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Non-communicable Diseases, Big Data and Artificial Intelligence
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Non-communicable Diseases, Big Data and Artificial Intelligence

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 36949

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Youxin Wang

E-Mail Website
Guest Editor
Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China
Interests: molecular epidemiology; non-communicable diseases; 3P medicine; precision medicine; machine learning; public health
Dr. Ming Feng

E-Mail Website
Guest Editor
Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, China
Interests: neurosurgery; neuroendocrinology; pitutary; stroke; big data; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

The 2030 Agenda for Sustainable Development adopted by the United Nations in 2015 recognized non-communicable diseases (NCDs) as a major public health challenge. NCDs are usually multifactorial diseases influenced by both genetic and environmental factors, which makes them difficult to prevent and treat effectively. Medical and health big data consisting of lifestyle, clinical, and biological data provide an almost unlimited amount of information about diseases, far exceeding the human ability to make sense of it. Artificial intelligence (AI) offers the potential to analyze large and complex datasets in order to improve predicative, preventive, and personalized medicine (3P medicine). This Special Issue plans to give an overview of the most recent advances in the field of AI and their application potential in 3P medicine.

Potential topics include, but are not limited to:

  • Cutting edge advances in AI in terms of medical and health big data.
  • Application of AI in risk stratifying of diseases.
  • Risk factor screening using AI.
  • Application of AI in diagnosis.
  • Application of AI in prognosis.
  • Evaluation of the therapeutic effects of using AI.

Prof. Dr. Youxin Wang
Dr. Ming Feng
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Non-Communicable Diseases
  • Big data 
  • 3P Medicine
  • Precision Medicine
  • Artificial Intelligence
  • Machine Learning 
  • Diagnosis
  • Prognosis

Published Papers (15 papers)

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11 pages, 1014 KiB  
Article
Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults
by Qing Liu, Qing Zhou, Yifeng He, Jingui Zou, Yan Guo and Yaqiong Yan
J. Pers. Med. 2022, 12(7), 1055; https://doi.org/10.3390/jpm12071055 - 27 Jun 2022
Cited by 7 | Viewed by 2022
Abstract
Identifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of incident [...] Read more.
Identifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of incident diabetes. We used data from the free medical examination service project for elderly people who were 65 years or older to develop logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) machine learning models for the follow-up results of 2019 and 2020 and performed internal validation. The receiver operating characteristic (ROC), sensitivity, specificity, accuracy, and F1 score were used to select the model with better performance. The average annual progression rate to diabetes in prediabetic elderly people was 14.21%. Each model was trained using eight features and one outcome variable from 9607 prediabetic individuals, and the performance of the models was assessed in 2402 prediabetes patients. The predictive ability of four models in the first year was better than in the second year. The XGBoost model performed relatively efficiently (ROC: 0.6742 for 2019 and 0.6707 for 2020). We established and compared four machine learning models to predict the risk of progression from prediabetes to diabetes. Although there was little difference in the performance of the four models, the XGBoost model had a relatively good ROC value, which might perform well in future exploration in this field. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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16 pages, 2033 KiB  
Article
Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques
by Qing Liu, Miao Zhang, Yifeng He, Lei Zhang, Jingui Zou, Yaqiong Yan and Yan Guo
J. Pers. Med. 2022, 12(6), 905; https://doi.org/10.3390/jpm12060905 - 31 May 2022
Cited by 18 | Viewed by 2646
Abstract
Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the [...] Read more.
Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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14 pages, 8052 KiB  
Article
Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning
by Na Shi, Lan Lan, Jiawei Luo, Ping Zhu, Thomas R. W. Ward, Peter Szatmary, Robert Sutton, Wei Huang, John A. Windsor, Xiaobo Zhou and Qing Xia
J. Pers. Med. 2022, 12(4), 616; https://doi.org/10.3390/jpm12040616 - 11 Apr 2022
Cited by 2 | Viewed by 1711
Abstract
Background: Current approaches to predicting intervention needs and mortality have reached 65–85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning [...] Read more.
