical College, Shanxi Bethune Hospital, Shanxi Medical University, Tongji Shanxi Hospital, Huazhong University of Science and Technology, Taiyuan, China
| | - Lihui Zhang
- Department of General Medical, Shanxi Academy of Medical Sciences, Tongji Medical College, Shanxi Bethune Hospital, Shanxi Medical University, Tongji Shanxi Hospital, Huazhong University of Science and Technology, Taiyuan, China
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33
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Al-Sowi AM, AlMasri N, Hammo B, Al-Qwaqzeh FAZ. Cerebral Palsy classification based on multi-feature analysis using machine learning. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023] Open
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34
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Ogasawara T, Mukaino M, Matsuura H, Aoshima Y, Suzuki T, Togo H, Nakashima H, Saitoh E, Yamaguchi M, Otaka Y, Tsukada S. Ensemble averaging for categorical variables: Validation study of imputing lost data in 24-h recorded postures of inpatients. Front Physiol 2023; 14:1094946. [PMID: 36776969 PMCID: PMC9910696 DOI: 10.3389/fphys.2023.1094946] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/06/2023] [Indexed: 01/27/2023] Open
Abstract
Acceleration sensors are widely used in consumer wearable devices and smartphones. Postures estimated from recorded accelerations are commonly used as features indicating the activities of patients in medical studies. However, recording for over 24 h is more likely to result in data losses than recording for a few hours, especially when consumer-grade wearable devices are used. Here, to impute postures over a period of 24 h, we propose an imputation method that uses ensemble averaging. This method outputs a time series of postures over 24 h with less lost data by calculating the ratios of postures taken at the same time of day during several measurement-session days. Whereas conventional imputation methods are based on approaches with groups of subjects having multiple variables, the proposed method imputes the lost data variables individually and does not require other variables except posture. We validated the method on 306 measurement data from 99 stroke inpatients in a hospital rehabilitation ward. First, to classify postures from acceleration data measured by a wearable sensor placed on the patient's trunk, we preliminary estimated possible thresholds for classifying postures as 'reclining' and 'sitting or standing' by investigating the valleys in the histogram of occurrences of trunk angles during a long-term recording. Next, the imputations of the proposed method were validated. The proposed method significantly reduced the missing data rate from 5.76% to 0.21%, outperforming a conventional method.
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Affiliation(s)
- Takayuki Ogasawara
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan,*Correspondence: Takayuki Ogasawara,
| | - Masahiko Mukaino
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan,Department of Rehabilitation Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Hirotaka Matsuura
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Yasushi Aoshima
- Department of Rehabilitation, Fujita Health University Hospital, Toyoake, Japan
| | - Takuya Suzuki
- Department of Rehabilitation, Fujita Health University Hospital, Toyoake, Japan
| | - Hiroyoshi Togo
- NTT Device Innovation Center, NTT Corporation, Atsugi, Japan
| | - Hiroshi Nakashima
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
| | - Eiichi Saitoh
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Masumi Yamaguchi
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
| | - Yohei Otaka
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Shingo Tsukada
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
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Peralbo-Molina Á, Solà-Santos P, Perera-Lluna A, Chicano-Gálvez E. Data Processing and Analysis in Mass Spectrometry-Based Metabolomics. Methods Mol Biol 2023; 2571:207-239. [PMID: 36152164 DOI: 10.1007/978-1-0716-2699-3_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.
