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Dubin JA, Bains SS, Hameed D, Chen Z, Gaertner E, Nace J, Mont MA, Delanois RE. Evaluating ChatGPT's Utility in Medicine Guidelines Through Web Search Analysis. Perm J 2024; 28:55-69. [PMID: 38665043 PMCID: PMC11232914 DOI: 10.7812/tpp/23.126] [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: 07/10/2024]
Abstract
INTRODUCTION With the rise of machine learning applications in health care, shifts in medical fields that rely on precise prognostic models and pattern detection tools are anticipated in the near future. Chat Generative Pretrained Transformer (ChatGPT) is a recent machine learning innovation known for producing text that mimics human conversation. To gauge ChatGPT's capability in addressing patient inquiries, the authors set out to juxtapose it with Google Search, America's predominant search engine. Their comparison focused on: 1) the top questions related to clinical practice guidelines from the American Academy of Family Physicians by category and subject; 2) responses to these prevalent questions; and 3) the top questions that elicited a numerical reply. METHODS Utilizing a freshly installed Google Chrome browser (version 109.0.5414.119), the authors conducted a Google web search (www.google.com) on March 4, 2023, ensuring minimal influence from personalized search algorithms. Search phrases were derived from the clinical guidelines of the American Academy of Family Physicians. The authors prompted ChatGPT with: "Search Google using the term '(refer to search terms)' and document the top four questions linked to the term." The same 25 search terms were employed. The authors cataloged the primary 4 questions and their answers for each term, resulting in 100 questions and answers. RESULTS Of the 100 questions, 42% (42 questions) were consistent across all search terms. ChatGPT predominantly sourced from academic (38% vs 15%, p = 0.0002) and government (50% vs 39%, p = 0.12) domains, whereas Google web searches leaned toward commercial sources (32% vs 11%, p = 0.0002). Thirty-nine percent (39 questions) of the questions yielded divergent answers between the 2 platforms. Notably, 16 of the 39 distinct answers from ChatGPT lacked a numerical reply, instead advising a consultation with a medical professional for health guidance. CONCLUSION Google Search and ChatGPT present varied questions and answers for both broad and specific queries. Both patients and doctors should exercise prudence when considering ChatGPT as a digital health adviser. It's essential for medical professionals to assist patients in accurately communicating their online discoveries and ensuing inquiries for a comprehensive discussion.
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Affiliation(s)
- Jeremy A Dubin
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, MD, USA
| | - Sandeep S Bains
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, MD, USA
| | - Daniel Hameed
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, MD, USA
| | - Zhongming Chen
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, MD, USA
| | - Erica Gaertner
- Department of Family Medicine, LifeBridge Health, Sinai Hospital, Baltimore, MD, USA
| | - James Nace
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, MD, USA
| | - Michael A Mont
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, MD, USA
| | - Ronald E Delanois
- LifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopedics, Baltimore, MD, USA
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Sahid MA, Babar MUH, Uddin MP. Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics. PLoS One 2024; 19:e0300785. [PMID: 38753669 PMCID: PMC11098411 DOI: 10.1371/journal.pone.0300785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/05/2024] [Indexed: 05/18/2024] Open
Abstract
Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection-filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics.
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Affiliation(s)
- Md Abdus Sahid
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Mozaddid Ul Hoque Babar
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Md Palash Uddin
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
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Bernstorff M, Hansen L, Enevoldsen K, Damgaard J, Hæstrup F, Perfalk E, Danielsen AA, Østergaard SD. Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness. Acta Psychiatr Scand 2024. [PMID: 38575118 DOI: 10.1111/acps.13687] [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] [Received: 11/20/2023] [Revised: 03/08/2024] [Accepted: 03/28/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness. METHODS The study was based on routine clinical data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74,880 patients with 1.59 million psychiatric service contacts were included in the analyses. We created 1343 potential predictors from 51 source variables, covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised as HbA1c ≥48 mmol/mol, fasting plasma glucose ≥7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularised logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts. RESULTS The XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%). CONCLUSION A machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.
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Affiliation(s)
- Martin Bernstorff
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Lasse Hansen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Kenneth Enevoldsen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Jakob Damgaard
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Frida Hæstrup
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Center for Humanities Computing, Aarhus University, Aarhus, Denmark
| | - Erik Perfalk
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Andreas Aalkjær Danielsen
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Søren Dinesen Østergaard
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Zou X, Luo Y, Huang Q, Zhu Z, Li Y, Zhang X, Zhou X, Ji L. Differential effect of interventions in patients with prediabetes stratified by a machine learning-based diabetes progression prediction model. Diabetes Obes Metab 2024; 26:97-107. [PMID: 37779358 DOI: 10.1111/dom.15291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 10/03/2023]
Abstract
AIM To investigate whether stratifying participants with prediabetes according to their diabetes progression risks (PR) could affect their responses to interventions. METHODS We developed a machine learning-based model to predict the 1-year diabetes PR (ML-PR) with the least predictors. The model was developed and internally validated in participants with prediabetes in the Pinggu Study (a prospective population-based survey in suburban Beijing; n = 622). Patients from the Beijing Prediabetes Reversion Program cohort (a multicentre randomized control trial to evaluate the efficacy of lifestyle and/or pioglitazone on prediabetes reversion; n = 1936) were stratified to low-, medium- and high-risk groups using ML-PR. Different effect of four interventions within subgroups on prediabetes reversal and diabetes progression was assessed. RESULTS Using least predictors including fasting plasma glucose, 2-h postprandial glucose after 75 g glucose administration, glycated haemoglobin, high-density lipoprotein cholesterol and triglycerides, and the ML algorithm XGBoost, ML-PR successfully predicted the 1-year progression of participants with prediabetes in the Pinggu study [internal area under the curve of the receiver operating characteristic curve 0.80 (0.72-0.89)] and Beijing Prediabetes Reversion Program [external area under the curve of the receiver operating characteristic curve 0.80 (0.74-0.86)]. In the high-risk group pioglitazone plus intensive lifestyle therapy significantly reduced diabetes progression by about 50% at year l and the end of the trial in the high-risk group compared with conventional lifestyle therapy with placebo. In the medium- or low-risk group, intensified lifestyle therapy, pioglitazone or their combination did not show any benefit on diabetes progression and prediabetes reversion. CONCLUSIONS This study suggests personalized treatment for prediabetes according to their PR is necessary. ML-PR model with simple clinical variables may facilitate personal treatment strategies in participants with prediabetes.
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Affiliation(s)
- Xiantong Zou
- Peking University People's Hospital, Beijing, China
| | - Yingying Luo
- Peking University People's Hospital, Beijing, China
| | - Qi Huang
- Peking University People's Hospital, Beijing, China
| | - Zhanxing Zhu
- School of Mathematical Sciences, Peking University, Beijing, China
- Center for Data Science, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Yufeng Li
- Department of Endocrinology, Beijing Friendship Hospital Pinggu Campus, Capital Medical University, Beijing, China
| | | | | | - Linong Ji
- Peking University People's Hospital, Beijing, China
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Yang T, Yang Q, Zhou Y, Wen C. Glucose trend prediction model based on improved wavelet transform and gated recurrent unit. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:17037-17056. [PMID: 37920046 DOI: 10.3934/mbe.2023760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Glucose trend prediction based on continuous glucose monitoring (CGM) data is a crucial step in the implementation of an artificial pancreas (AP). A glucose trend prediction model with high accuracy in real-time can greatly improve the glycemic control effect of the artificial pancreas and effectively prevent the occurrence of hyperglycemia and hypoglycemia. In this paper, we propose an improved wavelet transform threshold denoising algorithm for the non-linearity and non-smoothness of the original CGM data. By quantitatively comparing the mean square error (MSE) and signal-to-noise ratio (SNR) before and after the improvement, we prove that the improved wavelet transform threshold denoising algorithm can reduce the degree of distortion after the smoothing of CGM data and improve the extraction effect of CGM data features at the same time. Based on this finding, we propose a glucose trend prediction model (IWT-GRU) based on the improved wavelet transform threshold denoising algorithm and gated recurrent unit. We compared the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination ($ {\mathrm{R}}^{2} $) of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support vector regression (SVR), Gated Recurrent Unit (GRU) and IWT-GRU on the original CGM monitoring data of 80 patients for 7 consecutive days with different prediction horizon (PH). The results showed that the IWT-GRU model outperformed the other four models. At PH = 45 min, the RMSE was 0.5537 mmol/L, MAPE was 2.2147%, $ {\mathrm{R}}^{2} $ was 0.989 and the average runtime was only 37.2 seconds. Finally, we analyze the limitations of this study and provide an outlook on the future direction of blood glucose trend prediction.
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Affiliation(s)
- Tao Yang
- College of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, Sichuan, China
- Xin-Huangpu Joint Innovation Institute of Chinese Medicine, Guangzhou 510000, Guangdong, China
| | - Qicheng Yang
- College of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, Sichuan, China
- Xin-Huangpu Joint Innovation Institute of Chinese Medicine, Guangzhou 510000, Guangdong, China
| | - Yibo Zhou
- Beijing Certificate Authority Co., Ltd., Beijing 100000, China
| | - Chuanbiao Wen
- Xin-Huangpu Joint Innovation Institute of Chinese Medicine, Guangzhou 510000, Guangdong, China
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Zhang B, Shi H, Wang H. Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach. J Multidiscip Healthc 2023; 16:1779-1791. [PMID: 37398894 PMCID: PMC10312208 DOI: 10.2147/jmdh.s410301] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/12/2023] [Indexed: 07/04/2023] Open
Abstract
Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.
