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## Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders.

#### In Journal of the American Medical Informatics Association : JAMIA OBJECTIVES : The study sought to assess the clinical performance of a machine learning model aiming to identify unusual medication orders.MATERIALS AND METHODS : This prospective study was conducted at CHU Sainte-Justine, Canada, from April to August 2020. An unsupervised machine learning model based on GANomaly and 2 baselines were trained to learn medication order patterns from 10 years of data. Clinical pharmacists dichotomously (typical or atypical) labeled orders and pharmacological profiles (patients' medication lists). Confusion matrices, areas under the precision-recall curve (AUPRs), and F1 scores were calculated.RESULTS : A total of 12 471 medication orders and 1356 profiles were labeled by 25 pharmacists. Medication order predictions showed a precision of 35%, recall (sensitivity) of 26%, and specificity of 97% as compared with pharmacist labels, with an AUPR of 0.25 and an F1 score of 0.30. Profile predictions showed a precision of 49%, recall of 75%, and specificity of 82%, with an AUPR of 0.60, and an F1 score of 0.59. The model performed better than the baselines. According to the pharmacists, the model was a useful screening tool, and 9 of 15 participants preferred predictions by medication, rather than by profile.DISCUSSION : Predictions for profiles had higher F1 scores and recall compared with medication order predictions. Although the performance was much better for profile predictions, pharmacists generally preferred medication order predictions.CONCLUSIONS : Based on the AUPR, this model showed better performance for the identification of atypical pharmacological profiles than for medication orders. Pharmacists considered the model a useful screening tool. Improving these predictions should be prioritized in future research to maximize clinical impact.Hogue Sophie-Camille, Chen Flora, Brassard Geneviève, Lebel Denis, Bussières Jean-François, Durand Audrey, Thibault Maxime2021-May-06clinical, clinical pharmacy information systems, decision support systems, hospital pharmaceutical services, machine learning, medical order entry systems

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## Development and Validation of a Deep Learning Model Using Convolutional Neural Networks to Identify Scaphoid Fractures in Radiographs.

#### In JAMA network open Importance : Scaphoid fractures are the most common carpal fracture, but as many as 20% are not visible (ie, occult) in the initial injury radiograph; untreated scaphoid fractures can lead to degenerative wrist arthritis and debilitating pain, detrimentally affecting productivity and quality of life. Occult scaphoid fractures are among the primary causes of scaphoid nonunions, secondary to delayed diagnosis.Objective : To develop and validate a deep convolutional neural network (DCNN) that can reliably detect both apparent and occult scaphoid fractures from radiographic images.Design, Setting, and Participants : This diagnostic study used a radiographic data set compiled for all patients presenting to Chang Gung Memorial Hospital (Taipei, Taiwan) and Michigan Medicine (Ann Arbor) with possible scaphoid fractures between January 2001 and December 2019. This group was randomly split into training, validation, and test data sets. The images were passed through a detection model to crop around the scaphoid and were then used to train a DCNN model based on the EfficientNetB3 architecture to classify apparent and occult scaphoid fractures. Data analysis was conducted from January to October 2020.Exposures : A DCNN trained to discriminate radiographs with normal and fractured scaphoids.Main Outcomes and Measures : Area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Fracture localization was assessed using gradient-weighted class activation mapping.Results : Of the 11 838 included radiographs (4917 [41.5%] with scaphoid fracture; 6921 [58.5%] without scaphoid fracture), 8356 (70.6%) were used for training, 1177 (9.9%) for validation, and 2305 (19.5%) for testing. In the testing test, the first DCNN achieved an overall sensitivity and specificity of 87.1% (95% CI, 84.8%-89.2%) and 92.1% (95% CI, 90.6%-93.5%), respectively, with an AUROC of 0.955 in distinguishing scaphoid fractures from scaphoids without fracture. Gradient-weighted class activation mapping closely corresponded to visible fracture sites. The second DCNN achieved an overall sensitivity of 79.0% (95% CI, 70.6%-71.6%) and specificity of 71.6% (95% CI, 69.0%-74.1%) with an AUROC of 0.810 when examining negative cases from the first model. Two-stage examination identified 20 of 22 cases (90.9%) of occult fracture.Conclusions and Relevance : In this study, DCNN models were trained to identify scaphoid fractures. This suggests that such models may be able to assist with radiographic detection of occult scaphoid fractures that are not visible to human observers and to reliably detect fractures of other small bones.Yoon Alfred P, Lee Yi-Lun, Kane Robert L, Kuo Chang-Fu, Lin Chihung, Chung Kevin C2021-May-03

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## Twitter Surveillance at the Intersection of the Triangulum.