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Affiliation(s)
- Ángela Peralbo-Molina
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain.
| | - Pol Solà-Santos
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Alexandre Perera-Lluna
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Eduardo Chicano-Gálvez
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain
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36
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Hoffman S, Filak M, Jasiński R. Air Quality Modeling with the Use of Regression Neural Networks. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16494. [PMID: 36554373 PMCID: PMC9779138 DOI: 10.3390/ijerph192416494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/26/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Air quality is assessed on the basis of air monitoring data. Monitoring data are often not complete enough to carry out an air quality assessment. To fill the measurement gaps, predictive models can be used, which enable the approximation of missing data. Prediction models use historical data and relationships between measured variables, including air pollutant concentrations and meteorological factors. The known predictive air quality models are not accurate, so it is important to look for models that give a lower approximation error. The use of artificial neural networks reduces the prediction error compared to classical regression methods. In previous studies, a single regression model over the entire concentration range was used to approximate the concentrations of a selected pollutant. In this study, it was assumed that not a single model, but a group of models, could be used for the prediction. In this approach, each model from the group was dedicated to a different sub-range of the concentration of the modeled pollutant. The aim of the analysis was to check whether this approach would improve the quality of modeling. A long-term data set recorded at two air monitoring stations in Poland was used in the examination. Hourly data of basic air pollutants and meteorological parameters were used to create predictive regression models. The prediction errors for the sub-range models were compared with the corresponding errors calculated for one full-range regression model. It was found that the application of sub-range models reduced the modeling error of basic air pollutants.
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37
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Sue KL, Tsai CF, Tsau HM. Missing value imputation and the effect of feature normalisation on financial distress prediction. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2153278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
| | - Chih-Fong Tsai
- Department of Information Management, National Central University, Taoyuan, Taiwan
| | - Hau-Min Tsau
- Department of Information Management, National Central University, Taoyuan, Taiwan
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38
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Kong W, Hui HWH, Peng H, Goh WWB. Dealing with missing values in proteomics data. Proteomics 2022; 22:e2200092. [PMID: 36349819 DOI: 10.1002/pmic.202200092] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/15/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022]
Abstract
Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.
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Affiliation(s)
- Weijia Kong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Harvard Wai Hann Hui
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Hui Peng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.,Centre for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
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39
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Kalyakulina A, Yusipov I, Bacalini MG, Franceschi C, Vedunova M, Ivanchenko M. Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI. Gigascience 2022; 11:giac097. [PMID: 36259657 PMCID: PMC9718659 DOI: 10.1093/gigascience/giac097] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/01/2022] [Accepted: 09/15/2022] [Indexed: 07/25/2023] Open
Abstract
BACKGROUND DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific workflow for joint data processing. RESULTS We propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinson's disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. XAI approaches have allowed us to explain model predictions from both populational and individual perspectives. CONCLUSIONS We propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole-blood DNA methylation data using Parkinson's disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.
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Affiliation(s)
- Alena Kalyakulina
- Correspondence author. Alena Kalyakulina, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Gagarin avenue 22, Nizhny Novgorod 603022, Russia. E-mail:
| | | | | | - Claudio Franceschi
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, 603022 Nizhny Novgorod, Russia
| | - Maria Vedunova
- Institute of Biology and Biomedicine, Lobachevsky State University, 603022 Nizhny Novgorod, Russia
| | - Mikhail Ivanchenko
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, 603022 Nizhny Novgorod, Russia
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Alcazar O, Ogihara M, Ren G, Buchwald P, Abdulreda MH. Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets. Biomolecules 2022; 12:biom12101444. [PMID: 36291653 PMCID: PMC9599756 DOI: 10.3390/biom12101444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/25/2022] Open
Abstract
Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.
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Affiliation(s)
- Oscar Alcazar
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Mitsunori Ogihara
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Department of Computer Science, University of Miami, Coral Gables, FL 33146, USA
- Correspondence: (M.O.); (G.R.); (P.B.); (M.H.A.); Tel.: +1-30-5284-2308 (M.O.); +1-30-5243-1649 (G.R.); +1-30-5243-9657 (P.B.); +1-30-5243-9871 (M.H.A.)
| | - Gang Ren
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Department of Computer Science, University of Miami, Coral Gables, FL 33146, USA
- Correspondence: (M.O.); (G.R.); (P.B.); (M.H.A.); Tel.: +1-30-5284-2308 (M.O.); +1-30-5243-1649 (G.R.); +1-30-5243-9657 (P.B.); +1-30-5243-9871 (M.H.A.)