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Affiliation(s)
- Bo Zhang
- Jinling Institute of Science and Technology, Nanjing City, Jiangsu Province, People’s Republic of China
| | - Huiping Shi
- Jinling Institute of Science and Technology, Nanjing City, Jiangsu Province, People’s Republic of China
| | - Hongtao Wang
- School of Life Science, Tonghua Normal University, Tonghua City, Jilin Province, People’s Republic of China
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A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries. Nutrition 2023; 108:111947. [PMID: 36641887 DOI: 10.1016/j.nut.2022.111947] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/29/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND The proper intake of nutrients is essential to the growth and maturation of youngsters. In sub-Saharan Africa, 1 in 7 children dies before age 5 y, and more than a third of these deaths are attributed to malnutrition. The main purpose of this study was to develop a majority voting-based hybrid ensemble (MVBHE) learning model to accelerate the prediction accuracy of malnutrition data of under-five children in sub-Saharan Africa. METHODS This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algorithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model. RESULTS We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%). CONCLUSIONS The random forest algorithm demonstrated the highest prediction accuracy (81%) compared with the decision tree, eXtreme Gradient Boosting, and k-nearest neighbors algorithms. The accuracy was then enhanced to 96% using the MVBHE model. The MVBHE model is recommended by the present study as the best way to predict malnutrition in under-five children.
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Wang D, Pan B, Huang JC, Chen Q, Cui SP, Lang R, Lyu SC. Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma. Front Oncol 2023; 13:1106029. [PMID: 37007095 PMCID: PMC10050553 DOI: 10.3389/fonc.2023.1106029] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/27/2023] [Indexed: 03/17/2023] Open
Abstract
BackgroundDistal cholangiocarcinoma (dCCA), originating from the common bile duct, is greatly associated with a dismal prognosis. A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. In this study, we explored and compared several novel machine learning models that might lead to an improvement in prediction accuracy and treatment options for patients with dCCA.MethodsIn this study, 169 patients with dCCA were recruited and randomly divided into the training cohort (n = 118) and the validation cohort (n = 51), and their medical records were reviewed, including survival outcomes, laboratory values, treatment strategies, pathological results, and demographic information. Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). We measured and compared the performance of models using the receiver operating characteristic (ROC) curve, integrated Brier score (IBS), and concordance index (C-index) following cross-validation. The machine learning model with the best performance was screened out and compared with the TNM Classification using ROC, IBS, and C-index. Finally, patients were stratified based on the model with the best performance to assess whether they benefited from postoperative chemotherapy through the log-rank test.ResultsAmong medical features, five variables, including tumor differentiation, T-stage, lymph node metastasis (LNM), albumin-to-fibrinogen ratio (AFR), and carbohydrate antigen 19-9 (CA19-9), were used to develop machine learning models. In the training cohort and the validation cohort, C-index achieved 0.763 vs. 0.686 (SVM), 0.749 vs. 0.692 (SurvivalTree), 0.747 vs. 0.690 (Coxboost), 0.745 vs. 0.690 (RSF), 0.746 vs. 0.711 (DeepSurv), and 0.724 vs. 0.701 (CoxPH), respectively. The DeepSurv model (0.823 vs. 0.754) had the highest mean area under the ROC curve (AUC) than other models, including SVM (0.819 vs. 0.736), SurvivalTree (0.814 vs. 0.737), Coxboost (0.816 vs. 0.734), RSF (0.813 vs. 0.730), and CoxPH (0.788 vs. 0.753). The IBS of the DeepSurv model (0.132 vs. 0.147) was lower than that of SurvivalTree (0.135 vs. 0.236), Coxboost (0.141 vs. 0.207), RSF (0.140 vs. 0.225), and CoxPH (0.145 vs. 0.196). Results of the calibration chart and decision curve analysis (DCA) also demonstrated that DeepSurv had a satisfactory predictive performance. In addition, the performance of the DeepSurv model was better than that of the TNM Classification in C-index, mean AUC, and IBS (0.746 vs. 0.598, 0.823 vs. 0.613, and 0.132 vs. 0.186, respectively) in the training cohort. Patients were stratified and divided into high- and low-risk groups based on the DeepSurv model. In the training cohort, patients in the high-risk group would not benefit from postoperative chemotherapy (p = 0.519). In the low-risk group, patients receiving postoperative chemotherapy might have a better prognosis (p = 0.035).ConclusionsIn this study, the DeepSurv model was good at predicting prognosis and risk stratification to guide treatment options. AFR level might be a potential prognostic factor for dCCA. For the low-risk group in the DeepSurv model, patients might benefit from postoperative chemotherapy.
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Affiliation(s)
| | | | | | | | | | - Ren Lang
- *Correspondence: Ren Lang, ; Shao-Cheng Lyu,
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Chang M, Ku Y. LSTM model for predicting the daily number of asthma patients in Seoul, South Korea, using meteorological and air pollution data. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:37440-37448. [PMID: 36574119 DOI: 10.1007/s11356-022-24956-9] [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: 08/29/2022] [Accepted: 12/20/2022] [Indexed: 06/18/2023]
Abstract
Asthma is a common respiratory disease that is affected by air pollutants and meteorological factors. In this study, we developed models that predict the daily number of patients receiving treatment for asthma using air pollution and meteorological data. A neural network with long short-term memory (LSTM) and fully connected (FC) layers was used. The daily number of asthma patients in the city of Seoul, the capital of South Korea, was collected from the National Health Insurance Service. The data from 2015 to 2018 were used as the training and validation datasets for model development. Unseen data from 2019 were used for testing. The daily number of asthma patients per 100,000 inhabitants was predicted. The LSTM-FC neural network model achieved a Pearson correlation coefficient of 0.984 (P < 0.001) and root mean square error of 3.472 between the predicted and original values on the unseen testing dataset. The factors that impacted the prediction were the number of asthma patients in the previous time step before the predicted date, type of day (regular day and day after a holiday), minimum temperature, SO2, daily changes in the amount of cloud, and daily changes in diurnal temperature range. We successfully developed a neural network that predicts the onset and exacerbation of asthma, and we identified the crucial influencing air pollutants and meteorological factors. This study will help us to establish appropriate measures according to the daily predicted number of asthma patients and reduce the daily onset and exacerbation of asthma in the susceptible population.
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Affiliation(s)
- Munyoung Chang
- Department of Otorhinolaryngology-Head and Neck Surgery, Chung-Ang University College of Medicine, 84 Heukseok-Ro, Dongjak-Gu, 06974, Seoul, South Korea.
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, 08826, Seoul, South Korea.
| | - Yunseo Ku
- Department of Biomedical Engineering, Chungnam National University College of Medicine, 99 Daehak-Ro, Yuseong-Gu, 34134, Daejeon, South Korea
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Zaizar-Fregoso SA, Lara-Esqueda A, Hernández-Suarez CM, Delgado-Enciso J, Garcia-Nevares A, Canseco-Avila LM, Guzman-Esquivel J, Rodriguez-Sanchez IP, Martinez-Fierro ML, Ceja-Espiritu G, Ochoa-Díaz-Lopez H, Espinoza-Gomez F, Sanchez-Diaz I, Delgado-Enciso I. Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective. J Diabetes Res 2023; 2023:8898958. [PMID: 36846513 PMCID: PMC9949947 DOI: 10.1155/2023/8898958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
Diabetes mellitus is a disease with no cure that can cause complications and even death. Moreover, over time, it will lead to chronic complications. Predictive models have been used to identify people with a tendency to develop diabetes mellitus. At the same time, there is limited information regarding the chronic complications of patients with diabetes. Our study is aimed at creating a machine-learning model that will be able to identify the risk factors of a diabetic patient developing chronic complications such as amputations, myocardial infarction, stroke, nephropathy, and retinopathy. The design is a national nested case-control study with 63,776 patients and 215 predictors with four years of data. Using an XGBoost model, the prediction of chronic complications has an AUC of 84%, and the model has identified the risk factors for chronic complications in patients with diabetes. According to the analysis, the most crucial risk factors based on SHAP values (Shapley additive explanations) are continued management, metformin treatment, age between 68 and 104 years, nutrition consultation, and treatment adherence. But we highlight two exciting findings. The first is a reaffirmation that high blood pressure figures across patients with diabetes without hypertension become a significant risk factor at diastolic > 70 mmHg (OR: 1.095, 95% CI: 1.078-1.113) or systolic > 120 mmHg (OR: 1.147, 95% CI: 1.124-1.171). Furthermore, people with diabetes with a BMI > 32 (overall obesity) (OR: 0.816, 95% CI: 0.8-0.833) have a statistically significant protective factor, which the paradox of obesity may explain. In conclusion, the results we have obtained show that artificial intelligence is a powerful and feasible tool to use for this type of study. However, we suggest that more studies be conducted to verify and elaborate upon our findings.
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Affiliation(s)
| | - Agustin Lara-Esqueda
- Facultad de Psicología y Terapia de la Comunicación Humana de la Universidad Juárez del Estado Durango, Durango 81301, Mexico
| | | | - Josuel Delgado-Enciso
- Fundacion para la Etica Educacion e Investigacion del Cancer del Instituto Estatal de Cancerologia de Colima AC, Colima 28085, Mexico
| | | | - Luis M. Canseco-Avila
- Facultad de Ciencias Químicas Campus IV, Universidad Autónoma de Chiapas, Tapachula, 30700 Chiapas, Mexico
| | - Jose Guzman-Esquivel
- Instituto Mexicano del Seguro Social, Delegación Colima, Villa de Alvarez, 28983 Colima, Mexico
| | - Iram P. Rodriguez-Sanchez
- Facultad de Ciencias Biológicas, Universidad Autonoma de Nuevo Leon, San Nicolás de los Garza, 66455 Nuevo Leon, Mexico
| | | | | | - Hector Ochoa-Díaz-Lopez
- Departamento de Salud, El Colegio de La Frontera Sur, San Cristóbal de Las Casas, 29290 Chiapas, Mexico
| | | | - Iyari Sanchez-Diaz
- Subdirección de Prevención y Protección a la Salud, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Ciudad de Mexico, 14070, Mexico
| | - Ivan Delgado-Enciso
- Facultad de Medicina, Universidad de Colima, Colima 28040, Mexico
- Instituto Estatal de Cancerología, Servicios de Salud del Estado de Colima, Colima 28085, Mexico
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11
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Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr 2022; 14:196. [PMID: 36572938 PMCID: PMC9793536 DOI: 10.1186/s13098-022-00969-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022] Open
Abstract
Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.