Background: Current approaches to predicting intervention needs and mortality have reached 65–85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML). Methods: Data were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model’s performance. Results: Three datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores. Conclusions: ML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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14 pages, 1974 KiB  
Article
Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD
by Guanhua Dou, Dongkai Shan, Kai Wang, Xi Wang, Zinuan Liu, Wei Zhang, Dandan Li, Bai He, Jing Jing, Sicong Wang, Yundai Chen and Junjie Yang
J. Pers. Med. 2022, 12(4), 596; https://doi.org/10.3390/jpm12040596 - 7 Apr 2022
Cited by 1 | Viewed by 1834
Abstract
Conventional prognostic risk analysis in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings, whereas machine learning (ML) algorithms include a greater number and complexity of variables. Therefore, this paper aimed to explore the predictive value of [...] Read more.
Conventional prognostic risk analysis in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings, whereas machine learning (ML) algorithms include a greater number and complexity of variables. Therefore, this paper aimed to explore the predictive value of integrating coronary plaque information from coronary computed tomographic angiography (CCTA) with ML to predict major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease (CAD). Patients who underwent CCTA due to suspected coronary artery disease with a 30-month follow-up for MACEs were included. We collected demographic characteristics, cardiovascular risk factors, and information on coronary plaques by analyzing CCTA information (plaque length, plaque composition and coronary artery stenosis of 18 coronary artery segments, coronary dominance, myocardial bridge (MB), and patients with vulnerable plaque) and follow-up information (cardiac death, nonfatal myocardial infarction and unstable angina requiring hospitalization). An ML algorithm was used for survival analysis (CoxBoost). This analysis showed that chest symptoms, the stenosis severity of the proximal anterior descending branch, and the stenosis severity of the middle right coronary artery were among the top three variables in the ML model. After the 22nd month of follow-up, in the testing dataset, ML showed the largest C-index and AUC compared with Cox regression, SIS, SIS score + clinical factors, and clinical factors. The DCA of all the models showed that the net benefit of the ML model was the highest when the treatment threshold probability was between 1% and 9%. Integrating coronary plaque information from CCTA based on ML technology provides a feasible and superior method to assess prognosis in patients with suspected coronary artery disease over an approximately three-year period. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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13 pages, 1976 KiB  
Article
Development and Evaluation of a Machine Learning Prediction Model for Small-for-Gestational-Age Births in Women Exposed to Radiation before Pregnancy
by Xi Bai, Zhibo Zhou, Yunyun Luo, Hongbo Yang, Huijuan Zhu, Shi Chen and Hui Pan
J. Pers. Med. 2022, 12(4), 550; https://doi.org/10.3390/jpm12040550 - 31 Mar 2022
Cited by 6 | Viewed by 2037
Abstract
Exposure to radiation has been associated with increased risk of delivering small-for-gestational-age (SGA) newborns. There are no tools to predict SGA newborns in pregnant women exposed to radiation before pregnancy. Here, we aimed to develop an array of machine learning (ML) models to [...] Read more.
Exposure to radiation has been associated with increased risk of delivering small-for-gestational-age (SGA) newborns. There are no tools to predict SGA newborns in pregnant women exposed to radiation before pregnancy. Here, we aimed to develop an array of machine learning (ML) models to predict SGA newborns in women exposed to radiation before pregnancy. Patients’ data was obtained from the National Free Preconception Health Examination Project from 2010 to 2012. The data were randomly divided into a training dataset (n = 364) and a testing dataset (n = 91). Eight various ML models were compared for solving the binary classification of SGA prediction, followed by a post hoc explainability based on the SHAP model to identify and interpret the most important features that contribute to the prediction outcome. A total of 455 newborns were included, with the occurrence of 60 SGA births (13.2%). Overall, the model obtained by extreme gradient boosting (XGBoost) achieved the highest area under the receiver-operating-characteristic curve (AUC) in the testing set (0.844, 95% confidence interval (CI): 0.713–0.974). All models showed satisfied AUCs, except for the logistic regression model (AUC: 0.561, 95% CI: 0.355–0.768). After feature selection by recursive feature elimination (RFE), 15 features were included in the final prediction model using the XGBoost algorithm, with an AUC of 0.821 (95% CI: 0.650–0.993). ML algorithms can generate robust models to predict SGA newborns in pregnant women exposed to radiation before pregnancy, which may thus be used as a prediction tool for SGA newborns in high-risk pregnant women. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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13 pages, 5160 KiB  
Article
Physical Activity Is Associated with a Lower Risk of Osteoporotic Fractures in Osteoporosis: A Longitudinal Study
by Chan-Yang Min, Jung-Woo Lee, Bong-Cheol Kwon, Mi-Jung Kwon, Ji-Hee Kim, Joo-Hee Kim, Woo-Jin Bang and Hyo-Geun Choi
J. Pers. Med. 2022, 12(3), 491; https://doi.org/10.3390/jpm12030491 - 18 Mar 2022
Cited by 1 | Viewed by 2025
Abstract
The purpose of our study was to examine the occurrence of osteoporotic fractures (fxs) according to the level of physical activity (PA) among osteoporosis using the Korean National Health Insurance Service (NHIS) customized database. From NHIS data from 2009 to 2017, osteoporosis was [...] Read more.