| | - Peter Buchwald
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Correspondence: (M.O.); (G.R.); (P.B.); (M.H.A.); Tel.: +1-30-5284-2308 (M.O.); +1-30-5243-1649 (G.R.); +1-30-5243-9657 (P.B.); +1-30-5243-9871 (M.H.A.)
| | - Midhat H. Abdulreda
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Surgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Correspondence: (M.O.); (G.R.); (P.B.); (M.H.A.); Tel.: +1-30-5284-2308 (M.O.); +1-30-5243-1649 (G.R.); +1-30-5243-9657 (P.B.); +1-30-5243-9871 (M.H.A.)
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Saravana Kumar K, Ramasubramanian S. A clinical decision support system for heart disease prediction with ensemble two-fold classification framework. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Cardiovascular disease (CVD) is a severe public health concern globally. Early and accurate CVD diagnosis is a difficult task but a necessary endeavour required to prevent further damage and protect patients’ lives. Machine Learning (ML)-based Clinical Decision Support Systems (CDSS) have the potential to assist healthcare providers in making accurate CVD diagnoses and treatments. Clinical data usually contains missing values (MVs); hence, the incorporated imputation techniques for ML have become a critical consideration when working with real-world medical datasets. Furthermore, removing instances with MVs will lead to essential data loss and produce incorrect results. To overcome these issues, this paper proposes an efficient and reliable CDSS with Ensemble Two-Fold Classification (ETC) framework for classifying heart diseases. The effectiveness of the proposed ETC framework using different supervised ML algorithms is evaluated with four distinct imputation methods for handling MVs over the standard benchmark dataset, viz., the University of California, Irwin (UCI). Experimental results show that our proposed ETC framework with the k-Nearest Neighbors(k-NN) imputation method achieves better classification accuracy of 0.9999 and a lesser error rate of 0.0989 compared to other imputation methods and classifiers with similar execution times.
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Affiliation(s)
- K. Saravana Kumar
- IT Department, UCE(BIT CAMPUS), Anna University, Trichy, TamilNadu, India
| | - S. Ramasubramanian
- Maths Department, UCE(BIT CAMPUS), Anna University, Trichy, TamilNadu, India
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42
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Lee MH, Shin KI. Bias corrected imputation method for non-ignorable non-response. KOREAN JOURNAL OF APPLIED STATISTICS 2022. [DOI: 10.5351/kjas.2022.35.4.485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Min-Ha Lee
- Department of Statistics, Hankuk University of Foreign Studies
| | - Key-Il Shin
- Department of Statistics, Hankuk University of Foreign Studies
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43
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Theophanous S, Lønne PI, Choudhury A, Berbee M, Dekker A, Dennis K, Dewdney A, Gambacorta MA, Gilbert A, Guren MG, Holloway L, Jadon R, Kochhar R, Mohamed AA, Muirhead R, Parés O, Raszewski L, Roy R, Scarsbrook A, Sebag-Montefiore D, Spezi E, Spindler KLG, van Triest B, Vassiliou V, Malinen E, Wee L, Appelt AL. Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study. Diagn Progn Res 2022; 6:14. [PMID: 35922837 PMCID: PMC9351222 DOI: 10.1186/s41512-022-00128-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 06/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy. METHODS This is a retrospective multi-centre cohort study, investigating overall survival, locoregional control and freedom from distant metastasis after primary chemoradiotherapy for anal squamous cell carcinoma. Patient data will be extracted and organised at each participating radiotherapy centre (n = 18). Candidate prognostic factors have been identified through literature review and expert opinion. Summary statistics will be calculated and exchanged between centres prior to modelling. The primary analysis will involve developing and validating Cox proportional hazards models across centres for each outcome through distributed learning. Outcomes at specific timepoints of interest and factor effect estimates will be reported, allowing for outcome prediction for future patients. DISCUSSION The atomCAT2 study will analyse one of the largest available cross-institutional cohorts of patients with anal cancer treated with chemoradiotherapy. The analysis aims to provide information on current international clinical practice outcomes and may aid the personalisation and design of future anal cancer clinical trials through contributing to a better understanding of patient risk stratification.
