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Wu M, Gu S, Yang J, Zhao Y, Sheng J, Cheng S, Xu S, Wu Y, Ma M, Luo X, Zhang H, Wang Y, Zhao A. Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer. BMC Cancer 2024; 24:267. [PMID: 38408960 PMCID: PMC10895771 DOI: 10.1186/s12885-024-11989-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/10/2024] [Indexed: 02/28/2024] Open
Abstract
PURPOSE Significant advancements in improving ovarian cancer (OC) outcomes have been limited over the past decade. To predict prognosis and improve outcomes of OC, we plan to develop and validate a robust prognosis signature based on blood features. METHODS We screened age and 33 blood features from 331 OC patients. Using ten machine learning algorithms, 88 combinations were generated, from which one was selected to construct a blood risk score (BRS) according to the highest C-index in the test dataset. RESULTS Stepcox (both) and Enet (alpha = 0.7) performed the best in the test dataset with a C-index of 0.711. Meanwhile, the low RBS group possessed observably prolonged survival in this model. Compared to traditional prognostic-related features such as age, stage, grade, and CA125, our combined model had the highest AUC values at 3, 5, and 7 years. According to the results of the model, BRS can provide accurate predictions of OC prognosis. BRS was also capable of identifying various prognostic stratifications in different stages and grades. Importantly, developing the nomogram may improve performance by combining BRS and stage. CONCLUSION This study provides a valuable combined machine-learning model that can be used for predicting the individualized prognosis of OC patients.
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Affiliation(s)
- Meixuan Wu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Sijia Gu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jiani Yang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Yaqian Zhao
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Jindan Sheng
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Shanshan Cheng
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Shilin Xu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yongsong Wu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Mingjun Ma
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Xiaomei Luo
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Hao Zhang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Yu Wang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China.
| | - Aimin Zhao
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
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Sheehy J, Rutledge H, Acharya UR, Loh HW, Gururajan R, Tao X, Zhou X, Li Y, Gurney T, Kondalsamy-Chennakesavan S. Gynecological cancer prognosis using machine learning techniques: A systematic review of last three decades (1990–2022). Artif Intell Med 2023; 139:102536. [PMID: 37100507 DOI: 10.1016/j.artmed.2023.102536] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023]
Abstract
OBJECTIVE Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs. METHODS Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly. RESULTS Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes. CONCLUSION There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.
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Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7842566. [PMID: 35434134 PMCID: PMC9010213 DOI: 10.1155/2022/7842566] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 01/29/2022] [Accepted: 03/06/2022] [Indexed: 02/07/2023]
Abstract
Purpose Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine.
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McGough SF, Incerti D, Lyalina S, Copping R, Narasimhan B, Tibshirani R. Penalized regression for left-truncated and right-censored survival data. Stat Med 2021; 40:5487-5500. [PMID: 34302373 PMCID: PMC9290657 DOI: 10.1002/sim.9136] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 01/14/2023]
Abstract
High‐dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high‐throughput screening, electronic health records, and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data are also left‐truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left‐truncated and right‐censored survival data and assess implications of left truncation adjustment on bias and interpretation. We use simulation studies and a high‐dimensional, real‐world clinico‐genomic database to highlight the pitfalls of failing to account for left truncation in survival modeling.
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Affiliation(s)
- Sarah F McGough
- Product Development, Genentech, Inc, South San Francisco, California, USA
| | - Devin Incerti
- Product Development, Genentech, Inc, South San Francisco, California, USA
| | - Svetlana Lyalina
- Product Development, Genentech, Inc, South San Francisco, California, USA
| | - Ryan Copping
- Product Development, Genentech, Inc, South San Francisco, California, USA
| | - Balasubramanian Narasimhan
- Department of Statistics, Stanford University, Stanford, California, USA.,Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Robert Tibshirani
- Department of Statistics, Stanford University, Stanford, California, USA.,Department of Biomedical Data Science, Stanford University, Stanford, California, USA
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Ni D, Xiao Z, Zhong B, Feng X. Multiple Human-Behaviour Indicators for Predicting Lung Cancer Mortality with Support Vector Machine. Sci Rep 2018; 8:16596. [PMID: 30413734 PMCID: PMC6226432 DOI: 10.1038/s41598-018-34945-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 10/27/2018] [Indexed: 11/25/2022] Open
Abstract
Lung cancer is still one of the most common causes of death around the world, while there is overwhelming evidence that the environment and lifestyle factors are predominant causes of most sporadic cancers. However, when applying human-behaviour indicators to the prediction of cancer mortality (CM), we are often caught in a dilemma with inadequate sample size. Thus, this study extracted 30 human-behaviour indicators of seven categories (air pollution, tobacco smoking & alcohol consumption, socioeconomic status, food structure, working culture, medical level, and demographic structure) from Organization for Economic Cooperation and Development Database and World Health Organization Mortality Database for 13 countries (1998-2013), and employed Support Vector Machine (SVM) to examine the weights of 30 indicators across the 13 countries and the power for predicting lung CM for the years between 2014-2016. The weights of different human-behaviour indicators indicate that every country has its own lung cancer killers, that is, the human-behaviour indicators are country specific; Moreover, SVM has an excellent power in predicting their lung CM. The average accuracy in prediction offered by SVM can be as high as 96.08% for the 13 countries tested between 2014 and 2016.
