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Liu K, Li L, Ma Y, Jiang J, Liu Z, Ye Z, Liu S, Pu C, Chen C, Wan Y. Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis. JMIR Med Inform 2023; 11:e47833. [PMID: 37983072 PMCID: PMC10696506 DOI: 10.2196/47833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/21/2023] [Accepted: 10/12/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. OBJECTIVE In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. METHODS PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. RESULTS In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. CONCLUSIONS Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. TRIAL REGISTRATION PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.
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Affiliation(s)
- Kui Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Linyi Li
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yifei Ma
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Jun Jiang
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Zhenhua Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Zichen Ye
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Shuang Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Chen Pu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Changsheng Chen
- Department of Health Statistics, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yi Wan
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
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Mermin-Bunnell K, Zhu Y, Hornback A, Damhorst G, Walker T, Robichaux C, Mathew L, Jaquemet N, Peters K, Johnson TM, Wang MD, Anderson B. Use of Natural Language Processing of Patient-Initiated Electronic Health Record Messages to Identify Patients With COVID-19 Infection. JAMA Netw Open 2023; 6:e2322299. [PMID: 37418261 PMCID: PMC10329205 DOI: 10.1001/jamanetworkopen.2023.22299] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 05/19/2023] [Indexed: 07/08/2023] Open
Abstract
Importance Natural language processing (NLP) has the potential to enable faster treatment access by reducing clinician response time and improving electronic health record (EHR) efficiency. Objective To develop an NLP model that can accurately classify patient-initiated EHR messages and triage COVID-19 cases to reduce clinician response time and improve access to antiviral treatment. Design, Setting, and Participants This retrospective cohort study assessed development of a novel NLP framework to classify patient-initiated EHR messages and subsequently evaluate the model's accuracy. Included patients sent messages via the EHR patient portal from 5 Atlanta, Georgia, hospitals between March 30 and September 1, 2022. Assessment of the model's accuracy consisted of manual review of message contents to confirm the classification label by a team of physicians, nurses, and medical students, followed by retrospective propensity score-matched clinical outcomes analysis. Exposure Prescription of antiviral treatment for COVID-19. Main Outcomes and Measures The 2 primary outcomes were (1) physician-validated evaluation of the NLP model's message classification accuracy and (2) analysis of the model's potential clinical effect via increased patient access to treatment. The model classified messages into COVID-19-other (pertaining to COVID-19 but not reporting a positive test), COVID-19-positive (reporting a positive at-home COVID-19 test result), and non-COVID-19 (not pertaining to COVID-19). Results Among 10 172 patients whose messages were included in analyses, the mean (SD) age was 58 (17) years; 6509 patients (64.0%) were women and 3663 (36.0%) were men. In terms of race and ethnicity, 2544 patients (25.0%) were African American or Black, 20 (0.2%) were American Indian or Alaska Native, 1508 (14.8%) were Asian, 28 (0.3%) were Native Hawaiian or other Pacific Islander, 5980 (58.8%) were White, 91 (0.9%) were more than 1 race or ethnicity, and 1 (0.01%) chose not to answer. The NLP model had high accuracy and sensitivity, with a macro F1 score of 94% and sensitivity of 85% for COVID-19-other, 96% for COVID-19-positive, and 100% for non-COVID-19 messages. Among the 3048 patient-generated messages reporting positive SARS-CoV-2 test results, 2982 (97.8%) were not documented in structured EHR data. Mean (SD) message response time for COVID-19-positive patients who received treatment (364.10 [784.47] minutes) was faster than for those who did not (490.38 [1132.14] minutes; P = .03). Likelihood of antiviral prescription was inversely correlated with message response time (odds ratio, 0.99 [95% CI, 0.98-1.00]; P = .003). Conclusions and Relevance In this cohort study of 2982 COVID-19-positive patients, a novel NLP model classified patient-initiated EHR messages reporting positive COVID-19 test results with high sensitivity. Furthermore, when responses to patient messages occurred faster, patients were more likely to receive antiviral medical prescription within the 5-day treatment window. Although additional analysis on the effect on clinical outcomes is needed, these findings represent a possible use case for integration of NLP algorithms into clinical care.