The purpose of our study was to examine the occurrence of osteoporotic fractures (fxs) according to the level of physical activity (PA) among osteoporosis using the Korean National Health Insurance Service (NHIS) customized database. From NHIS data from 2009 to 2017, osteoporosis was selected as requested. PA was classified into ‘high PA’ (n = 58,620), ‘moderate PA’ (n = 58,620), and ‘low PA’ (n = 58,620) and were matched in a 1:1:1 ratio by gender, age, income within the household unit, and region of residence. A stratified Cox proportional hazard model was used to calculate hazard ratios (HRs) for each type of fx comparing PA groups. The ‘low PA’ group was the reference group. For vertebral fx, the adjusted HR (95% confidence intervals (CIs)) was 0.27 (0.26–0.28) for the ‘high PA’ group and 0.43 (0.42–0.44) for the ‘moderate PA’ group. For hip fx, the adjusted HR (95% CIs) was 0.37 (0.34–0.40) for the ‘high PA’ group and 0.51 (0.47–0.55) for the ‘moderate PA’ group. For distal radius fx, the adjusted HR (95% CIs) was 0.32 (0.30–0.33) for the ‘high PA’ group and 0.46 (0.45–0.48) for the ‘moderate PA’ group. The results of this study suggest that a higher intensity of PA is associated with a lower risk of osteoporotic fxs, including vertebral fx, hip fx, and distal radius fx. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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12 pages, 3028 KiB  
Article
An Entropy Approach to Multiple Sclerosis Identification
by Gerardo Alfonso Perez and Javier Caballero Villarraso
J. Pers. Med. 2022, 12(3), 398; https://doi.org/10.3390/jpm12030398 - 4 Mar 2022
Cited by 1 | Viewed by 1813
Abstract
Multiple sclerosis (MS) is a relatively common neurodegenerative illness that frequently causes a large level of disability in patients. While its cause is not fully understood, it is likely due to a combination of genetic and environmental factors. Diagnosis of multiple sclerosis through [...] Read more.
Multiple sclerosis (MS) is a relatively common neurodegenerative illness that frequently causes a large level of disability in patients. While its cause is not fully understood, it is likely due to a combination of genetic and environmental factors. Diagnosis of multiple sclerosis through a simple clinical examination might be challenging as the evolution of the illness varies significantly from patient to patient, with some patients experiencing long periods of remission. In this regard, having a quick and inexpensive tool to help identify the illness, such as DNA CpG (cytosine-phosphate-guanine) methylation, might be useful. In this paper, a technique is presented, based on the concept of Shannon Entropy, to select CpGs as inputs for non-linear classification algorithms. It will be shown that this approach generates accurate classifications that are a statistically significant improvement over using all the data available or randomly selecting the same number of CpGs. The analysis controlled for factors such as age, gender and smoking status of the patient. This approach managed to reduce the number of CpGs used while at the same time significantly increasing the accuracy. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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14 pages, 1163 KiB  
Article
Polymorphisms and Gene-Gene Interaction in AGER/IL6 Pathway Might Be Associated with Diabetic Ischemic Heart Disease
by Kuo Liu, Yunyi Xie, Qian Zhao, Wenjuan Peng, Chunyue Guo, Jie Zhang and Ling Zhang
J. Pers. Med. 2022, 12(3), 392; https://doi.org/10.3390/jpm12030392 - 4 Mar 2022
Cited by 2 | Viewed by 2180
Abstract
Background: Although the genetic susceptibility to diabetes and ischemic heart disease (IHD) has been well demonstrated, studies aimed at exploring gene variations associated with diabetic IHD are still limited; Methods: Our study included 204 IHD cases who had been diagnosed with diabetes before [...] Read more.