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Affiliation(s)
- Du Ni
- School of Economics and Business Administration, Chongqing University, Chongqing, 400044, PR China
| | - Zhi Xiao
- School of Economics and Business Administration, Chongqing University, Chongqing, 400044, PR China.
| | - Bo Zhong
- College of Mathematics and Statistics, Chongqing University, Chongqing, 400044, PR China
| | - Xiaodong Feng
- School of Economics and Business Administration, Chongqing University, Chongqing, 400044, PR China
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Gao K, Wang D, Huang Y. Cross-cancer Prediction: A Novel Machine Learning Approach to Discover Molecular Targets for Development of Treatments for Multiple Cancers. Cancer Inform 2018; 17:1176935118805398. [PMID: 30364884 PMCID: PMC6198390 DOI: 10.1177/1176935118805398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 09/12/2018] [Indexed: 12/15/2022] Open
Abstract
Conventional cancer drug development has long been limited to organ- or tissue-specific cancer types. However, it has become increasingly known that specific genetic abnormalities are responsible for the carcinogenesis of multiple cancers. The recent US Food and Drug Administration (FDA) approval of the first multi-cancer drug, Keytruda, has demonstrated the feasibility of developing new drugs that target multiple cancers. Despite a promising future, methodological development for identifying multi-cancer molecular targets remains encumbered. This study developed a novel machine learning approach to identify such genes responsible for multiple cancers by synthesizing salient genomic information from cancer-specific classification models. This approach centered on the cross-cancer prediction method for identifying groups of cancers with high cross-cancer predictability. Furthermore, a robust hybrid classifier, comprising Prediction Analysis for Microarrays and Random Forest, was developed to integrate predictive models for gene inference. This approach has successfully identified key genes shared by endometrial cancer, mammary gland ductal carcinoma, and small cell lung cancer. The results are supported by published experimental evidence. This framework holds potential to transform the current methods of discovering multi-cancer molecular targets for clinical oncology.
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Affiliation(s)
| | | | - Yi Huang
- Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, USA
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7
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de Carvalho LP, Tan SH, Ow GS, Tang Z, Ching J, Kovalik JP, Poh SC, Chin CT, Richards AM, Martinez EC, Troughton RW, Fong AYY, Yan BP, Seneviratna A, Sorokin V, Summers SA, Kuznetsov VA, Chan MY. Plasma Ceramides as Prognostic Biomarkers and Their Arterial and Myocardial Tissue Correlates in Acute Myocardial Infarction. JACC Basic Transl Sci 2018; 3:163-175. [PMID: 30062203 PMCID: PMC6060200 DOI: 10.1016/j.jacbts.2017.12.005] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 09/29/2017] [Accepted: 12/18/2017] [Indexed: 11/16/2022]
Abstract
Targeted profiling of ceramides identified a 12-ceramide plasma signature that predicted 12-month cardiovascular death, MI, and stroke in 2 prospective cohorts of AMI patients. Among coronary artery bypass grafting patients, plasma ceramides were higher in those with recent AMI compared with those without recent acute MI. Analysis of rat ischemic myocardium revealed a consistent increase in ceramide levels and overexpression of 3 enzymes in ceramide biosynthesis.
We identified a plasma signature of 11 C14 to C26 ceramides and 1 C16 dihydroceramide predictive of major adverse cardiovascular events in patients with acute myocardial infarction (AMI). Among patients undergoing coronary artery bypass surgery, those with recent AMI, compared with those without recent AMI, showed a significant increase in 5 of the signature’s 12 ceramides in plasma but not simultaneously-biopsied aortic tissue. In contrast, a rat AMI model, compared with sham control, showed a significant increase in myocardial concentrations of all 12 ceramides and up-regulation of 3 ceramide-producing enzymes, suggesting ischemic myocardium as a possible source of this ceramide signature.
