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Javed K, Qin J, Mowery W, Kadaba D, Altschul D, Haranhalli N. Predicting 90-day Functional Dependency and Death after Endovascular Thrombectomy for Stroke: The BET Score. J Stroke Cerebrovasc Dis 2022; 31:106342. [DOI: 10.1016/j.jstrokecerebrovasdis.2022.106342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/06/2022] [Accepted: 01/19/2022] [Indexed: 11/26/2022] Open
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Chintalapudi N, Angeloni U, Battineni G, di Canio M, Marotta C, Rezza G, Sagaro GG, Silenzi A, Amenta F. LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining. Bioengineering (Basel) 2022; 9:bioengineering9030124. [PMID: 35324813 PMCID: PMC8945331 DOI: 10.3390/bioengineering9030124] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/02/2022] [Accepted: 03/16/2022] [Indexed: 12/31/2022] Open
Abstract
Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers’ text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers’ Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.
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Affiliation(s)
- Nalini Chintalapudi
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (G.B.); (M.d.C.); (G.G.S.); (F.A.)
- Correspondence: ; Tel.: +39-35-33776704
| | - Ulrico Angeloni
- General Directorate of Health Prevention, Ministry of Health, 00144 Rome, Italy; (U.A.); (C.M.); (G.R.); (A.S.)
| | - Gopi Battineni
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (G.B.); (M.d.C.); (G.G.S.); (F.A.)
| | - Marzio di Canio
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (G.B.); (M.d.C.); (G.G.S.); (F.A.)
- Research Department, International Radio Medical Centre (C.I.R.M.), 00144 Rome, Italy
| | - Claudia Marotta
- General Directorate of Health Prevention, Ministry of Health, 00144 Rome, Italy; (U.A.); (C.M.); (G.R.); (A.S.)
| | - Giovanni Rezza
- General Directorate of Health Prevention, Ministry of Health, 00144 Rome, Italy; (U.A.); (C.M.); (G.R.); (A.S.)
| | - Getu Gamo Sagaro
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (G.B.); (M.d.C.); (G.G.S.); (F.A.)
| | - Andrea Silenzi
- General Directorate of Health Prevention, Ministry of Health, 00144 Rome, Italy; (U.A.); (C.M.); (G.R.); (A.S.)
| | - Francesco Amenta
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (G.B.); (M.d.C.); (G.G.S.); (F.A.)
- Research Department, International Radio Medical Centre (C.I.R.M.), 00144 Rome, Italy
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Martins SS, Bruzelius E, Stingone JA, Wheeler-Martin K, Akbarnejad H, Mauro CM, Marziali ME, Samples H, Crystal S, S. Davis C, Rudolph KE, Keyes KM, Hasin DS, Cerdá M. Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning. Epidemiology 2021; 32:868-876. [PMID: 34310445 PMCID: PMC8556655 DOI: 10.1097/ede.0000000000001404] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multistage, machine-learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research. METHODS Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time. RESULTS PDMP patient data access provisions most consistently predicted high-dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing. CONCLUSIONS Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing and to examine potential interactions between PDMP access and pain management clinic provisions. See video abstract at, http://links.lww.com/EDE/B861.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Stephen Crystal
- Rutgers University, Center for Health Services Research, Institute for Health, and School of Social Work
| | | | | | | | - Deborah S. Hasin
- Columbia University Department of Epidemiology
- Columbia University Department of Psychiatry
| | - Magdalena Cerdá
- NYU Grossman School of Medicine Department of Population Health
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Jiang Z, Zhang Y, Wang J. A multi-surrogate-assisted dual-layer ensemble feature selection algorithm. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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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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Tang ZQ, Zhao L, Chen GX, Chen CYC. Novel and versatile artificial intelligence algorithms for investigating possible GHSR1α and DRD1 agonists for Alzheimer's disease. RSC Adv 2021; 11:6423-6446. [PMID: 35423219 PMCID: PMC8694922 DOI: 10.1039/d0ra10077c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 01/18/2021] [Indexed: 11/21/2022] Open
Abstract
Hippocampal lesions are recognized as the earliest pathological changes in Alzheimer's disease (AD). Recent researches have shown that the co-activation of growth hormone secretagogue receptor 1α (GHSR1α) and dopamine receptor D1 (DRD1) could recover the function of hippocampal synaptic and cognition. We combined traditional virtual screening technology with artificial intelligence models to screen multi-target agonists for target proteins from TCM database and a novel boost Generalized Regression Neural Network (GRNN) model was proposed in this article to improve the poor adjustability of GRNN. R-square was chosen to evaluate the accuracy of these artificial intelligent models. For the GHSR1α agonist dataset, Adaptive Boosting (AdaBoost), Linear Ridge Regression (LRR), Support Vector Machine (SVM), and boost GRNN achieved good results; the R-square of the test set of these models reached 0.900, 0.813, 0.708, and 0.802, respectively. For the DRD1 agonist dataset, Gradient Boosting (GB), Random Forest (RF), SVM, and boost GRNN achieved good results; the R-square of the test set of these models reached 0.839, 0.781, 0.763, and 0.815, respectively. According to these values of R-square, it is obvious that boost GRNN and SVM have better adaptability for different data sets and boost GRNN is more accurate than SVM. To evaluate the reliability of screening results, molecular dynamics (MD) simulation experiments were performed to make sure that candidates were docked well in the protein binding site. By analyzing the results of these artificial intelligent models and MD experiments, we suggest that 2007_17103 and 2007_13380 are the possible dual-target drugs for Alzheimer's disease (AD).