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12
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Epah J, Gülec I, Winter S, Dörr J, Geisen C, Haecker E, Link D, Schwab M, Seifried E, Schäfer R. From Unit to Dose: A Machine Learning Approach for Precise Prediction of Hemoglobin and Iron Content in Individual Packed Red Blood Cell Units. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2204077. [PMID: 36333123 PMCID: PMC9798979 DOI: 10.1002/advs.202204077] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/24/2022] [Indexed: 06/16/2023]
Abstract
Transfusion of packed red blood cells (pRBCs) saves lives, but iron overload limits survival of chronically transfused patients. Quality control methods, which involve entering pRBC units and removing them from the blood supply, reveal that hemoglobin (38.5-79.9 g) and heme iron (133.42-276.89 mg) vary substantially between pRBCs. Yet, neither hemoglobin nor iron content can be quantified for individual clinically used pRBCs leading to rules of thumb for pRBC transfusions. Keeping their integrity, the authors seek to predict hemoglobin/iron content of any given pRBC unit applying eight machine learning models on 6,058 pRBCs. Based on thirteen features routinely collected during blood donation, production and quality control testing, the model with best trade-off between performance and complexity in hemoglobin/iron content prediction is identified. Validation of this model in an independent cohort of 2637 pRBCs confirms an adjusted R2 > 0.9 corresponding to a mean absolute prediction error of ≤1.43 g hemoglobin/4.96 mg iron (associated standard deviation: ≤1.13 g hemoglobin/3.92 mg iron). Such unprecedented precise prediction enables reliable pRBC dosing per pharmaceutically active agent, and monitoring iron uptake in patients and individual iron loss in donors. The model is implemented in a free open source web application to facilitate clinical application.
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Affiliation(s)
- Jeremy Epah
- Institute for Transfusion Medicine and Immunohaematology, German Red Cross Blood Donor Service Baden‐Württemberg‐Hessen gGmbHGoethe University Hospital60528Frankfurt am MainGermany
| | - Ilay Gülec
- Institute for Transfusion Medicine and Immunohaematology, German Red Cross Blood Donor Service Baden‐Württemberg‐Hessen gGmbHGoethe University Hospital60528Frankfurt am MainGermany
| | - Stefan Winter
- Dr. Margarete Fischer‐Bosch Institute of Clinical PharmacologyStuttgart, GermanyUniversity of Tübingen72076TübingenGermany
| | - Johanna Dörr
- Institute for Transfusion Medicine and Immunohaematology, German Red Cross Blood Donor Service Baden‐Württemberg‐Hessen gGmbHGoethe University Hospital60528Frankfurt am MainGermany
| | - Christof Geisen
- Institute for Transfusion Medicine and Immunohaematology, German Red Cross Blood Donor Service Baden‐Württemberg‐Hessen gGmbHGoethe University Hospital60528Frankfurt am MainGermany
| | - Eva Haecker
- Institute for Transfusion Medicine and Immunohaematology, German Red Cross Blood Donor Service Baden‐Württemberg‐Hessen gGmbHGoethe University Hospital60528Frankfurt am MainGermany
| | - Dietmar Link
- Institute for Transfusion Medicine and Immunohaematology, German Red Cross Blood Donor Service Baden‐Württemberg‐Hessen gGmbHGoethe University Hospital60528Frankfurt am MainGermany
| | - Matthias Schwab
- Dr. Margarete Fischer‐Bosch Institute of Clinical PharmacologyStuttgart, GermanyUniversity of Tübingen72076TübingenGermany
- Departments of Clinical PharmacologyPharmacy and BiochemistryUniversity of Tübingen72076TübingenGermany
- Cluster of Excellence iFIT (EXC 2180), Image‐Guided and Functionally Instructed Tumor Therapies“University of Tübingen72076TübingenGermany
| | - Erhard Seifried
- Institute for Transfusion Medicine and Immunohaematology, German Red Cross Blood Donor Service Baden‐Württemberg‐Hessen gGmbHGoethe University Hospital60528Frankfurt am MainGermany
| | - Richard Schäfer
- Institute for Transfusion Medicine and Immunohaematology, German Red Cross Blood Donor Service Baden‐Württemberg‐Hessen gGmbHGoethe University Hospital60528Frankfurt am MainGermany
- Institute for Transfusion Medicine and Gene TherapyMedical Center – University of Freiburg79106FreiburgGermany
- Center for Chronic Immunodeficiency (CCI)Medical Center – University of Freiburg79106FreiburgGermany
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13
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Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input. J Pers Med 2022; 12:jpm12071105. [PMID: 35887602 PMCID: PMC9322556 DOI: 10.3390/jpm12071105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/02/2022] [Accepted: 07/04/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability. Methods: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient’s survival 5 years after a cardiac event, i.e., two classes, either yes or no. Results: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%. Conclusions: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients’ 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs.
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Kodama S, Fujihara K, Horikawa C, Kitazawa M, Iwanaga M, Kato K, Watanabe K, Nakagawa Y, Matsuzaka T, Shimano H, Sone H. Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis. J Diabetes Investig 2022; 13:900-908. [PMID: 34942059 PMCID: PMC9077721 DOI: 10.1111/jdi.13736] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/22/2022] Open
Abstract
AIMS/INTRODUCTION Recently, an increasing number of cohort studies have suggested using machine learning (ML) to predict type 2 diabetes mellitus. However, its predictive ability remains inconclusive. This meta-analysis evaluated the current ability of ML algorithms for predicting incident type 2 diabetes mellitus. MATERIALS AND METHODS We systematically searched longitudinal studies published from 1 January 1950 to 17 May 2020 using MEDLINE and EMBASE. Included studies had to compare ML's classification with the actual incidence of type 2 diabetes mellitus, and present data on the number of true positives, false positives, true negatives and false negatives. The dataset for these four values was pooled with a hierarchical summary receiver operating characteristic and a bivariate random effects model. RESULTS There were 12 eligible studies. The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 0.81 (95% confidence interval [CI] 0.67-0.90), 0.82 [95% CI 0.74-0.88], 4.55 [95% CI 3.07-6.75] and 0.23 [95% CI 0.13-0.42], respectively. The area under the summarized receiver operating characteristic curve was 0.88 (95% CI 0.85-0.91). CONCLUSIONS Current ML algorithms have sufficient ability to help clinicians determine whether individuals will develop type 2 diabetes mellitus in the future. However, persons should be cautious before changing their attitude toward future diabetes risk after learning the result of the diabetes prediction test using ML algorithms.
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Affiliation(s)
- Satoru Kodama
- Department of Prevention of Noncommunicable Diseases and Promotion of Health CheckupNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
- Department of Hematology, Endocrinology and MetabolismNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
| | - Kazuya Fujihara
- Department of Hematology, Endocrinology and MetabolismNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
| | - Chika Horikawa
- Department of Health and NutritionFaculty of Human Life StudiesUniversity of Niigata PrefectureNiigataJapan
| | - Masaru Kitazawa
- Department of Prevention of Noncommunicable Diseases and Promotion of Health CheckupNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
| | - Midori Iwanaga
- Department of Prevention of Noncommunicable Diseases and Promotion of Health CheckupNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
- Department of Hematology, Endocrinology and MetabolismNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
| | - Kiminori Kato
- Department of Prevention of Noncommunicable Diseases and Promotion of Health CheckupNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
- Department of Hematology, Endocrinology and MetabolismNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
| | - Kenichi Watanabe
- Department of Prevention of Noncommunicable Diseases and Promotion of Health CheckupNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
- Department of Hematology, Endocrinology and MetabolismNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
| | - Yoshimi Nakagawa
- Division of Complex Biosystem ResearchInstitute of Natural MedicineToyama UniversityToyamaJapan
| | - Takashi Matsuzaka
- Department of Internal Medicine (Endocrinology and Metabolism)Faculty of MedicineUniversity of TsukubaIbarakiJapan
| | - Hitoshi Shimano
- Department of Internal Medicine (Endocrinology and Metabolism)Faculty of MedicineUniversity of TsukubaIbarakiJapan
| | - Hirohito Sone
- Department of Hematology, Endocrinology and MetabolismNiigata University Graduate School of Medical and Dental SciencesNiigataJapan
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15
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Tuppad A, Patil SD. Machine learning for diabetes clinical decision support: a review. ADVANCES IN COMPUTATIONAL INTELLIGENCE 2022; 2:22. [PMID: 35434723 PMCID: PMC9006199 DOI: 10.1007/s43674-022-00034-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/27/2022] [Accepted: 03/03/2022] [Indexed: 12/14/2022]
Abstract
Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.