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Affiliation(s)
- Kellen Mermin-Bunnell
- Currently a medical student at Emory University School of Medicine, Atlanta, Georgia
| | - Yuanda Zhu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta
| | - Andrew Hornback
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta
| | - Gregory Damhorst
- Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia
| | - Tiffany Walker
- Division of General Internal Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Chad Robichaux
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia
| | - Lejy Mathew
- Division of General Internal Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Nour Jaquemet
- Currently a medical student at Emory University School of Medicine, Atlanta, Georgia
| | | | - Theodore M. Johnson
- Division of General Internal Medicine, Emory University School of Medicine, Atlanta, Georgia
- Atlanta Veterans Affairs Healthcare System, Decatur, Georgia
| | - May Dongmei Wang
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
| | - Blake Anderson
- Division of General Internal Medicine, Emory University School of Medicine, Atlanta, Georgia
- Atlanta Veterans Affairs Healthcare System, Decatur, Georgia
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Prediction of Prednisolone Dose Correction Using Machine Learning. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:84-103. [PMID: 36910914 PMCID: PMC9995628 DOI: 10.1007/s41666-023-00128-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/20/2022] [Accepted: 02/03/2023] [Indexed: 02/17/2023]
Abstract
Wrong dose, a common prescription error, can cause serious patient harm, especially in the case of high-risk drugs like oral corticosteroids. This study aims to build a machine learning model to predict dose-related prescription modifications for oral prednisolone tablets (i.e., highly imbalanced data with very few positive cases). Prescription data were obtained from the electronic medical records at a single institute. Cluster analysis classified the clinical departments into six clusters with similar patterns of prednisolone prescription. Two patterns of training datasets were created with/without preprocessing by the SMOTE method. Five ML models (SVM, KNN, GB, RF, and BRF) and logistic regression (LR) models were constructed by Python. The model was internally validated by five-fold stratified cross-validation and was validated with a 30% holdout test dataset. Eighty-two thousand five hundred fifty-three prescribing data for prednisolone tablets containing 135 dose-corrected positive cases were obtained. In the original dataset (without SMOTE), only the BRF model showed a good performance (in test dataset, ROC-AUC:0.917, recall: 0.951). In the training dataset preprocessed by SMOTE, performance was improved on all models. The highest performance models with SMOTE were SVM (in test dataset, ROC-AUC: 0.820, recall: 0.659) and BRF (ROC-AUC: 0.814, recall: 0.634). Although the prescribing data for dose-related collection are highly imbalanced, various techniques such as the following have allowed us to build high-performance prediction models: data preprocessing by SMOTE, stratified cross-validation, and BRF classifier corresponding to imbalanced data. ML is useful in complicated dose audits such as oral prednisolone. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00128-3.
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Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, Junaid T, Bostic T. The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature. Pharmaceut Med 2022; 36:295-306. [PMID: 35904529 DOI: 10.1007/s40290-022-00441-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. OBJECTIVE The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. METHODS Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. RESULTS Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. CONCLUSION Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.
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Affiliation(s)
- Maribel Salas
- Daiichi Sankyo, Inc. & Center for Real-World Effectiveness and Safety of Therapeutics (CREST), University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 211 Mount Airy Rd, Basking Ridge, NJ, USA
| | - Jan Petracek
- Institute of Pharmacovigilance, Hvezdova 2b, 14000, Prague, Czech Republic
| | - Priyanka Yalamanchili
- Daiichi Sankyo, Inc. & Rutgers University, 211 Mount Airy Rd, Basking Ridge, NJ, USA.
| | | | | | - Sameer Dhingra
- Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, India
| | | | - Tina Bostic
- PPD, part of Thermo Fisher Scientific, Wilmington, NC, USA
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Chen T, Zhang Y, Dou Q, Zheng X, Wang F, Zou J, Jia R. Machine learning-assisted preoperative diagnosis of infection stones in urolithiasis patients. J Endourol 2022; 36:1091-1098. [PMID: 35369740 DOI: 10.1089/end.2021.0783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Tingting Chen
- China Pharmaceutical University, 56651, School of Basic medical and Clinical pharmacy, Nanjing, Jiangsu, China
| | | | | | | | | | - Jianjun Zou
- Nanjing First Hospital, 385685, Clinical pharmarcy department, Nanjing, Nangjing, China, 210029
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Zhang Y, Razbek J, Li D, Yang L, Bao L, Xia W, Mao H, Daken M, Zhang X, Cao M. Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models. BMC Public Health 2022; 22:251. [PMID: 35135534 PMCID: PMC8822755 DOI: 10.1186/s12889-022-12617-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 01/17/2022] [Indexed: 12/03/2022] Open
Abstract
Background We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS. Methods This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model. Results Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise. Conclusions Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.