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Key Words
- AMI, acute myocardial infarction
- CABG, coronary artery bypass graft
- CAD, coronary artery disease
- CerS6, ceramide synthase 6
- DDg, data-driven grouping
- HILIC, hydrophilic interaction LC
- LAD, left anterior descending
- MACCE, major adverse cardiac and cerebrovascular events
- MI, myocardial infarction
- SPT, serine palmitoyl transferase
- SPTLC2, serine palmitoyl transferase-2
- SWVg, statistically-weighted voting grouping
- acute coronary syndrome
- ceramides
- dihydroceramides
- major adverse cardiovascular and cerebrovascular events
- nSMase, neutral sphingomelinase
- prognosis
- risk prediction
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Affiliation(s)
- Leonardo P de Carvalho
- Federal University of Sao Paulo State, Sao Paulo, Brazil.,National University Heart Center, Singapore, Singapore.,Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Albert Einstein Hospital, São Paulo, Brazil
| | - Sock Hwee Tan
- National University Heart Center, Singapore, Singapore.,Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Zhiqun Tang
- Bioinformatics Institute, ASTAR, Singapore.,Institute of Molecular and Cell Biology, ASTAR, Singapore
| | - Jianhong Ching
- Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Jean-Paul Kovalik
- Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Graduate Medical School, Singapore
| | | | - Chee-Tang Chin
- Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Graduate Medical School, Singapore.,National Heart Centre Singapore, Singapore
| | - A Mark Richards
- National University Heart Center, Singapore, Singapore.,Christchurch Heart Institute, University of Otago Christchurch, Christchurch Hospital, Christchurch, New Zealand
| | | | - Richard W Troughton
- Christchurch Heart Institute, University of Otago Christchurch, Christchurch Hospital, Christchurch, New Zealand
| | - Alan Yean-Yip Fong
- Clinical Research Centre, Sarawak General Hospital, Kuching, Malaysia.,Department of Cardiology, Sarawak General Hospital, Kuching, Malaysia
| | - Bryan P Yan
- Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Vitaly Sorokin
- National University Heart Center, Singapore, Singapore.,Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Vladimir A Kuznetsov
- Bioinformatics Institute, ASTAR, Singapore.,Nanyang Institute of Technology in Health & Medicine, Nanyang Technological University, Singapore
| | - Mark Y Chan
- National University Heart Center, Singapore, Singapore.,Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Pashazadeh A, Navimipour NJ. Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review. J Biomed Inform 2018; 82:47-62. [PMID: 29655946 DOI: 10.1016/j.jbi.2018.03.014] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 11/19/2017] [Accepted: 03/23/2018] [Indexed: 01/08/2023]
Abstract
Healthcare provides many services such as diagnosing, treatment, prevention of diseases, illnesses, injuries, and other physical and mental disorders. Large-scale distributed data processing applications in healthcare as a basic concept operates on large amounts of data. Therefore, big data application functions are the main part of healthcare operations, but there was not any comprehensive and systematic survey about studying and evaluating the important techniques in this field. Therefore, this paper aims at providing the comprehensive, detailed, and systematic study of the state-of-the-art mechanisms in the big data related to healthcare applications in five categories, including machine learning, cloud-based, heuristic-based, agent-based, and hybrid mechanisms. Also, this paper displayed a systematic literature review (SLR) of the big data applications in the healthcare literature up to the end of 2016. Initially, 205 papers were identified, but a paper selection process reduced the number of papers to 29 important studies.
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Affiliation(s)
- Asma Pashazadeh
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
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Bing D, Ying J, Miao J, Lan L, Wang D, Zhao L, Yin Z, Yu L, Guan J, Wang Q. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clin Otolaryngol 2018; 43:868-874. [PMID: 29356346 DOI: 10.1111/coa.13068] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2018] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Sudden sensorineural hearing loss (SSHL) is a multifactorial disorder with high heterogeneity, thus the outcomes vary widely. This study aimed to develop predictive models based on four machine learning methods for SSHL, identifying the best performer for clinical application. DESIGN Single-centre retrospective study. SETTING Chinese People's liberation army (PLA) hospital, Beijing, China. PARTICIPANTS A total of 1220 in-patient SSHL patients were enrolled between June 2008 and December 2015. MAIN OUTCOME MEASURES An advanced deep learning technique, deep belief network (DBN), together with the conventional logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) were developed to predict the dichotomised hearing outcome of SSHL by inputting six feature collections derived from 149 potential predictors. Accuracy, precision, recall, F-score and the area under the receiver operator characteristic curves (ROC-AUC) were exploited to compare the prediction performance of different models. RESULTS Overall the best predictive ability was provided by the DBN model when tested in the raw data set with 149 variables, achieving an accuracy of 77.58% and AUC of 0.84. Nevertheless, DBN yielded inferior performance after feature pruning. In contrast, the LR, SVM and MLP models demonstrated opposite trend as the greatest individual prediction powers were obtained when included merely three variables, with the ROC-AUC ranging from 0.79 to 0.81, and then decreased with the increasing size of input features combinations. CONCLUSIONS With the input of enough features, DBN can be a robust prediction tool for SSHL. But LR is more practical for early prediction in routine clinical application using three readily available variables, that is time elapse between symptom onset and study entry, initial hearing level and audiogram.
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Affiliation(s)
- D Bing
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - J Ying
- Medical Support Center, Chinese PLA General Hospital, Beijing, China
| | - J Miao
- Keele campus, York University, Toronto, Canada
| | - L Lan
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - D Wang
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - L Zhao
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - Z Yin
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - L Yu
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - J Guan
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - Q Wang
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
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Kraus JM, Lausser L, Kuhn P, Jobst F, Bock M, Halanke C, Hummel M, Heuschmann P, Kestler HA. Big data and precision medicine: challenges and strategies with healthcare data. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2018. [DOI: 10.1007/s41060-018-0095-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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