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Affiliation(s)
- Ashwini Tuppad
- School of Computer Science and Engineering, REVA University, Rukmini Knowledge Park, Kattigenahalli, Bangalore, Karnataka India
| | - Shantala Devi Patil
- School of Computer Science and Engineering, REVA University, Rukmini Knowledge Park, Kattigenahalli, Bangalore, Karnataka India
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16
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Bogart M, Liu Y, Oakland T, Stiegler M. Evaluating Triple Therapy Treatment Pathways in Chronic Obstructive Pulmonary Disease (COPD): A Machine-Learning Predictive Model. Int J Chron Obstruct Pulmon Dis 2022; 17:735-747. [PMID: 35418750 PMCID: PMC8995152 DOI: 10.2147/copd.s336297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 02/27/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose Inhaled triple therapy (TT) comprising a long-acting muscarinic antagonist, long-acting β2 agonist, and inhaled corticosteroid is recommended for symptomatic chronic obstructive pulmonary disease (COPD) patients, or those at risk of exacerbation. However, it is not well understood which patient characteristics contribute most to future exacerbation risk. This study assessed patient predictors associated with future exacerbation time following initiation of TT. Patients and Methods This retrospective cohort study used data from the Optum™ Clinformatics™ Data Mart, a large health claims database in the United States. COPD patients who initiated TT between January 2008 and March 2018 (index) were eligible. Patients were required to be aged ≥18 years at index and have continuous enrollment for the 12 months prior to index (baseline) and the 12 months following index (follow-up). Patients who had received TT during baseline were excluded. Data from eligible patients were analyzed using a reverse engineering forward simulation machine learning platform to predict future COPD exacerbation time. Results Data from 73,625 patients were included. The model found that prior exacerbation was largely correlated with post-index exacerbation time; patients who had ≥4 exacerbation episodes during baseline had an average increase of 32.4 days post-index exacerbation, compared with patients with no exacerbations during baseline. Likewise, ≥2 inpatient visits (effect size 27.1 days), the use of xanthines (effect size 11.5 days), or rheumatoid arthritis (effect size 6.4 days) during baseline were associated with increased exacerbation time. Conversely, diagnosis of anemia (effect size –5.68 days), or oral corticosteroids in the past month (effect size –3.43 days) were associated with reduced exacerbation time. Conclusion Frequent prior exacerbations, healthcare resource utilization, xanthine use, and rheumatoid arthritis were the strongest factors predicting the future increase of exacerbations. These results improve our understanding of exacerbation risk among COPD patients initiating triple therapy.
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Affiliation(s)
- Michael Bogart
- Value Evidence and Outcomes, GlaxoSmithKline, Research Triangle Park, NC, USA
- Correspondence: Michael Bogart, GlaxoSmithKline, Five Moore Drive, PO Box 13398, Research Triangle Park, NC, 27709-3398, USA, Tel +19198897413, Email
| | | | | | - Marjorie Stiegler
- Value Evidence and Outcomes, GlaxoSmithKline, Research Triangle Park, NC, USA
- University of North Carolina Chapel Hill, Chapel Hill, NC, USA
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17
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Schwartz JL, Tseng E, Maruthur NM, Rouhizadeh M. Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm. JMIR Med Inform 2022; 10:e29803. [PMID: 35200154 PMCID: PMC8914791 DOI: 10.2196/29803] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 11/15/2021] [Accepted: 12/04/2021] [Indexed: 01/09/2023] Open
Abstract
Background Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions, so understanding how providers discuss prediabetes with patients will inform how to improve their care. Objective This study aimed to develop a natural language processing (NLP) algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation. Methods We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied 7 machine learning models against our manual annotation. Results Machine learning classifiers were able to achieve classification results that were close to human performance with up to 98% precision and recall to identify prediabetes discussions in clinical documentation. Conclusions We demonstrated that prediabetes discussions can be accurately identified using an NLP algorithm. This approach can be used to understand and identify prediabetes management practices in primary care, thereby informing interventions to improve guideline-concordant care.
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Affiliation(s)
- Jessica L Schwartz
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Division of Hospital Medicine, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Eva Tseng
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins University, Baltimore, MD, United States
| | - Nisa M Maruthur
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins University, Baltimore, MD, United States.,Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
| | - Masoud Rouhizadeh
- Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL, United States.,Division of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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18
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Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: A review. Metabolism 2021; 124:154872. [PMID: 34480920 DOI: 10.1016/j.metabol.2021.154872] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/27/2021] [Accepted: 08/28/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.
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Affiliation(s)
- Thibault Gautier
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America.
| | - Leah B Ziegler
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Matthew S Gerber
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Enrique Campos-Náñez
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Stephen D Patek
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
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19
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Fenta HM, Zewotir T, Muluneh EK. A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. BMC Med Inform Decis Mak 2021; 21:291. [PMID: 34689769 PMCID: PMC8542294 DOI: 10.1186/s12911-021-01652-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/04/2021] [Indexed: 12/23/2022] Open
Abstract
Background Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors.
Method The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models. Results Based on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban–rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones. Conclusion Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01652-1.
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Affiliation(s)
- Haile Mekonnen Fenta
- Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
| | - Temesgen Zewotir
- School of Mathematics, Statistics and Computer Science, College of Agriculture Engineering and Science, University of KwaZulu-Natal, Durban, South Africa
| | - Essey Kebede Muluneh
- School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
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20
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Vänni P, Tejesvi MV, Ainonen S, Renko M, Korpela K, Salo J, Paalanne N, Tapiainen T. Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome. Sci Rep 2021; 11:17483. [PMID: 34471207 PMCID: PMC8410856 DOI: 10.1038/s41598-021-97007-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
Abstract
Delivery mode and perinatal antibiotics influence gut microbiome composition in children. Most microbiome studies have used the sequencing of the bacterial 16S marker gene but have not reported the metabolic function of the gut microbiome, which may mediate biological effects on the host. Here, we used the PICRUSt2 bioinformatics tool to predict the functional profiles of the gut microbiome based on 16S sequencing in two child cohorts. Both Caesarean section and perinatal antibiotics markedly influenced the functional profiles of the gut microbiome at the age of 1 year. In machine learning analysis, bacterial fatty acid, phospholipid, and biotin biosynthesis were the most important pathways that differed according to delivery mode. Proteinogenic amino acid biosynthesis, carbohydrate degradation, pyrimidine deoxyribonucleotide and biotin biosynthesis were the most important pathways differing according to antibiotic exposure. Our study shows that both Caesarean section and perinatal antibiotics markedly influence the predicted metabolic profiles of the gut microbiome at the age of 1 year.
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Affiliation(s)
- Petri Vänni
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland.
| | - Mysore V Tejesvi
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Ecology and Genetics, Faculty of Science, University of Oulu, Oulu, Finland
| | - Sofia Ainonen
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Marjo Renko
- Department of Paediatrics, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Katja Korpela
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
| | - Jarmo Salo
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, Oulu, Finland
| | - Niko Paalanne
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, Oulu, Finland
| | - Terhi Tapiainen
- PEDEGO (Pediatrics, Dermatology, Gynecology, Obstetrics) Research Unit and Medical Research Center Oulu, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
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21
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Bompelli A, Wang Y, Wan R, Singh E, Zhou Y, Xu L, Oniani D, Kshatriya BSA, Balls-Berry J(JE, Zhang R. Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review. HEALTH DATA SCIENCE 2021; 2021:9759016. [PMID: 38487504 PMCID: PMC10880156 DOI: 10.34133/2021/9759016] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 06/28/2021] [Indexed: 03/17/2024]
Abstract
Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes.Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues.Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.
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Affiliation(s)
- Anusha Bompelli
- Department of Pharmaceutical Care & Health Systems, University of Minnesota, USA
| | - Yanshan Wang
- Department of Health Information Management, University of Pittsburgh, USA
| | - Ruyuan Wan
- Department of Computer Science, University of Minnesota, USA
| | - Esha Singh
- Department of Computer Science, University of Minnesota, USA
| | - Yuqi Zhou
- Institute for Health Informatics and College of Pharmacy, University of Minnesota, USA
| | - Lin Xu
- Carlson School of Business, University of Minnesota, USA
| | - David Oniani
- Department of Computer Science and Mathematics, Luther College, USA
| | | | | | - Rui Zhang
- Institute for Health Informatics, Department of Pharmaceutical Care & Health Systems, University of Minnesota, USA
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22
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Punchoo R, Bhoora S, Pillay N. Applications of machine learning in the chemical pathology laboratory. J Clin Pathol 2021; 74:435-442. [PMID: 34117102 DOI: 10.1136/jclinpath-2021-207393] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 02/16/2021] [Accepted: 03/10/2021] [Indexed: 01/05/2023]
Abstract
Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly and accurately, by supervised and unsupervised learning techniques, to provide classification and prediction value outputs. The application of ML to chemical pathology can potentially enhance efficiency at all phases of the laboratory's total testing process. Our review will broadly discuss the theoretical foundation of ML in laboratory medicine. Furthermore, we will explore the current applications of ML to diverse chemical pathology laboratory processes, for example, clinical decision support, error detection in the preanalytical phase, and ML applications in gel-based image analysis and biomarker discovery. ML currently demonstrates exploratory applications in chemical pathology with promising advancements, which have the potential to improve all phases of the chemical pathology total testing pathway.