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Affiliation(s)
- Yan Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Jaina Razbek
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Deyang Li
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lei Yang
- Xinjiang De Kang Ci Hui Health Services Group, Urumqi, Xinjiang, China
| | - Liangliang Bao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wenjun Xia
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Hongkai Mao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mayisha Daken
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiaoxu Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mingqin Cao
- Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China.
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Davoudi A, Lee NS, Luong T, Delaney T, Asch E, Chaiyachati K, Mowery D. Identifying Medication-related Intents from a Bidirectional Text Messaging Platform for Hypertension Management: A Pilot Study using a Unsupervised Learning Approach (Preprint). J Med Internet Res 2022; 24:e36151. [PMID: 35767327 PMCID: PMC9280462 DOI: 10.2196/36151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/01/2022] [Accepted: 05/17/2022] [Indexed: 12/02/2022] Open
Abstract
Background Free-text communication between patients and providers plays an increasing role in chronic disease management, through platforms varying from traditional health care portals to novel mobile messaging apps. These text data are rich resources for clinical purposes, but their sheer volume render them difficult to manage. Even automated approaches, such as natural language processing, require labor-intensive manual classification for developing training data sets. Automated approaches to organizing free-text data are necessary to facilitate use of free-text communication for clinical care. Objective The aim of this study was to apply unsupervised learning approaches to (1) understand the types of topics discussed and (2) learn medication-related intents from messages sent between patients and providers through a bidirectional text messaging system for managing participant blood pressure (BP). Methods This study was a secondary analysis of deidentified messages from a remote, mobile, text-based employee hypertension management program at an academic institution. We trained a latent Dirichlet allocation (LDA) model for each message type (ie, inbound patient messages and outbound provider messages) and identified the distribution of major topics and significant topics (probability >.20) across message types. Next, we annotated all medication-related messages with a single medication intent. Then, we trained a second medication-specific LDA (medLDA) model to assess how well the unsupervised method could identify more fine-grained medication intents. We encoded each medication message with n-grams (n=1-3 words) using spaCy, clinical named entities using Stanza, and medication categories using MedEx; we then applied chi-square feature selection to learn the most informative features associated with each medication intent. Results In total, 253 participants and 5 providers engaged in the program, generating 12,131 total messages: 46.90% (n=5689) patient messages and 53.10% (n=6442) provider messages. Most patient messages corresponded to BP reporting, BP encouragement, and appointment scheduling; most provider messages corresponded to BP reporting, medication adherence, and confirmatory statements. Most patient and provider messages contained 1 topic and few contained more than 3 topics identified using LDA. In total, 534 medication messages were annotated with a single medication intent. Of these, 282 (52.8%) were patient medication messages: most referred to the medication request intent (n=134, 47.5%). Most of the 252 (47.2%) provider medication messages referred to the medication question intent (n=173, 68.7%). Although the medLDA model could identify a majority intent within each topic, it could not distinguish medication intents with low prevalence within patient or provider messages. Richer feature engineering identified informative lexical-semantic patterns associated with each medication intent class. Conclusions LDA can be an effective method for generating subgroups of messages with similar term usage and facilitating the review of topics to inform annotations. However, few training cases and shared vocabulary between intents precludes the use of LDA for fully automated, deep, medication intent classification. International Registered Report Identifier (IRRID) RR2-10.1101/2021.12.23.21268061
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Affiliation(s)
- Anahita Davoudi
- Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Natalie S Lee
- Division of General Internal Medicine, Department of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - ThaiBinh Luong
- Penn Medicine Predictive Healthcare, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Timothy Delaney
- Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, United States
| | - Elizabeth Asch
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
| | - Krisda Chaiyachati
- Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, United States
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Danielle Mowery
- Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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Zheng Y, Dickson VV, Blecker S, Ng JM, Rice BC, Melkus GD, Shenkar L, Mortejo MCR, Johnson SB. Identifying Patients with Hypoglycemia Using Natural Language Processing: A Systematic Literature Review (Preprint). JMIR Diabetes 2021; 7:e34681. [PMID: 35576579 PMCID: PMC9152713 DOI: 10.2196/34681] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 04/03/2022] [Accepted: 04/08/2022] [Indexed: 01/22/2023] Open
Abstract
Background Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. Objective The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. Methods Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. Results This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. Conclusions The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing.