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Affiliation(s)
- Stelios Theophanous
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
| | - Per-Ivar Lønne
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
| | - Ananya Choudhury
- MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands
| | - Maaike Berbee
- MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands
| | - Andre Dekker
- MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands
| | | | | | | | - Alexandra Gilbert
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Marianne Grønlie Guren
- Department of Oncology, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lois Holloway
- Ingham Research Institute and Liverpool Hospital, Liverpool, New South Wales, Australia
| | | | | | | | | | | | | | - Rajarshi Roy
- Hull University Teaching Hospitals NHS Trust, Hull, UK
| | - Andrew Scarsbrook
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | | | | | - Baukelien van Triest
- The Netherlands Cancer Institute-Antoni van Leeuwenhoek (NKI-AVL), Amsterdam, The Netherlands
| | | | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
| | - Leonard Wee
- MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands
| | - Ane L Appelt
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
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Samad MD, Abrar S, Diawara N. Missing value estimation using clustering and deep learning within multiple imputation framework. Knowl Based Syst 2022; 249. [DOI: 10.1016/j.knosys.2022.108968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Pan H, Ye Z, He Q, Yan C, Yuan J, Lai X, Su J, Li R. Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent. SENSORS (BASEL, SWITZERLAND) 2022; 22:5645. [PMID: 35957197 PMCID: PMC9371018 DOI: 10.3390/s22155645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/21/2022] [Accepted: 07/26/2022] [Indexed: 06/15/2023]
Abstract
Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems. Because the missing data may reduce productivity, missing value imputation is an important research topic in data mining. At present, most studies mainly focus on imputation methods for continuous missing data, while a few concentrate on discrete missing data. In this paper, a discrete missing value imputation method based on a multilayer perceptron (MLP) is proposed, which employs a momentum gradient descent algorithm, and some prefilling strategies are utilized to improve the convergence speed of the MLP. To verify the effectiveness of the method, experiments are conducted to compare the classification accuracy with eight common imputation methods, such as the mode, random, hot-deck, KNN, autoencoder, and MLP, under different missing mechanisms and missing proportions. Experimental results verify that the improved MLP model (IMLP) can effectively impute discrete missing values in most situations under three missing patterns.
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Affiliation(s)
- Hu Pan
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China; (H.P.); (Q.H.); (C.Y.); (J.Y.); (J.S.); (R.L.)
| | - Zhiwei Ye
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China; (H.P.); (Q.H.); (C.Y.); (J.Y.); (J.S.); (R.L.)
- Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China
- Key Laboratory of Intelligent Computing and Information Processing, Fujian Province, Quanzhou 362000, China
| | - Qiyi He
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China; (H.P.); (Q.H.); (C.Y.); (J.Y.); (J.S.); (R.L.)
| | - Chunyan Yan
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China; (H.P.); (Q.H.); (C.Y.); (J.Y.); (J.S.); (R.L.)
| | - Jianyu Yuan
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China; (H.P.); (Q.H.); (C.Y.); (J.Y.); (J.S.); (R.L.)
| | - Xudong Lai
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China;
| | - Jun Su
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China; (H.P.); (Q.H.); (C.Y.); (J.Y.); (J.S.); (R.L.)
| | - Ruihan Li
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China; (H.P.); (Q.H.); (C.Y.); (J.Y.); (J.S.); (R.L.)