Background: Although the genetic susceptibility to diabetes and ischemic heart disease (IHD) has been well demonstrated, studies aimed at exploring gene variations associated with diabetic IHD are still limited; Methods: Our study included 204 IHD cases who had been diagnosed with diabetes before the diagnosis of IHD and 882 healthy controls. Logistic regression was used to find the association of candidate SNPs and polygenic risk score (PRS) with diabetic IHD. The diagnostic accuracy was represented with AUC. Generalized multifactor dimensionality reduction (GMDR) was used to illustrate gene-gene interactions; Results: For IL6R rs4845625, the CT and TT genotypes were associated with a lower risk of diabetic IHD than the CC genotype (OR = 0.619, p = 0.033; OR = 0.542, p = 0.025, respectively). Haplotypes in the AGER gene (rs184003-rs1035798-rs2070600-rs1800624) and IL6R gene (rs7529229-rs4845625-rs4129267-rs7514452-rs4072391) were both significantly associated with diabetic IHD. PRS was associated with the disease (OR = 1.100, p = 0.005) after adjusting for covariates, and the AUC were 0.763 (p < 0.001). The GMDR analysis suggested that rs184003 and rs4845625 were the best interaction model after permutation testing (p = 0.001) with a cross-validation consistency of 10/10; Conclusions: SNPs and haplotypes in the AGER and IL6R genes and the interaction of rs184003 and rs4845625 were significantly associated with diabetic IHD. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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14 pages, 20316 KiB  
Article
Multi-Task Deep Learning Approach for Simultaneous Objective Response Prediction and Tumor Segmentation in HCC Patients with Transarterial Chemoembolization
by Yuze Li, Ziming Xu, Chao An, Huijun Chen and Xiao Li
J. Pers. Med. 2022, 12(2), 248; https://doi.org/10.3390/jpm12020248 - 9 Feb 2022
Cited by 6 | Viewed by 2705
Abstract
This study aimed to develop a deep learning-based model to simultaneously perform the objective response (OR) and tumor segmentation for hepatocellular carcinoma (HCC) patients who underwent transarterial chemoembolization (TACE) treatment. A total of 248 patients from two hospitals were retrospectively included and divided [...] Read more.
This study aimed to develop a deep learning-based model to simultaneously perform the objective response (OR) and tumor segmentation for hepatocellular carcinoma (HCC) patients who underwent transarterial chemoembolization (TACE) treatment. A total of 248 patients from two hospitals were retrospectively included and divided into the training, internal validation, and external testing cohort. A network consisting of an encoder pathway, a prediction pathway, and a segmentation pathway was developed, and named multi-DL (multi-task deep learning), using contrast-enhanced CT images as input. We compared multi-DL with other deep learning-based OR prediction and tumor segmentation methods to explore the incremental value of introducing the interconnected task into a unified network. Additionally, the clinical model was developed using multivariate logistic regression to predict OR. Results showed that multi-DL could achieve the highest AUC of 0.871 in OR prediction and the highest dice coefficient of 73.6% in tumor segmentation. Furthermore, multi-DL can successfully perform the risk stratification that the low-risk and high-risk patients showed a significant difference in survival (p = 0.006). In conclusion, the proposed method may provide a useful tool for therapeutic regime selection in clinical practice. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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13 pages, 1816 KiB  
Article
Machine Learning Prediction of Visual Outcome after Surgical Decompression of Sellar Region Tumors
by Nidan Qiao, Yichen Ma, Xiaochen Chen, Zhao Ye, Hongying Ye, Zhaoyun Zhang, Yongfei Wang, Zhaozeng Lu, Zhiliang Wang, Yiqin Xiao and Yao Zhao
J. Pers. Med. 2022, 12(2), 152; https://doi.org/10.3390/jpm12020152 - 25 Jan 2022
Cited by 3 | Viewed by 2343
Abstract
Introduction: This study aims to develop a machine learning-based model integrating clinical and ophthalmic features to predict visual outcomes after transsphenoidal resection of sellar region tumors. Methods: Adult patients with optic chiasm compression by a sellar region tumor were examined to develop a [...] Read more.