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Affiliation(s)
- Yaguang Zheng
- Rory Meyers College of Nursing, New York University, New York, NY, United States
| | | | - Saul Blecker
- Department of Population Health, Grossman School of Medicine, New York University, New York, NY, United States
| | - Jason M Ng
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Gail D'Eramo Melkus
- Rory Meyers College of Nursing, New York University, New York, NY, United States
| | - Liat Shenkar
- Lehigh Valley Health Network, Lehigh Valley Reilly Children's Hospital, Allentown, PA, United States
| | | | - Stephen B Johnson
- Department of Population Health, Grossman School of Medicine, New York University, New York, NY, United States
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Pilla SJ, Park J, Schwartz JL, Albert MC, Ephraim PL, Boulware LE, Mathioudakis NN, Maruthur NM, Beach MC, Greer RC. Hypoglycemia Communication in Primary Care Visits for Patients with Diabetes. J Gen Intern Med 2021; 36:1533-1542. [PMID: 33479925 PMCID: PMC8175615 DOI: 10.1007/s11606-020-06385-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Hypoglycemia is a common and serious adverse effect of diabetes treatment, especially for patients using insulin or insulin secretagogues. Guidelines recommend that these patients be assessed for interval hypoglycemic events at each clinical encounter and be provided anticipatory guidance for hypoglycemia prevention. OBJECTIVE To determine the frequency and content of hypoglycemia communication in primary care visits. DESIGN Qualitative study PARTICIPANTS: We examined 83 primary care visits from one urban health practice representing 8 clinicians and 33 patients using insulin or insulin secretagogues. APPROACH Using a directed content analysis approach, we analyzed audio-recorded primary care visits collected as part of the Achieving Blood Pressure Control Together study, a randomized trial of behavioral interventions for hypertension. The coding framework included communication about interval hypoglycemia, defined as discussion of hypoglycemic events or symptoms; the components of hypoglycemia anticipatory guidance in diabetes guidelines; and hypoglycemia unawareness. Hypoglycemia documentation in visit notes was compared to visit transcripts. KEY RESULTS Communication about interval hypoglycemia occurred in 24% of visits, and hypoglycemic events were reported in 16%. Despite patients voicing fear of hypoglycemia, clinicians rarely assessed hypoglycemia frequency, severity, or its impact on quality of life. Hypoglycemia anticipatory guidance was provided in 21% of visits which focused on diet and behavior change; clinicians rarely counseled on hypoglycemia treatment or avoidance of driving. Limited discussions of hypoglycemia unawareness occurred in 8% of visits. Documentation in visit notes had low sensitivity but high specificity for ascertaining interval hypoglycemia communication or hypoglycemic events, compared to visit transcripts. CONCLUSIONS In this high hypoglycemia risk population, communication about interval hypoglycemia and counseling for hypoglycemia prevention occurred in a minority of visits. There is a need to support clinicians to more regularly assess their patients' hypoglycemia burden and enhance counseling practices in order to optimize hypoglycemia prevention in primary care.
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Affiliation(s)
- Scott J Pilla
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, MD, USA.