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Affiliation(s)
- Rivak Punchoo
- Tshwane Academic Division, National Health Laboratory Service, Pretoria, Gauteng, South Africa .,Chemical Pathology, University of Pretoria Faculty of Health Sciences, Pretoria, Gauteng, South Africa
| | - Sachin Bhoora
- Chemical Pathology, University of Pretoria Faculty of Health Sciences, Pretoria, Gauteng, South Africa
| | - Nelishia Pillay
- Computer Science, University of Pretoria Faculty of Engineering Built Environment and IT, Pretoria, Gauteng, South Africa
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23
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Ravaut M, Harish V, Sadeghi H, Leung KK, Volkovs M, Kornas K, Watson T, Poutanen T, Rosella LC. Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. JAMA Netw Open 2021; 4:e2111315. [PMID: 34032855 PMCID: PMC8150694 DOI: 10.1001/jamanetworkopen.2021.11315] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/01/2021] [Indexed: 11/14/2022] Open
Abstract
Importance Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions. Objective To develop and validate a population-level machine learning model for predicting type 2 diabetes 5 years before diabetes onset using administrative health data. Design, Setting, and Participants This decision analytical model study used linked administrative health data from the diverse, single-payer health system in Ontario, Canada, between January 1, 2006, and December 31, 2016. A gradient boosting decision tree model was trained on data from 1 657 395 patients, validated on 243 442 patients, and tested on 236 506 patients. Costs associated with each patient were estimated using a validated costing algorithm. Data were analyzed from January 1, 2006, to December 31, 2016. Exposures A random sample of 2 137 343 residents of Ontario without type 2 diabetes was obtained at study start time. More than 300 features from data sets capturing demographic information, laboratory measurements, drug benefits, health care system interactions, social determinants of health, and ambulatory care and hospitalization records were compiled over 2-year patient medical histories to generate quarterly predictions. Main Outcomes and Measures Discrimination was assessed using the area under the receiver operating characteristic curve statistic, and calibration was assessed visually using calibration plots. Feature contribution was assessed with Shapley values. Costs were estimated in 2020 US dollars. Results This study trained a gradient boosting decision tree model on data from 1 657 395 patients (12 900 257 instances; 6 666 662 women [51.7%]). The developed model achieved a test area under the curve of 80.26 (range, 80.21-80.29), demonstrated good calibration, and was robust to sex, immigration status, area-level marginalization with regard to material deprivation and race/ethnicity, and low contact with the health care system. The top 5% of patients predicted as high risk by the model represented 26% of the total annual diabetes cost in Ontario. Conclusions and Relevance In this decision analytical model study, a machine learning model approach accurately predicted the incidence of diabetes in the population using routinely collected health administrative data. These results suggest that the model could be used to inform decision-making for population health planning and diabetes prevention.
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Affiliation(s)
- Mathieu Ravaut
- Layer 6 AI, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Vinyas Harish
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | | | | | | | - Kathy Kornas
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Tristan Watson
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute of Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada
| | | | - Laura C. Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Institute of Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada
- Institute for Better Health, Trillium Health Partners, Mississauga, Ontario, Canada
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24
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Najafi B, Mishra R. Harnessing Digital Health Technologies to Remotely Manage Diabetic Foot Syndrome: A Narrative Review. ACTA ACUST UNITED AC 2021; 57:medicina57040377. [PMID: 33919683 PMCID: PMC8069817 DOI: 10.3390/medicina57040377] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/05/2021] [Accepted: 04/07/2021] [Indexed: 12/15/2022]
Abstract
About 422 million people worldwide have diabetes and approximately one-third of them have a major risk factor for diabetic foot ulcers, including poor sensation in their feet from peripheral neuropathy and/or poor perfusion to their feet from peripheral artery disease. The current healthcare ecosystem, which is centered on the treatment of established foot disease, often fails to adequately control key reversible risk factors to prevent diabetic foot ulcers leading to unacceptable high foot disease amputation rate, 40% recurrence of ulcers rate in the first year, and high hospital admissions. Thus, the latest diabetic foot ulcer guidelines emphasize that a paradigm shift in research priority from siloed hospital treatments to innovative integrated community prevention is now critical to address the high diabetic foot ulcer burden. The widespread uptake and acceptance of wearable and digital health technologies provide a means to timely monitor major risk factors associated with diabetic foot ulcer, empower patients in self-care, and effectively deliver the remote monitoring and multi-disciplinary prevention needed for those at-risk people and address the health care access disadvantage that people living in remote areas. This narrative review paper summarizes some of the latest innovations in three specific areas, including technologies supporting triaging high-risk patients, technologies supporting care in place, and technologies empowering self-care. While many of these technologies are still in infancy, we anticipate that in response to the Coronavirus Disease 2019 pandemic and current unmet needs to decentralize care for people with foot disease, we will see a new wave of innovations in the area of digital health, smart wearables, telehealth technologies, and “hospital-at-home” care delivery model. These technologies will be quickly adopted at scale to improve remote management of diabetic foot ulcers, smartly triaging those who need to be seen in outpatient or inpatient clinics, and supporting acute or subacute care at home.
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25
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Alabi RO, Youssef O, Pirinen M, Elmusrati M, Mäkitie AA, Leivo I, Almangush A. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review. Artif Intell Med 2021; 115:102060. [PMID: 34001326 DOI: 10.1016/j.artmed.2021.102060] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 01/27/2021] [Accepted: 03/23/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. OBJECTIVES This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. DATA SOURCES We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. ELIGIBILITY CRITERIA Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. DATA EXTRACTION Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. RESULTS A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. CONCLUSION Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Omar Youssef
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
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26
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Brnabic A, Hess LM. Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Med Inform Decis Mak 2021; 21:54. [PMID: 33588830 PMCID: PMC7885605 DOI: 10.1186/s12911-021-01403-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 01/20/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. METHODS This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. RESULTS A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. CONCLUSIONS A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.
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Affiliation(s)
| | - Lisa M Hess
- Eli Lilly and Company, Indianapolis, IN, USA.
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27
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Pham Q, Gamble A, Hearn J, Cafazzo JA. The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations. J Med Internet Res 2021; 23:e22320. [PMID: 33565982 PMCID: PMC7904401 DOI: 10.2196/22320] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/02/2020] [Accepted: 01/16/2021] [Indexed: 12/13/2022] Open
Abstract
There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.
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Affiliation(s)
- Quynh Pham
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Anissa Gamble
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Jason Hearn
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Joseph A Cafazzo
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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28
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De Silva K, Jönsson D, Demmer RT. A combined strategy of feature selection and machine learning to identify predictors of prediabetes. J Am Med Inform Assoc 2021; 27:396-406. [PMID: 31889178 DOI: 10.1093/jamia/ocz204] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/07/2019] [Accepted: 11/13/2019] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population. MATERIALS AND METHODS We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance. RESULTS Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05). DISCUSSION Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified. CONCLUSION This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.
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Affiliation(s)
- Kushan De Silva
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund,Sweden.,Department of General Practice, School of Primary and Allied Health Care, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Notting Hill, Australia
| | - Daniel Jönsson
- Department of Periodontology, Malmö University, Malmö and Swedish Dental Service of Skane, Lund, Sweden
| | - Ryan T Demmer
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
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29
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Trading-Off Machine Learning Algorithms towards Data-Driven Administrative-Socio-Economic Population Health Management. COMPUTERS 2020. [DOI: 10.3390/computers10010004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Together with population ageing, the number of people suffering from multimorbidity is increasing, up to more than half of the population by 2035. This part of the population is composed by the highest-risk patients, who are, at the same time, the major users of the healthcare systems. The early identification of this sub-population can really help to improve people’s quality of life and reduce healthcare costs. In this paper, we describe a population health management tool based on state-of-the-art intelligent algorithms, starting from administrative and socio-economic data, for the early identification of high-risk patients. The study refers to the population of the Local Health Unit of Central Tuscany in 2015, which amounts to 1,670,129 residents. After a trade-off on machine learning models and on input data, Random Forest applied to 1-year of historical data achieves the best results, outperforming state-of-the-art models. The most important variables for this model, in terms of mean minimal depth, accuracy decrease and Gini decrease, result to be age and some group of drugs, such as high-ceiling diuretics. Thanks to the low inference time and reduced memory usage, the resulting model allows for real-time risk prediction updates whenever new data become available, giving General Practitioners the possibility to early adopt personalised medicine.
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30
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Kuo KM, Talley P, Kao Y, Huang CH. A multi-class classification model for supporting the diagnosis of type II diabetes mellitus. PeerJ 2020; 8:e9920. [PMID: 32974105 PMCID: PMC7487151 DOI: 10.7717/peerj.9920] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 08/20/2020] [Indexed: 12/21/2022] Open
Abstract
Background Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided. Methods This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance. Results The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.
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Affiliation(s)
- Kuang-Ming Kuo
- Department of Healthcare Administration, I-Shou University, Kaohsiung City, Taiwan, Republic of China
| | - Paul Talley
- Department of Applied English, I-Shou University, Kaohsiung City, Taiwan, Republic of China
| | - YuHsi Kao
- Department of Endocrinology, E-Da Hospital, Kaohsiung City, Taiwan, Republic of China
| | - Chi Hsien Huang
- Department of Family Medicine, E-Da Hospital, I-Shou University, Kaohsiung City, Taiwan, Republic of China.,Department of Community Healthcare and Geriatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
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31
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O'Leary OE, Schoetzau A, Amruthalingam L, Geber-Hollbach N, Plattner K, Jenoe P, Schmidt A, Ullmer C, Drawnel FM, Fauser S, Scholl HPN, Passweg J, Halter JP, Goldblum D. Tear Proteomic Predictive Biomarker Model for Ocular Graft Versus Host Disease Classification. Transl Vis Sci Technol 2020; 9:3. [PMID: 32879760 PMCID: PMC7442883 DOI: 10.1167/tvst.9.9.3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/26/2020] [Indexed: 12/20/2022] Open
Abstract
Purpose Diagnosis of ocular graft-versus-host disease (oGVHD) is hampered by a lack of clinically-validated biomarkers. This study aims to predict disease severity on the basis of tear protein expression in mild oGVHD. Methods Forty-nine patients with and without chronic oGVHD after AHCT were recruited to a cross-sectional observational study. Patients were stratified using NIH guidelines for oGVHD severity: NIH 0 (none; n = 14), NIH 1 (mild; n = 9), NIH 2 (moderate; n = 16), and NIH 3 (severe; n = 10). The proteomic profile of tears was analyzed using liquid chromatography-tandem mass spectrometry. Random forest and penalized logistic regression were used to generate classification and prediction models to stratify patients according to disease severity. Results Mass spectrometry detected 785 proteins across all samples. A random forest model used to classify patients by disease grade achieved F1-measure values for correct classification of 0.95 (NIH 0), 0.8 (NIH 1), 0.74 (NIH 2), and 0.83 (NIH 3). A penalized logistic regression model was generated by comparing patients without oGVHD and those with mild oGVHD and applied to identify potential biomarkers present early in disease. A panel of 13 discriminant markers achieved significant diagnostic accuracy in identifying patients with moderate-to-severe disease. Conclusions Our work demonstrates the utility of tear protein biomarkers in classifying oGVHD severity and adds further evidence indicating ocular surface inflammation as a main driver of oGVHD clinical phenotype. Translational Relevance Expression levels of a 13-marker tear protein panel in AHCT patients with mild oGVHD may predict development of more severe oGVHD clinical phenotypes.