Introduction: This study aims to develop a machine learning-based model integrating clinical and ophthalmic features to predict visual outcomes after transsphenoidal resection of sellar region tumors. Methods: Adult patients with optic chiasm compression by a sellar region tumor were examined to develop a model, and an independent retrospective cohort and a prospective cohort were used to validate our model. Predictors included demographic information, and ophthalmic and laboratory test results. We defined “recovery” as more than 5% for a p-value in mean deviation compared with the general population in the follow-up. Seven machine learning classifiers were employed, and the best-performing algorithm was selected. A decision curve analysis was used to assess the clinical usefulness of our model by estimating net benefit. We developed a nomogram based on essential features ranked by the SHAP score. Results: We included 159 patients (57.2% male), and the mean age was 42.3 years old. Among them, 96 patients were craniopharyngiomas and 63 patients were pituitary adenomas. Larger tumors (3.3 cm vs. 2.8 cm in tumor height) and craniopharyngiomas (73.6%) were associated with a worse prognosis (p < 0.001). Eyes with better outcomes were those with better visual field and thicker ganglion cell layer before operation. The ensemble model yielded the highest AUC of 0.911 [95% CI, 0.885–0.938], and the corresponding accuracy was 84.3%, with 0.863 in sensitivity and 0.820 in specificity. The model yielded AUCs of 0.861 and 0.843 in the two validation cohorts. Our model provided greater net benefit than the competing extremes of intervening in all or no patients in the decision curve analysis. A model explanation using SHAP score demonstrated that visual field, ganglion cell layer, tumor height, total thyroxine, and diagnosis were the most important features in predicting visual outcome. Conclusion: SHAP score can be a valuable resource for healthcare professionals in identifying patients with a higher risk of persistent visual deficit. The large-scale and prospective application of the proposed model would strengthen its clinical utility and universal applicability in practice. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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9 pages, 2467 KiB  
Article
Predicting Venous Thrombosis in Osteoarthritis Using a Machine Learning Algorithm: A Population-Based Cohort Study
by Chao Lu, Jiayin Song, Hui Li, Wenxing Yu, Yangquan Hao, Ke Xu and Peng Xu
J. Pers. Med. 2022, 12(1), 114; https://doi.org/10.3390/jpm12010114 - 15 Jan 2022
Cited by 12 | Viewed by 2293
Abstract
Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish [...] Read more.
Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi’an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren–Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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11 pages, 1189 KiB  
Article
Machine Learning-Based Approaches for Prediction of Patients’ Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage
by Rui Guo, Renjie Zhang, Ran Liu, Yi Liu, Hao Li, Lu Ma, Min He, Chao You and Rui Tian
J. Pers. Med. 2022, 12(1), 112; https://doi.org/10.3390/jpm12010112 - 14 Jan 2022
Cited by 12 | Viewed by 1881
Abstract
Spontaneous intracerebral hemorrhage (SICH) has been common in China with high morbidity and mortality rates. This study aims to develop a machine learning (ML)-based predictive model for the 90-day evaluation after SICH. We retrospectively reviewed 751 patients with SICH diagnosis and analyzed clinical, [...] Read more.
Spontaneous intracerebral hemorrhage (SICH) has been common in China with high morbidity and mortality rates. This study aims to develop a machine learning (ML)-based predictive model for the 90-day evaluation after SICH. We retrospectively reviewed 751 patients with SICH diagnosis and analyzed clinical, radiographic, and laboratory data. A modified Rankin scale (mRS) of 0–2 was defined as a favorable functional outcome, while an mRS of 3–6 was defined as an unfavorable functional outcome. We evaluated 90-day functional outcome and mortality to develop six ML-based predictive models and compared their efficacy with a traditional risk stratification scale, the intracerebral hemorrhage (ICH) score. The predictive performance was evaluated by the areas under the receiver operating characteristic curves (AUC). A total of 553 patients (73.6%) reached the functional outcome at the 3rd month, with the 90-day mortality rate of 10.2%. Logistic regression (LR) and logistic regression CV (LRCV) showed the best predictive performance for functional outcome (AUC = 0.890 and 0.887, respectively), and category boosting presented the best predictive performance for the mortality (AUC = 0.841). Therefore, ML might be of potential assistance in the prediction of the prognosis of SICH. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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11 pages, 935 KiB  
Article
Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores
by Jie Wang, Zhuo Wang, Ning Liu, Caiyan Liu, Chenhui Mao, Liling Dong, Jie Li, Xinying Huang, Dan Lei, Shanshan Chu, Jianyong Wang and Jing Gao
J. Pers. Med. 2022, 12(1), 37; https://doi.org/10.3390/jpm12010037 - 4 Jan 2022
Cited by 9 | Viewed by 2316
Abstract
Background: Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery [...] Read more.