| | - Jenny Park
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jessica L Schwartz
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael C Albert
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins Community Physicians, Johns Hopkins University, Baltimore, MD, USA
| | - Patti L Ephraim
- Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, MD, USA
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - L Ebony Boulware
- Division of General Internal Medicine, Duke University, Durham, NC, USA
| | - Nestoras N Mathioudakis
- Department of Medicine, Division of Endocrinology, Diabetes, & Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nisa M Maruthur
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, MD, USA
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mary Catherine Beach
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, MD, USA
- Department of Health, Behavior & Society, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Raquel C Greer
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, MD, USA
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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10
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Sung SF, Hung LC, Hu YH. Developing a stroke alert trigger for clinical decision support at emergency triage using machine learning. Int J Med Inform 2021; 152:104505. [PMID: 34030088 DOI: 10.1016/j.ijmedinf.2021.104505] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/01/2021] [Accepted: 05/17/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Acute stroke is an urgent medical condition that requires immediate assessment and treatment. Prompt identification of patients with suspected stroke at emergency department (ED) triage followed by timely activation of code stroke systems is the key to successful management of stroke. While false negative detection of stroke may prevent patients from receiving optimal treatment, excessive false positive alarms will substantially burden stroke neurologists. This study aimed to develop a stroke-alert trigger to identify patients with suspected stroke at ED triage. METHODS Patients who arrived at the ED within 12 h of symptom onset and were suspected of a stroke or transient ischemic attack or triaged with a stroke-related symptom were included. Clinical features at ED triage were collected, including the presenting complaint, triage level, self-reported medical history (hypertension, diabetes, hyperlipidemia, heart disease, and prior stroke), vital signs, and presence of atrial fibrillation. Three rule-based algorithms, ie, Face Arm Speech Test (FAST) and two flavors of Balance, Eyes, FAST (BE-FAST), and six machine learning (ML) techniques with various resampling methods were used to build classifiers for identification of patients with suspected stroke. Logistic regression (LR) was used to find important features. RESULTS The study population consisted of 1361 patients. The values of area under the precision-recall curve (AUPRC) were 0.737, 0.710, and 0.562 for the FAST, BE-FAST-1, and BE-FAST-2 models, respectively. The values of AUPRC for the top three ML models were 0.787 for classification and regression tree with undersampling, 0.783 for LR with synthetic minority oversampling technique (SMOTE), and 0.782 for LR with class weighting. Among the ML models, logistic regression and random forest models in general achieved higher values of AUPRC, in particular in those with class weighting or SMOTE to handle class imbalance problem. In addition to the presenting complaint and triage level, age, diastolic blood pressure, body temperature, and pulse rate, were also important features for developing a stroke-alert trigger. CONCLUSIONS ML techniques significantly improved the performance of prediction models for identification of patients with suspected stroke. Such ML models can be embedded in the electronic triage system for clinical decision support at ED triage.
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Affiliation(s)
- Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan; Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan; Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan
| | - Ling-Chien Hung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Ya-Han Hu
- Department of Information Management, National Central University, Taoyuan City, Taiwan.
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11
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Turchin A, Florez Builes LF. Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review. J Diabetes Sci Technol 2021; 15:553-560. [PMID: 33736486 PMCID: PMC8120048 DOI: 10.1177/19322968211000831] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data. METHODS We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included. RESULTS We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F1 score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care. CONCLUSION Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.
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Affiliation(s)
- Alexander Turchin
- Brigham and Women’s Hospital, Boston,
MA, USA
- Alexander Turchin, MD, MS, Brigham and
Women’s Hospital, 221 Longwood Avenue, Boston, MA 02115, USA.
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12
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Jenie RP, Nurdin NM, Husein I, Alatas H. Sensitivity and Specificity of Non-Invasive Blood Glucose Level Measurement Optical Device to Detect Hypoglycaemia. J Nutr Sci Vitaminol (Tokyo) 2021; 66:S226-S229. [PMID: 33612600 DOI: 10.3177/jnsv.66.s226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Hypoglycemia is related to lethargy, psychiatric disorders, and impaired brain metabolism. Hypoglycemia is one of the leading factors of death in blood glucose level (BGL) metabolism disorders. Optical methods have been heavily researched due to its potential to eliminate drawbacks of conventional hypoglycemia detection; however, clinical data are still scarce. This study objective was to measure the sensitivity and specificity of non-invasive BGL Measurement Optical Device (NI-BGL-MOD) to detect hypoglycemia. The reference standard is venipuncture spectrophotometry. Researcher has developed NI-BGL-MOD, which we have used in a clinical trial in December 2015. The researchers have used spectral data collected from the device to measure the BGL of randomly selected 110 participants who were older than 17 y old. Each participant was measured five times. There are a total of 550 data sets that were then compared to BGL measurement using the reference standard. The spectral data were optimized using Discrete Fourier Transform and inferred to BGL prediction using the Fast Artificial Neural Network. Researchers have defined hypoglycemia case with BGL level at 75 mg/dL or lower. The researchers have calculated sensitivity and specificity using epiR in Rstudio. Respondents' BGL values were between 67 to 96 mg/dL. Researchers have classified eighty-nine cases as hypoglycemia. There are 461 cases classified as not hypoglycemia. The sensitivity was 54%, and the specificity was 97%. Diagnostic accuracy was 86%, and the number to diagnose was 1.96. The newly developed method NI-BGL-MOD could be used to detect hypoglycemia.