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Affiliation(s)
- Olivia E O'Leary
- Department of Ophthalmology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Andreas Schoetzau
- Department of Ophthalmology, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Nadine Geber-Hollbach
- Department of Ophthalmology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Kim Plattner
- Department of Ophthalmology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Paul Jenoe
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Christoph Ullmer
- Pharma Research and Early Development, Roche Innovation Center, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Faye M Drawnel
- Pharma Research and Early Development, Roche Innovation Center, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Sascha Fauser
- Pharma Research and Early Development, Roche Innovation Center, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Hendrik P N Scholl
- Department of Ophthalmology, University Hospital Basel, University of Basel, Basel, Switzerland.,Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland.,Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jakob Passweg
- Department of Hematology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Joerg P Halter
- Department of Hematology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - David Goldblum
- Department of Ophthalmology, University Hospital Basel, University of Basel, Basel, Switzerland
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Perry BI, Upthegrove R, Crawford O, Jang S, Lau E, McGill I, Carver E, Jones PB, Khandaker GM. Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis. Acta Psychiatr Scand 2020; 142:215-232. [PMID: 32654119 DOI: 10.1111/acps.13212] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/06/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVE Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis. METHODS We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance. RESULTS We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved. CONCLUSION Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.
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Affiliation(s)
- B I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - O Crawford
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - S Jang
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - E Lau
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - I McGill
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - E Carver
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G M Khandaker
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
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Zhang L, Shang X, Sreedharan S, Yan X, Liu J, Keel S, Wu J, Peng W, He M. Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study. JMIR Med Inform 2020; 8:e16850. [PMID: 32720912 PMCID: PMC7420582 DOI: 10.2196/16850] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 02/20/2020] [Accepted: 02/26/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. OBJECTIVE We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. METHODS We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. RESULTS Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001). CONCLUSIONS A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.
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Affiliation(s)
- Lei Zhang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Xianwen Shang
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Subhashaan Sreedharan
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Xixi Yan
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Jianbin Liu
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Stuart Keel
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Jinrong Wu
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Wei Peng
- Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
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Vettoretti M, Longato E, Zandonà A, Li Y, Pagán JA, Siscovick D, Carnethon MR, Bertoni AG, Facchinetti A, Di Camillo B. Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions. BMJ Open Diabetes Res Care 2020; 8:8/1/e001223. [PMID: 32747386 PMCID: PMC7398107 DOI: 10.1136/bmjdrc-2020-001223] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 06/03/2020] [Accepted: 06/10/2020] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Many predictive models for incident type 2 diabetes (T2D) exist, but these models are not used frequently for public health management. Barriers to their application include (1) the problem of model choice (some models are applicable only to certain ethnic groups), (2) missing input variables, and (3) the lack of calibration. While (1) and (2) drives to missing predictions, (3) causes inaccurate incidence predictions. In this paper, a combined T2D risk model for public health management that addresses these three issues is developed. RESEARCH DESIGN AND METHODS The combined T2D risk model combines eight existing predictive models by weighted average to overcome the problem of missing incidence predictions. Moreover, the combined model implements a simple recalibration strategy in which the risk scores are rescaled based on the T2D incidence in the target population. The performance of the combined model was compared with that of the eight existing models using data from two test datasets extracted from the Multi-Ethnic Study of Atherosclerosis (MESA; n=1031) and the English Longitudinal Study of Ageing (ELSA; n=4820). Metrics of discrimination, calibration, and missing incidence predictions were used for the assessment. RESULTS The combined T2D model performed well in terms of both discrimination (concordance index: 0.83 on MESA; 0.77 on ELSA) and calibration (expected to observed event ratio: 1.00 on MESA; 1.17 on ELSA), similarly to the best-performing existing models. However, while the existing models yielded a large percentage of missing predictions (17%-45% on MESA; 63%-64% on ELSA), this was negligible with the combined model (0% on MESA, 4% on ELSA). CONCLUSIONS Leveraging on existing literature T2D predictive models, a simple approach based on risk score rescaling and averaging was shown to provide accurate and robust incidence predictions, overcoming the problem of recalibration and missing predictions in practical application of predictive models.
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Affiliation(s)
- Martina Vettoretti
- Department of Information Engineering, School of Engineering, University of Padova, Padova, Italy
| | - Enrico Longato
- Department of Information Engineering, School of Engineering, University of Padova, Padova, Italy
| | - Alessandro Zandonà
- Department of Information Engineering, School of Engineering, University of Padova, Padova, Italy
| | - Yan Li
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - José Antonio Pagán
- Department of Public Health Policy and Management, New York University, New York, New York, USA
- Center for Health Innovation, New York Academy of Medicine, New York, New York, USA
| | - David Siscovick
- Research, Evaluation & Policy, New York Academy of Medicine, New York, New York, USA
| | - Mercedes R Carnethon
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, USA
| | - Alain G Bertoni
- Division of Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina, USA
| | - Andrea Facchinetti
- Department of Information Engineering, School of Engineering, University of Padova, Padova, Italy
| | - Barbara Di Camillo
- Department of Information Engineering, School of Engineering, University of Padova, Padova, Italy
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Liu J, Wang L, Zhang L, Zhang Z, Zhang S. Predictive analytics for blood glucose concentration: an empirical study using the tree-based ensemble approach. LIBRARY HI TECH 2020. [DOI: 10.1108/lht-08-2019-0171] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe primary objective of this study was to recognize critical indicators in predicting blood glucose (BG) through data-driven methods and to compare the prediction performance of four tree-based ensemble models, i.e. bagging with tree regressors (bagging-decision tree [Bagging-DT]), AdaBoost with tree regressors (Adaboost-DT), random forest (RF) and gradient boosting decision tree (GBDT).Design/methodology/approachThis study proposed a majority voting feature selection method by combining lasso regression with the Akaike information criterion (AIC) (LR-AIC), lasso regression with the Bayesian information criterion (BIC) (LR-BIC) and RF to select indicators with excellent predictive performance from initial 38 indicators in 5,642 samples. The selected features were deployed to build the tree-based ensemble models. The 10-fold cross-validation (CV) method was used to evaluate the performance of each ensemble model.FindingsThe results of feature selection indicated that age, corpuscular hemoglobin concentration (CHC), red blood cell volume distribution width (RBCVDW), red blood cell volume and leucocyte count are five most important clinical/physical indicators in BG prediction. Furthermore, this study also found that the GBDT ensemble model combined with the proposed majority voting feature selection method is better than other three models with respect to prediction performance and stability.Practical implicationsThis study proposed a novel BG prediction framework for better predictive analytics in health care.Social implicationsThis study incorporated medical background and machine learning technology to reduce diabetes morbidity and formulate precise medical schemes.Originality/valueThe majority voting feature selection method combined with the GBDT ensemble model provides an effective decision-making tool for predicting BG and detecting diabetes risk in advance.
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Del Parigi A, Tang W, Liu D, Lee C, Pratley R. Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data. Pharmaceut Med 2020; 33:209-217. [PMID: 31933292 DOI: 10.1007/s40290-019-00281-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. OBJECTIVES We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. METHODS Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. RESULTS In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. CONCLUSIONS Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making.
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Affiliation(s)
| | - Wenbo Tang
- Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Dacheng Liu
- Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | | | - Richard Pratley
- Florida Hospital Diabetes Institute, AdventHealth Translational Research Institute for Metabolism and Diabetes, 301 Princeton Ave, Orlando, FL, 32804, USA.
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Mueller L, Berhanu P, Bouchard J, Alas V, Elder K, Thai N, Hitchcock C, Hadzi T, Khalil I, Miller-Wilson LA. Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes. Diabetes Ther 2020; 11:681-699. [PMID: 32009223 PMCID: PMC7048891 DOI: 10.1007/s13300-020-00759-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Indexed: 12/19/2022] Open
Abstract
INTRODUCTION To identify predictors of hypoglycemia and five other clinical and economic outcomes among treated patients with type 2 diabetes (T2D) using machine learning and structured data from a large, geographically diverse administrative claims database. METHODS A retrospective cohort study design was applied to Optum Clinformatics claims data indexed on first antidiabetic prescription date. A hypothesis-free, Bayesian machine learning analytics platform (GNS Healthcare REFS™: Reverse Engineering and Forward Simulation) was used to build ensembles of generalized linear models to predict six outcomes defined in patients' 1-year post-index claims history, including hypoglycemia, antidiabetic class persistence, glycated hemoglobin (HbA1c) target attainment, HbA1c change, T2D-related inpatient admissions, and T2D-related medical costs. A unified set of 388 variables defined in patients' 1-year pre-index claims history constituted the set of predictors for all REFS models. RESULTS The derivation cohort comprised 453,487 patients with a T2D diagnosis between 2014 and 2017. Patients with comorbid conditions had the highest risk of hypoglycemia, including those with prior hypoglycemia (odds ratio [OR] = 25.61) and anemia (OR = 1.29). Other identified risk factors included insulin (OR = 2.84) and sulfonylurea use (OR = 1.80). Biguanide use (OR = 0.75), high blood glucose (> 125 mg/dL vs. < 100 mg/dL, OR = 0.47; 100-125 mg/dL vs. < 100 mg/dL, OR = 0.53), and missing blood glucose test (OR = 0.40) were associated with reduced risk of hypoglycemia. Area under the curve (AUC) of the hypoglycemia model in held-out testing data was 0.77. Patients in the top 15% of predicted hypoglycemia risk constituted 50% of observed hypoglycemic events, 26% of T2D-related inpatient admissions, and 24% of all T2D-related medical costs. CONCLUSIONS Machine learning models built within high-dimensional, real-world data can predict patients at risk of clinical outcomes with a high degree of accuracy, while uncovering important factors associated with outcomes that can guide clinical practice. Targeted interventions towards these patients may help reduce hypoglycemia risk and thereby favorably impact associated economic outcomes relevant to key stakeholders.