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Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland. ENERGIES 2022. [DOI: 10.3390/en15145129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this article, we analyze the energy consumption data of business customers registered by trading companies in Poland. We focus on estimating missing data in hourly series, as forecasts of this frequency are needed to determine the volume of electricity orders on the power exchange or the contract market. Our goal is to identify an appropriate method of imputation missing data for this type of data. Trading companies expect a specific solution, so we use a procedure that allows to choose the imputation method, which will consequently improve the accuracy of forecasting energy consumption. Using this procedure, a statistical analysis of the occurrence of missing values is performed. Then, three techniques for generating missing data are selected (missing data are generated in randomly selected series without missing values). The selected imputation methods are tested and the best method is chosen based on MAE and MAPE errors.
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47
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Chen YP, Huang CH, Lo YH, Chen YY, Lai F. Combining Attention with Spectrum to Handle Missing Values on Time Series Data Without Imputation. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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48
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Hu X, Shen Y, Pedrycz W, Li Y, Wu G. Granular Fuzzy Rule-Based Modeling With Incomplete Data Representation. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:6420-6433. [PMID: 33909582 DOI: 10.1109/tcyb.2021.3071145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Incomplete data are frequently encountered and bring difficulties when it comes to further processing. The concepts of granular computing (GrC) help deliver a higher level of abstraction to address this problem. Most of the existing data imputation and related modeling methods are of numeric nature and require prior numeric models to be provided. The underlying objective of this study is to introduce a novel and straightforward approach that uses information granules as a vehicle to effectively represent missing data and build granular fuzzy models directly from resulting hybrid granular and numeric data. The evaluation and optimization of this method are guided by the principle of justifiable granularity engaging the coverage and specificity criteria and carried out with the help of particle swarm optimization. We provide a collection of experimental studies using a synthetic dataset and several publicly available real-world datasets to demonstrate the feasibility and analyze the main features of this method.
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Malakar S, Roy SD, Das S, Sen S, Velásquez JD, Sarkar R. Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5525-5567. [PMID: 35729963 PMCID: PMC9199478 DOI: 10.1007/s11831-022-09776-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.
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Affiliation(s)
- Samir Malakar
- Department of Computer Science, Asutosh College, Kolkata, India
| | - Soumya Deep Roy
- Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India
| | - Soham Das
- Department of Metallurgical and Material Engineering, Jadavpur University, Kolkata, India
| | - Swaraj Sen
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Juan D. Velásquez
- Departament of Industrial Engineering, University of Chile, Santiago, Chile
- Instituto Sistemas Complejos de Ingeniería (ISCI), Santiago, Chile
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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Identification of Secondary Breast Cancer in Vital Organs through the Integration of Machine Learning and Microarrays. ELECTRONICS 2022. [DOI: 10.3390/electronics11121879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Breast cancer includes genetic and environmental factors and is the most prevalent malignancy in women contributing to the pathogenesis and progression of cancer. Breast cancer prognosis metastasizes towards bones, the liver, brain, and lungs, and is the main cause of death in patients. Furthermore, the selection of features and classification is significant in microarray data analysis, which suffers from huge time consumption. To address these issues, this research uniquely integrates machine learning and microarrays to identify secondary breast cancer in vital organs. This work firstly imputes the missing values using K-nearest neighbors and improves the recursive feature elimination with cross-validation (RFECV) using the random forest method. Secondly, the class imbalance is handled by employing K-means synthetic object oversampling technique (SMOTE) to balance minority class and prevent noise. We successfully identified the 16 most essential Entrez gene ids responsible for predicting metastatic locations in the bones, brain, liver, and lungs. Extensive experiments are conducted on NCBI Gene Expression Omnibus GSE14020 and GSE54323 datasets. The proposed methods have handled class imbalance, prevented noise, and appropriately reduced time consumption. Reliable results were obtained on four classification models: decision tree; K-nearest neighbors; random forest; and support vector machine. Results are presented having considered confusion matrices, accuracy, ROC-AUC and PR-AUC, and F1-score.
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