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Affiliation(s)
- Renan Prasta Jenie
- Physics Department, Mathematics and Natural Sciences Faculty, IPB University.,Community Nutrition Department, Human Ecology Faculty, IPB University.,Public Health Department, Public Health Faculty, Binawan University
| | | | - Irzaman Husein
- Physics Department, Mathematics and Natural Sciences Faculty, IPB University
| | - Husin Alatas
- Physics Department, Mathematics and Natural Sciences Faculty, IPB University
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13
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Kodama S, Fujihara K, Shiozaki H, Horikawa C, Yamada MH, Sato T, Yaguchi Y, Yamamoto M, Kitazawa M, Iwanaga M, Matsubayashi Y, Sone H. Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis. JMIR Diabetes 2021; 6:e22458. [PMID: 33512324 PMCID: PMC7880810 DOI: 10.2196/22458] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/09/2020] [Accepted: 12/07/2020] [Indexed: 12/12/2022] Open
Abstract
Background Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. Objective The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). Methods Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. Results A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. Conclusions Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682
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Affiliation(s)
- Satoru Kodama
- Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kazuya Fujihara
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Haruka Shiozaki
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Chika Horikawa
- Department of Health and Nutrition, Faculty of Human Life Studies, University of Niigata Prefecture, Niigata, Japan
| | - Mayuko Harada Yamada
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takaaki Sato
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yuta Yaguchi
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masahiko Yamamoto
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masaru Kitazawa
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Midori Iwanaga
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yasuhiro Matsubayashi
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hirohito Sone
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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14
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Mujahid O, Contreras I, Vehi J. Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges. SENSORS (BASEL, SWITZERLAND) 2021; 21:E546. [PMID: 33466659 PMCID: PMC7828835 DOI: 10.3390/s21020546] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/08/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022]
Abstract
(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.
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Affiliation(s)
- Omer Mujahid
- Model Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain; (O.M.); (I.C.)
| | - Ivan Contreras
- Model Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain; (O.M.); (I.C.)
| | - Josep Vehi
- Model Identification and Control Laboratory, Institut d’Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain; (O.M.); (I.C.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 17003 Girona, Spain
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15
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Ferrario A, Demiray B, Yordanova K, Luo M, Martin M. Social Reminiscence in Older Adults' Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning. J Med Internet Res 2020; 22:e19133. [PMID: 32866108 PMCID: PMC7525396 DOI: 10.2196/19133] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/27/2020] [Accepted: 08/11/2020] [Indexed: 01/23/2023] Open
Abstract
Background Reminiscence is the act of thinking or talking about personal experiences that occurred in the past. It is a central task of old age that is essential for healthy aging, and it serves multiple functions, such as decision-making and introspection, transmitting life lessons, and bonding with others. The study of social reminiscence behavior in everyday life can be used to generate data and detect reminiscence from general conversations. Objective The aims of this original paper are to (1) preprocess coded transcripts of conversations in German of older adults with natural language processing (NLP), and (2) implement and evaluate learning strategies using different NLP features and machine learning algorithms to detect reminiscence in a corpus of transcripts. Methods The methods in this study comprise (1) collecting and coding of transcripts of older adults’ conversations in German, (2) preprocessing transcripts to generate NLP features (bag-of-words models, part-of-speech tags, pretrained German word embeddings), and (3) training machine learning models to detect reminiscence using random forests, support vector machines, and adaptive and extreme gradient boosting algorithms. The data set comprises 2214 transcripts, including 109 transcripts with reminiscence. Due to class imbalance in the data, we introduced three learning strategies: (1) class-weighted learning, (2) a meta-classifier consisting of a voting ensemble, and (3) data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm. For each learning strategy, we performed cross-validation on a random sample of the training data set of transcripts. We computed the area under the curve (AUC), the average precision (AP), precision, recall, as well as F1 score and specificity measures on the test data, for all combinations of NLP features, algorithms, and learning strategies. Results Class-weighted support vector machines on bag-of-words features outperformed all other classifiers (AUC=0.91, AP=0.56, precision=0.5, recall=0.45, F1=0.48, specificity=0.98), followed by support vector machines on SMOTE-augmented data and word embeddings features (AUC=0.89, AP=0.54, precision=0.35, recall=0.59, F1=0.44, specificity=0.94). For the meta-classifier strategy, adaptive and extreme gradient boosting algorithms trained on word embeddings and bag-of-words outperformed all other classifiers and NLP features; however, the performance of the meta-classifier learning strategy was lower compared to other strategies, with highly imbalanced precision-recall trade-offs. Conclusions This study provides evidence of the applicability of NLP and machine learning pipelines for the automated detection of reminiscence in older adults’ everyday conversations in German. The methods and findings of this study could be relevant for designing unobtrusive computer systems for the real-time detection of social reminiscence in the everyday life of older adults and classifying their functions. With further improvements, these systems could be deployed in health interventions aimed at improving older adults’ well-being by promoting self-reflection and suggesting coping strategies to be used in the case of dysfunctional reminiscence cases, which can undermine physical and mental health.