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Evaluation of machine learning methodology for the prediction of healthcare resource utilization and healthcare costs in patients with critical limb ischemia-is preventive and personalized approach on the horizon? EPMA J 2020; 11:53-64. [PMID: 32140185 DOI: 10.1007/s13167-019-00196-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/04/2019] [Indexed: 12/16/2022]
Abstract
Background Critical limb ischemia (CLI) is a severe stage of peripheral arterial disease and has a substantial disease and economic burden not only to patients and families, but also to the society and healthcare systems. We aim to develop a personalized prediction model that utilizes baseline patient characteristics prior to CLI diagnosis to predict subsequent 1-year all-cause hospitalizations and total annual healthcare cost, using a novel Bayesian machine learning platform, Reverse Engineering Forward Simulation™ (REFS™), to support a paradigm shift from reactive healthcare to Predictive Preventive and Personalized Medicine (PPPM)-driven healthcare. Methods Patients ≥ 50 years with CLI plus clinical activity for a 6-month pre-index and a 12-month post-index period or death during the post-index period were included in this retrospective cohort of the linked Optum-Humedica databases. REFS™ built an ensemble of 256 predictive models to identify predictors of all-cause hospitalizations and total annual all-cause healthcare costs during the 12-month post-index interval. Results The mean age of 3189 eligible patients was 71.9 years. The most common CLI-related comorbidities were hypertension (79.5%), dyslipidemia (61.4%), coronary atherosclerosis and other heart disease (42.3%), and type 2 diabetes (39.2%). Post-index CLI-related healthcare utilization included inpatient services (14.6%) and ≥ 1 outpatient visits (32.1%). Median annual all-cause and CLI-related costs per patient were $30,514 and $2196, respectively. REFS™ identified diagnosis of skin and subcutaneous tissue infections, cellulitis and abscess, use of nonselective beta-blockers, other aftercare, and osteoarthritis as high confidence predictors of all-cause hospitalizations. The leading predictors for total all-cause costs included region of residence and comorbid health conditions including other diseases of kidney and ureters, blindness of vision defects, chronic ulcer of skin, and chronic ulcer of leg or foot. Conclusions REFS™ identified baseline predictors of subsequent healthcare resource utilization and costs in CLI patients. Machine learning and model-based, data-driven medicine may complement physicians' evidence-based medical services. These findings also support the PPPM framework that a paradigm shift from post-diagnosis disease care to early management of comorbidities and targeted prevention is warranted to deliver a cost-effective medical services and desirable healthcare economy.
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Nguyen BP, Pham HN, Tran H, Nghiem N, Nguyen QH, Do TTT, Tran CT, Simpson CR. Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 182:105055. [PMID: 31505379 DOI: 10.1016/j.cmpb.2019.105055] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 08/17/2019] [Accepted: 08/27/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVE Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step in reducing the burden of diabetes and its complications. Electronic health records (EHRs) for each individual or a population have become important tools in understanding developing trends of diseases. Using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. In this paper, we apply a wide and deep learning model that combines the strength of a generalised linear model with various features and a deep feed-forward neural network to improve the prediction of the onset of type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS The proposed method was implemented by training various models into a logistic loss function using a stochastic gradient descent. We applied this model using public hospital record data provided by the Practice Fusion EHRs for the United States population. The dataset consists of de-identified electronic health records for 9948 patients, of which 1904 have been diagnosed with T2DM. Prediction of diabetes in 2012 was based on data obtained from previous years (2009-2011). The imbalance class of the model was handled by Synthetic Minority Oversampling Technique (SMOTE) for each cross-validation training fold to analyse the performance when synthetic examples for the minority class are created. We used SMOTE of 150 and 300 percent, in which 300 percent means that three new synthetic instances are created for each minority class instance. This results in the approximated diabetes:non-diabetes distributions in the training set of 1:2 and 1:1, respectively. RESULTS Our final ensemble model not using SMOTE obtained an accuracy of 84.28%, area under the receiver operating characteristic curve (AUC) of 84.13%, sensitivity of 31.17% and specificity of 96.85%. Using SMOTE of 150 and 300 percent did not improve AUC (83.33% and 82.12%, respectively) but increased sensitivity (49.40% and 71.57%, respectively) with a moderate decrease in specificity (90.16% and 76.59%, respectively). DISCUSSION AND CONCLUSIONS Our algorithm has further optimised the prediction of diabetes onset using a novel state-of-the-art machine learning algorithm: the wide and deep learning neural network architecture.
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Affiliation(s)
- Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6140, New Zealand.
| | - Hung N Pham
- School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi 100000, Vietnam
| | - Hop Tran
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6140, New Zealand
| | - Nhung Nghiem
- Department of Public Health, University of Otago, 23A Mein Street, Wellington 6021, New Zealand
| | - Quang H Nguyen
- School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi 100000, Vietnam
| | - Trang T T Do
- Institute for Infocomm Research, Agency for Science, Technology and Research, 1 Fusionopolis Way, Singapore 138632, Singapore
| | - Cao Truong Tran
- Faculty of Information Technology, Le Quy Don Technical University, 236 Hoang Quoc Viet Street, Hanoi 100000, Vietnam
| | - Colin R Simpson
- Faculty of Health, Victoria University of Wellington, Kelburn Parade, Wellington 6140, New Zealand; Usher Institute, The University of Edinburgh, Edinburgh, EH89AG, United Kingdom
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Abhari S, Niakan Kalhori SR, Ebrahimi M, Hasannejadasl H, Garavand A. Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods. Healthc Inform Res 2019; 25:248-261. [PMID: 31777668 PMCID: PMC6859270 DOI: 10.4258/hir.2019.25.4.248] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 10/06/2019] [Accepted: 10/09/2019] [Indexed: 12/18/2022] Open
Abstract
Objectives The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. Methods This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. Results The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. Conclusions It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.
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Affiliation(s)
- Shahabeddin Abhari
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Sharareh R Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Ebrahimi
- Department of Internal Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hajar Hasannejadasl
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Garavand
- Department of Health Information Management and Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Spänig S, Emberger-Klein A, Sowa JP, Canbay A, Menrad K, Heider D. The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes. Artif Intell Med 2019; 100:101706. [PMID: 31607340 DOI: 10.1016/j.artmed.2019.101706] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/27/2019] [Accepted: 08/18/2019] [Indexed: 11/16/2022]
Abstract
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer prediction. In the current study, we developed an AI that is able to interact with a patient (virtual doctor) by using a speech recognition and speech synthesis system and thus can autonomously interact with the patient, which is particularly important for, e.g., rural areas, where the availability of primary medical care is strongly limited by low population densities. As a proof-of-concept, the system is able to predict type 2 diabetes mellitus (T2DM) based on non-invasive sensors and deep neural networks. Moreover, the system provides an easy-to-interpret probability estimation for T2DM for a given patient. Besides the development of the AI, we further analyzed the acceptance of young people for AI in healthcare to estimate the impact of such a system in the future.
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Affiliation(s)
- Sebastian Spänig
- Department of Bioinformatics, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, 35037 Marburg, Germany
| | - Agnes Emberger-Klein
- Chair of Marketing and Management of Biogenic Resources, Weihenstephan-Triesdorf University of Applied Sciences/TUM Campus Straubing for Biotechnology and Sustainability, Petersgasse 18, 94315 Straubing, Germany
| | - Jan-Peter Sowa
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
| | - Ali Canbay
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
| | - Klaus Menrad
- Chair of Marketing and Management of Biogenic Resources, Weihenstephan-Triesdorf University of Applied Sciences/TUM Campus Straubing for Biotechnology and Sustainability, Petersgasse 18, 94315 Straubing, Germany
| | - Dominik Heider
- Department of Bioinformatics, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, 35037 Marburg, Germany.
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Makino M, Yoshimoto R, Ono M, Itoko T, Katsuki T, Koseki A, Kudo M, Haida K, Kuroda J, Yanagiya R, Saitoh E, Hoshinaga K, Yuzawa Y, Suzuki A. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci Rep 2019; 9:11862. [PMID: 31413285 PMCID: PMC6694113 DOI: 10.1038/s41598-019-48263-5] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 08/01/2019] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis. AI could predict DKD aggravation with 71% accuracy. Furthermore, the group with DKD aggravation had a significantly higher incidence of hemodialysis than the non-aggravation group, over 10 years (N = 2,900). The new predictive model by AI could detect progression of DKD and may contribute to more effective and accurate intervention to reduce hemodialysis.
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Affiliation(s)
- Masaki Makino
- Department of Endocrinology and Metabolism, Fujita Health University, Toyoake, Aichi, Japan
| | - Ryo Yoshimoto
- Department of Endocrinology and Metabolism, Fujita Health University, Toyoake, Aichi, Japan
| | | | | | | | | | | | - Kyoichi Haida
- Business Process Planning Department, The Dai-ichi Life Insurance Company, Limited, Tokyo, Japan
| | - Jun Kuroda
- IT Business Process Planning Department, The Dai-ichi Life Insurance Company, Limited, Tokyo, Japan
| | - Ryosuke Yanagiya
- Division of Medical Information Systems, Fujita Health University, Toyoake, Aichi, Japan
| | - Eiichi Saitoh
- Department of Rehabilitation Medicine, Fujita Health University, Toyoake, Aichi, Japan
| | | | - Yukio Yuzawa
- Department of Nephrology, Fujita Health University, Toyoake, Aichi, Japan
| | - Atsushi Suzuki
- Department of Endocrinology and Metabolism, Fujita Health University, Toyoake, Aichi, Japan.