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Affiliation(s)
- Zi-Qiang Tang
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen Guangzhou 510275 China
| | - Lu Zhao
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen Guangzhou 510275 China
- Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou 510655 China
| | - Guan-Xing Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen Guangzhou 510275 China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen Guangzhou 510275 China
- Department of Medical Research, China Medical University Hospital Taichung 40447 Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University Taichung 41354 Taiwan
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Korzeniewski SJ, Bezold C, Carbone JT, Danagoulian S, Foster B, Misra D, El-Masri MM, Zhu D, Welch R, Meloche L, Hill AB, Levy P. The Population Health OutcomEs aNd Information EXchange (PHOENIX) Program - A Transformative Approach to Reduce the Burden of Chronic Disease. Online J Public Health Inform 2020; 12:e3. [PMID: 32577152 PMCID: PMC7295585 DOI: 10.5210/ojphi.v12i1.10456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
This concept article introduces a transformative vision to reduce the population burden of chronic disease by focusing on data integration, analytics, implementation and community engagement. Known as PHOENIX (The Population Health OutcomEs aNd Information EXchange), the approach leverages a state level health information exchange and multiple other resources to facilitate the integration of clinical and social determinants of health data with a goal of achieving true population health monitoring and management. After reviewing historical context, we describe how multilevel and multimodal data can be used to facilitate core public health services, before discussing the controversies and challenges that lie ahead.
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Khanji C, Schnitzer ME, Bareil C, Perreault S, Lalonde L. Concordance of care processes between medical records and patient self-administered questionnaires. BMC FAMILY PRACTICE 2019; 20:92. [PMID: 31269902 PMCID: PMC6607524 DOI: 10.1186/s12875-019-0979-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 06/13/2019] [Indexed: 11/10/2022]
Abstract
Background Despite the increasing use of medical records to measure quality of care, studies have shown that their validity is suboptimal. The objective of this study is to assess the concordance of cardiovascular care processes evaluated through medical record review and patient self-administered questionnaires (SAQs) using ten quality indicators (TRANSIT indicators). These indicators were developed as part of a participatory research program (TRANSIT study) dedicated to TRANSforming InTerprofessional clinical practices to improve cardiovascular disease (CVD) prevention in primary care. Methods For every patient participating in the TRANSIT study, the compliance to each indicator (individual scores) as well as the mean compliance to all indicators of a category (subscale scores) and to the complete set of ten indicators (overall scale score) were established. Concordance between results obtained using medical records and patient SAQs was assessed by prevalence-adjusted bias-adjusted kappa (PABAK) coefficients as well as intraclass correlation coefficients (ICCs) and 95% confidence intervals (95% CI). Generalized linear mixed models (GLMM) were used to identify patients’ sociodemographic and clinical characteristics associated with agreement between the two data sources. Results The TRANSIT study was conducted in a primary care setting among patients (n = 759) with multimorbidity, at moderate (16%) and high risk (83%) of cardiovascular diseases. Quality of care, as measured by the TRANSIT indicators, varied substantially between medical records and patient SAQ. Concordance between the two data sources, as measured by ICCs (95% CI), was poor for the subscale (0.18 [0.08–0.27] to 0.46 [0.40–0.52]) and overall (0.46 [0.40–0.53]) compliance scale scores. GLMM showed that agreement was not affected by patients’ characteristics. Conclusions In quality improvement strategies, researchers must acknowledge that care processes may not be consistently recorded in medical records. They must also be aware that the evaluation of the quality of care may vary depending on the source of information, the clinician responsible of documenting the interventions, and the domain of care. Electronic supplementary material The online version of this article (10.1186/s12875-019-0979-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Cynthia Khanji
- Faculty of pharmacy, University of Montreal, 2940 Polytechnique Road, Montreal, Quebec, H3T1J4, Canada
| | - Mireille E Schnitzer
- Faculty of pharmacy, University of Montreal, 2940 Polytechnique Road, Montreal, Quebec, H3T1J4, Canada
| | - Céline Bareil
- HEC Montréal, University of Montreal, 3000 Côte-Sainte-Catherine Road, Montreal, Quebec, H3T2A7, Canada
| | - Sylvie Perreault
- Faculty of pharmacy, University of Montreal, 2940 Polytechnique Road, Montreal, Quebec, H3T1J4, Canada.,Sanofi Aventis Endowment Chair in Drug Utilization, Montreal, Canada
| | - Lyne Lalonde
- Faculty of pharmacy, University of Montreal, 2940 Polytechnique Road, Montreal, Quebec, H3T1J4, Canada. .,Sanofi Aventis Endowment Chair in Ambulatory Pharmaceutical Care, Montreal, Canada.
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Khaire UM, Dhanalakshmi R. Stability of feature selection algorithm: A review. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2019. [DOI: 10.1016/j.jksuci.2019.06.012] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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