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Affiliation(s)
- Andrea Ferrario
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Burcu Demiray
- Department of Psychology, University of Zurich, Zurich, Switzerland.,University Research Priority Program, University of Zurich, Zurich, Switzerland.,Collegium Helveticum, Zurich, Switzerland
| | - Kristina Yordanova
- Institute of Computer Science, University of Rostock, Rostock, Germany.,Institute of Visual & Analytic Computing, University of Rostock, Rostock, Germany.,Interdisciplinary Faculty Ageing of Individuals and Society, University of Rostock, Rostock, Germany
| | - Minxia Luo
- Department of Psychology, University of Zurich, Zurich, Switzerland.,University Research Priority Program, University of Zurich, Zurich, Switzerland
| | - Mike Martin
- Department of Psychology, University of Zurich, Zurich, Switzerland.,University Research Priority Program, University of Zurich, Zurich, Switzerland.,Collegium Helveticum, Zurich, Switzerland.,School of Psychology, Faculty of Health and Behavioural Sciences, University of Queensland, Brisbane, Australia
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16
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A Machine Learning Approach to Predicting Readmission or Mortality in Patients Hospitalized for Stroke or Transient Ischemic Attack. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186337] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Readmissions after stroke are not only associated with greater levels of disability and a higher risk of mortality but also increase overall medical costs. Predicting readmission risk and understanding its causes are thus essential for healthcare resource allocation and quality improvement planning. By using machine learning techniques on initial admission data, this study aimed to develop prediction models for readmission or mortality after stroke. During model development, resampling methods were implemented to balance the class distribution. Two-layer nested cross-validation was used to build and evaluate the prediction models. A total of 3422 patients were included for analysis. The 90-day rate of readmission or mortality was 17.6%. This study identified several important predictive factors, including age, prior emergency department visits, pre-stroke functional status, stroke severity, body mass index, consciousness level, and use of a nasogastric tube. The Naïve Bayes model with class weighting to compensate for class imbalance achieved the highest discriminatory capacity in terms of the area under the receiver operating characteristic curve (0.661). Despite having room for improvement, the prediction models could be used for early risk assessment of patients with stroke. Identification of patients at high risk for readmission or mortality immediately after admission has the potential of enabling early discharge planning and transitional care interventions.
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17
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López Seguí F, Ander Egg Aguilar R, de Maeztu G, García-Altés A, García Cuyàs F, Walsh S, Sagarra Castro M, Vidal-Alaball J. Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E1093. [PMID: 32050435 PMCID: PMC7036927 DOI: 10.3390/ijerph17031093] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 01/30/2020] [Accepted: 02/07/2020] [Indexed: 11/16/2022]
Abstract
Background: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: Twenty machine learning algorithms (based on five types of algorithms and four text representation techniques) were trained using a sample of 3559 messages (169,102 words) corresponding to 2268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: The best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query" (precision = 0.48). Conclusion: To the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.
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Affiliation(s)
- Francesc López Seguí
- TIC Salut Social—Ministry of Health, 08028 Barcelona, Spain;
- CRES&CEXS—Pompeu Fabra University, 08003 Barcelona, Spain
| | | | | | - Anna García-Altés
- Agency for Healthcare Quality and Evaluation of Catalonia (AQuAS), Catalan Ministry of Health, 08005 Barcelona, Spain;
| | | | - Sandra Walsh
- Institut de Biologia Evolutiva (UPF-CSIC), Pompeu Fabra University, 08003 Barcelona, Spain;
| | - Marta Sagarra Castro
- Centre d’Atenció Primària Capellades, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, 08786 Sant Fruitós de Bages, Spain;
| | - Josep Vidal-Alaball
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, 08272 Sant Fruitós de Bages, Spain
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina, 08272 Sant Fruitós de Bages, Spain
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