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Barrett LA, Payrovnaziri SN, Bian J, He Z. Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2019; 2019:407-416. [PMID: 31258994 PMCID: PMC6568079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Heart disease remains the leading cause of death in the United States. Compared with risk assessment guidelines that require manual calculation of scores, machine learning-based prediction for disease outcomes such as mortality can be utilized to save time and improve prediction accuracy. This study built and evaluated various machine learning models to predict one-year mortality in patients diagnosed with acute myocardial infarction or post myocardial infarction syndrome in the MIMIC-III database. The results of the best performing shallow prediction models were compared to a deep feedforward neural network (Deep FNN) with back propagation. We included a cohort of 5436 admissions. Six datasets were developed and compared. The models applying Logistic Model Trees (LMT) and Simple Logistic algorithms to the combined dataset resulted in the highest prediction accuracy at 85.12% and the highest AUC at .901. In addition, other factors were observed to have an impact on outcomes as well.
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Affiliation(s)
- Laura A Barrett
- School of Information, Florida State University, Tallahassee, Florida, USA
| | | | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Zhe He
- School of Information, Florida State University, Tallahassee, Florida, USA
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Naugler C, Church DL. Automation and artificial intelligence in the clinical laboratory. Crit Rev Clin Lab Sci 2019; 56:98-110. [PMID: 30922144 DOI: 10.1080/10408363.2018.1561640] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The daily operation of clinical laboratories will be drastically impacted by two disruptive technologies: automation and artificial intelligence (the development and use of computer systems able to perform tasks that normally require human intelligence). These technologies will also expand the scope of laboratory medicine. Automation will result in increased efficiency but will require changes to laboratory infrastructure and a shift in workforce training requirements. The application of artificial intelligence to large clinical datasets generated through increased automation will lead to the development of new diagnostic and prognostic models. Together, automation and artificial intelligence will support the move to personalized medicine. Changes in pathology and clinical doctoral scientist training will be necessary to fully participate in these changes. KEYWORDS: Automation; artificial intelligence; deep learning; laboratory medicine.
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Affiliation(s)
- Christopher Naugler
- a Department of Pathology and Laboratory Medicine , University of Calgary , Calgary , Canada.,b Department of Family Medicine , University of Calgary , Calgary , Canada.,c Department of Community Health Sciences , University of Calgary , Calgary , Canada
| | - Deirdre L Church
- a Department of Pathology and Laboratory Medicine , University of Calgary , Calgary , Canada.,d Department of Medicine , University of Calgary , Calgary , Canada
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Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol 2019; 19:64. [PMID: 30890124 PMCID: PMC6425557 DOI: 10.1186/s12874-019-0681-4] [Citation(s) in RCA: 448] [Impact Index Per Article: 89.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 02/14/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data. METHODS We demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples. We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment. RESULTS The trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble. CONCLUSIONS We use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.
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Affiliation(s)
- Jenni A M Sidey-Gibbons
- Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK
| | - Chris J Sidey-Gibbons
- Department of Surgery, Harvard Medical School, 25 Shattuck Street, Boston, 01225, Massachusetts, USA.
- Department of Surgery, Brigham and Women's Hospital, 75 Francis Street, Boston, 01225, Massachusetts, USA.
- University of Cambridge Psychometrics Centre, Trumpington Street, Cambridge, CB2 1AG, UK.
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Bennett CC. REMOVED: Artificial intelligence for diabetes case management: The intersection of physical and mental health. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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Huth C, von Toerne C, Schederecker F, de Las Heras Gala T, Herder C, Kronenberg F, Meisinger C, Rathmann W, Koenig W, Waldenberger M, Roden M, Peters A, Hauck SM, Thorand B. Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study. Eur J Epidemiol 2018; 34:409-422. [PMID: 30599058 PMCID: PMC6451724 DOI: 10.1007/s10654-018-0475-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 12/14/2018] [Indexed: 12/26/2022]
Abstract
The objective of the present study was to identify proteins that contribute to pathophysiology and allow prediction of incident type 2 diabetes or incident prediabetes. We quantified 14 candidate proteins using targeted mass spectrometry in plasma samples of the prospective, population-based German KORA F4/FF4 study (6.5-year follow-up). 892 participants aged 42–81 years were selected using a case-cohort design, including 123 persons with incident type 2 diabetes and 255 persons with incident WHO-defined prediabetes. Prospective associations between protein levels and diabetes, prediabetes as well as continuous fasting and 2 h glucose, fasting insulin and insulin resistance were investigated using regression models adjusted for established risk factors. The best predictive panel of proteins on top of a non-invasive risk factor model or on top of HbA1c, age, and sex was selected. Mannan-binding lectin serine peptidase (MASP) levels were positively associated with both incident type 2 diabetes and prediabetes. Adiponectin was inversely associated with incident type 2 diabetes. MASP, adiponectin, apolipoprotein A-IV, apolipoprotein C-II, C-reactive protein, and glycosylphosphatidylinositol specific phospholipase D1 were associated with individual continuous outcomes. The combination of MASP, apolipoprotein E (apoE) and adiponectin improved diabetes prediction on top of both reference models, while prediabetes prediction was improved by MASP plus CRP on top of the HbA1c model. In conclusion, our mass spectrometric approach revealed a novel association of MASP with incident type 2 diabetes and incident prediabetes. In combination, MASP, adiponectin and apoE improved type 2 diabetes prediction beyond non-invasive risk factors or HbA1c, age and sex.
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Affiliation(s)
- Cornelia Huth
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany.
| | - Christine von Toerne
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Research Unit Protein Science, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Florian Schederecker
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Tonia de Las Heras Gala
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Christian Herder
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christa Meisinger
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Wolfgang Koenig
- Department of Internal Medicine II - Cardiology, University of Ulm Medical Center, Ulm, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Stefanie M Hauck
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Research Unit Protein Science, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
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Maeta K, Nishiyama Y, Fujibayashi K, Gunji T, Sasabe N, Iijima K, Naito T. Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study. JMIR Diabetes 2018; 3:e10212. [PMID: 30478026 PMCID: PMC6288596 DOI: 10.2196/10212] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 08/16/2018] [Accepted: 10/17/2018] [Indexed: 01/10/2023] Open
Abstract
Background A 75-g oral glucose tolerance test (OGTT) provides important information about glucose metabolism, although the test is expensive and invasive. Complete OGTT information, such as 1-hour and 2-hour postloading plasma glucose and immunoreactive insulin levels, may be useful for predicting the future risk of diabetes or glucose metabolism disorders (GMD), which includes both diabetes and prediabetes. Objective We trained several classification models for predicting the risk of developing diabetes or GMD using data from thousands of OGTTs and a machine learning technique (XGBoost). The receiver operating characteristic (ROC) curves and their area under the curve (AUC) values for the trained classification models are reported, along with the sensitivity and specificity determined by the cutoff values of the Youden index. We compared the performance of the machine learning techniques with logistic regressions (LR), which are traditionally used in medical research studies. Methods Data were collected from subjects who underwent multiple OGTTs during comprehensive check-up medical examinations conducted at a single facility in Tokyo, Japan, from May 2006 to April 2017. For each examination, a subject was diagnosed with diabetes or prediabetes according to the American Diabetes Association guidelines. Given the data, 2 studies were conducted: predicting the risk of developing diabetes (study 1) or GMD (study 2). For each study, to apply supervised machine learning methods, the required label data was prepared. If a subject was diagnosed with diabetes or GMD at least once during the period, then that subject’s data obtained in previous trials were classified into the risk group (y=1). After data processing, 13,581 and 6760 OGTTs were analyzed for study 1 and study 2, respectively. For each study, a randomly chosen subset representing 80% of the data was used for training 9 classification models and the remaining 20% was used for evaluating the models. Three classification models, A to C, used XGBoost with various input variables, some including OGTT data. The other 6 classification models, D to I, used LR for comparison. Results For study 1, the AUC values ranged from 0.78 to 0.93. For study 2, the AUC values ranged from 0.63 to 0.78. The machine learning approach using XGBoost showed better performance compared with traditional LR methods. The AUC values increased when the full OGTT variables were included. In our analysis using a particular setting of input variables, XGBoost showed that the OGTT variables were more important than fasting plasma glucose or glycated hemoglobin. Conclusions A machine learning approach, XGBoost, showed better prediction accuracy compared with LR, suggesting that advanced machine learning methods are useful for detecting the early signs of diabetes or GMD. The prediction accuracy increased when all OGTT variables were added. This indicates that complete OGTT information is important for predicting the future risk of diabetes and GMD accurately.
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Affiliation(s)
- Katsutoshi Maeta
- Faculty of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Yu Nishiyama
- Faculty of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Kazutoshi Fujibayashi
- Department of General Medicine, School of Medicine, Juntendo University, Tokyo, Japan
| | - Toshiaki Gunji
- Center for Preventive Medicine, NTT Medical Center Tokyo, Tokyo, Japan
| | - Noriko Sasabe
- Center for Preventive Medicine, NTT Medical Center Tokyo, Tokyo, Japan
| | - Kimiko Iijima
- Center for Preventive Medicine, NTT Medical Center Tokyo, Tokyo, Japan
| | - Toshio Naito
- Department of General Medicine, School of Medicine, Juntendo University, Tokyo, Japan
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50
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Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res 2018; 20:e10775. [PMID: 29848472 PMCID: PMC6000484 DOI: 10.2196/10775] [Citation(s) in RCA: 176] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 05/15/2018] [Accepted: 05/15/2018] [Indexed: 01/03/2023] Open
Abstract
Background Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. Methods A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. Results We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.
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Affiliation(s)
- Ivan Contreras
- Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain
| | - Josep Vehi
- Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain.,Centro de Investigación Biomédica en Red de Diabetes y Enfermadades Metabólicas Asociadas, Girona, Spain
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