#### In Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco INTRODUCTION : A holistic public health surveillance approach can help capture the public's tobacco and marijuana-related attitudes and behaviors. Using publicly available data from Twitter, this is one of the first studies to describe key topics of discussions related to each intersection (e-cigarette, combustible tobacco, and marijuana) of the Triangulum framework.METHOD : Twitter posts (n=999,447) containing marijuana, e-cigarette and combustible tobacco terms were collected from January 1, 2018, to December 23, 2019. Posts to Twitter with co-occurring mentions of keywords associated with the Triangulum were defined as an intersection (e-cigarettes and combustible tobacco, combustible tobacco and marijuana, e-cigarettes and marijuana, and marijuana, e-cigarettes and combustible tobacco). Text classifiers and unsupervised machine learning was used to identify predominant topics in posts.RESULTS : Product Features and Cartridges were commonly referenced at the intersection of e-cigarette and marijuana-related conversations. Blunts and Cigars and Drugs and Alcohol were commonly referenced at the intersection of combustible tobacco and marijuana-related discussions. Flavors and Health Risks were discussed at the intersection of e-cigarette and combustible-related conversations, while discussions about Illicit products and Health risks were key topics of discussion when e-cigarettes, combustible tobacco, and marijuana were referenced all together in a single post.CONCLUSION : By examining intersections of marijuana and tobacco products, this study offers inputs for designing comprehensive FDA regulations including regulating product features associated with appeal, improving enforcement to curb sales of illicit products, and informing the FDA's product review and standards procedures for tobacco products that can be used with marijuana.IMPLICATIONS : This study is the first to leverage the Triangulum framework and Twitter data to describe key topics of discussions at the intersection of e-cigarette, combustible tobacco, and marijuana. Real-time health communication interventions can identify Twitter users posting in the context of e-cigarettes, combustible tobacco, and marijuana by automated methods and deliver tailored messages. This study also demonstrates the utility of Twitter data for surveillance of complex and evolving health behaviors.Majmundar Anuja, Allem Jon-Patrick, Cruz Tess Boley, Unger Jennifer B, Pentz Mary Ann2021-May-06

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## Polo: an open-source graphical user interface for crystallization screening.

#### In Journal of applied crystallography Polo is a Python-based graphical user interface designed to streamline viewing and analysis of images to monitor crystal growth, with a specific target to enable users of the High-Throughput Crystallization Screening Center at Hauptman-Woodward Medical Research Institute (HWI) to efficiently inspect their crystallization experiments. Polo aims to increase efficiency, reducing time spent manually reviewing crystallization images, and to improve the potential of identifying positive crystallization conditions. Polo provides a streamlined one-click graphical interface for the Machine Recognition of Crystallization Outcomes (MARCO) convolutional neural network for automated image classification, as well as powerful tools to view and score crystallization images, to compare crystallization conditions, and to facilitate collaborative review of crystallization screening results. Crystallization images need not have been captured at HWI to utilize Polo's basic functionality. Polo is free to use and modify for both academic and commercial use under the terms of the copyleft GNU General Public License v3.0.Holleman Ethan T, Duguid Erica, Keefe Lisa J, Bowman Sarah E J2021-Apr-01crystal imaging, crystallization, machine learning, open-source graphical user interfaces

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## Enhanced NH3 and H2 gas sensing with H2S gas interference using multilayer SnO2/Pt/WO3 nanofilms.

#### In Journal of hazardous materials The selective detection and classification of NH3 and H2S gases with H2S gas interference based on conventional SnO2 thin film sensors is still the main problem. In this work, three layers of SnO2/Pt/WO3 nanofilms with different WO3 thicknesses (50, 80, 140, and 260 nm) were fabricated using the sputtering technique. The WO3 top layer were used as a gas filter to further improve the selectivity of sensors. The effect of WO3 thickness on the (NH3, H2, and H2S) gas-sensing properties of the sensors was investigated. At the optimal WO3 thickness of 140 nm, the gas responses of SnO2/Pt/WO3 sensors toward NH3 and H2 gases were slightly lower than those of Pt/SnO2 sensor film, and the gas response of SnO2/Pt/WO3 sensor films to H2S gas was almost negligible. The calcification of NH3 and H2 gases was effectively conducted by machine learning algorithms. These evidences manifested that SnO2/Pt/WO3 sensor films are suitable for the actual NH3 detection of NH3 and H2S gases.Van Toan Nguyen, Hung Chu Manh, Hoa Nguyen Duc, Van Duy Nguyen, Thi Thanh Le Dang, Thi Thu Hoa Nguyen, Viet Nguyen Ngoc, Phuoc Phan Hong, Van Hieu Nguyen2021-Jun-15Gas filter membrane, H(2)S, NH(3), Pt/SnO(2) nanofilm, WO(3) layer

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