Background: Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery (NTB) by machine learning and develop new classification models for distinguishing mild cognitive impairment (MCI) and dementia among individuals with MMSE ≥ 26. Methods: 375 participants with MMSE ≥ 26 were assigned a diagnosis of cognitively unimpaired (CU) (n = 67), MCI (n = 174), or dementia (n = 134). We compared the performance of five machine learning algorithms, including logistic regression, decision tree, SVM, XGBoost, and random forest (RF), in identifying MCI and dementia. Results: RF performed best in identifying MCI and dementia. Six neuropsychological subtests with high-importance features were selected to form a simplified NTB, and the test time was cut in half. The AUC of the RF model was 0.89 for distinguishing MCI from CU, and 0.84 for distinguishing dementia from nondementia. Conclusions: This simplified cognitive assessment model can be useful for the diagnosis of MCI and dementia in patients with normal MMSE. It not only optimizes the content of cognitive evaluation, but also improves diagnosis and reduces missed diagnosis. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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15 pages, 2142 KiB  
Article
Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition
by Danning Wu, Shi Chen, Yuelun Zhang, Huabing Zhang, Qing Wang, Jianqiang Li, Yibo Fu, Shirui Wang, Hongbo Yang, Hanze Du, Huijuan Zhu, Hui Pan and Zhen Shen
J. Pers. Med. 2021, 11(11), 1172; https://doi.org/10.3390/jpm11111172 - 10 Nov 2021
Cited by 6 | Viewed by 2918
Abstract
Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we [...] Read more.
Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we summarized and quantitatively analyzed studies on the diagnosis of heterogeneous diseases on the basis on facial features. In pooled data from 20 systematically identified studies involving 7 single diseases and 12,557 subjects, quantitative random-effects models revealed a pooled sensitivity of 89% (95% CI 82% to 93%) and a pooled specificity of 92% (95% CI 87% to 95%). A new index, the facial recognition intensity (FRI), was established to describe the complexity of the association of diseases with facial phenotypes. Meta-regression revealed the important contribution of FRI to heterogeneous diagnostic accuracy (p = 0.021), and a similar result was found in subgroup analyses (p = 0.003). An appropriate increase in the training size and the use of deep learning models helped to improve the diagnostic accuracy for diseases with low FRI, although no statistically significant association was found between accuracy and photographic resolution, training size, AI architecture, and number of diseases. In addition, a novel hypothesis is proposed for universal rules in AI performance, providing a new idea that could be explored in other AI applications. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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14 pages, 779 KiB  
Review
Artificial Intelligence in Cardiovascular Atherosclerosis Imaging
by Jia Zhang, Ruijuan Han, Guo Shao, Bin Lv and Kai Sun
J. Pers. Med. 2022, 12(3), 420; https://doi.org/10.3390/jpm12030420 - 8 Mar 2022
Cited by 10 | Viewed by 3087
Abstract
At present, artificial intelligence (AI) has already been applied in cardiovascular imaging (e.g., image segmentation, automated measurements, and eventually, automated diagnosis) and it has been propelled to the forefront of cardiovascular medical imaging research. In this review, we presented the current status of [...] Read more.
At present, artificial intelligence (AI) has already been applied in cardiovascular imaging (e.g., image segmentation, automated measurements, and eventually, automated diagnosis) and it has been propelled to the forefront of cardiovascular medical imaging research. In this review, we presented the current status of artificial intelligence applied to image analysis of coronary atherosclerotic plaques, covering multiple areas from plaque component analysis (e.g., identification of plaque properties, identification of vulnerable plaque, detection of myocardial function, and risk prediction) to risk prediction. Additionally, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging of atherosclerotic plaques, as well as lessons that can be learned from other areas. The continuous development of computer science and technology may further promote the development of this field. Full article
(This article belongs to the Special Issue Non-communicable Diseases, Big Data and Artificial Intelligence)
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