scholarly journals Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19

2021 ◽  
Author(s):  
Iva Halilaj ◽  
Avishek Chatterjee ◽  
Yvonka van Wijk ◽  
Guangyao Wu ◽  
Brice van Eeckhout ◽  
...  
Keyword(s):  
Open Source ◽  
Predictive Models ◽  
Prediction Models ◽  
Patient Specific ◽  
Diagnosis And Prognosis ◽  
Online Calculator ◽  
User Friendly ◽  
Type 3 ◽  
Proof Of Principle

AbstractObjectiveThe current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice.MethodsIn this proof-of-principle study, we performed a literature search in PubMed and WHO database to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and published online together with supporting documentation and links to the associated articles.ResultsThe open source website https://covid19risk.ai/ currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work.ConclusionWe have successfully demonstrated in this proof-of-principle study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally-validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.

scholarly journals Covid19Risk.ai: An Open Source Repository and Online Calculator of Prediction Models for Early Diagnosis and Prognosis of Covid-19

BioMed ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 41-49
Author(s):  
Iva Halilaj ◽  
Avishek Chatterjee ◽  
Yvonka van Wijk ◽  
Guangyao Wu ◽  
Brice van Eeckhout ◽  
...  
Keyword(s):  
Pilot Study ◽  
Open Source ◽  
Predictive Models ◽  
Prediction Models ◽  
Patient Specific ◽  
Diagnosis And Prognosis ◽  
Online Calculator ◽  
Practice Methods ◽  
User Friendly ◽  
Type 3

Background: The current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice. Methods: In this pilot study, we performed a literature search in the PubMed and WHO databases to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and were published online together with supporting documentation and links to the associated articles. Results: The open source website currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work. Conclusion: We have successfully demonstrated in this pilot study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.

scholarly journals An Objective Search for Unrecognized Bias in Validated COVID-19 Prediction Models

2021 ◽  
Author(s):  
Hossein Estiri ◽  
Zachary Strasser ◽  
Sina Rashidian ◽  
Jeffrey Klann ◽  
Kavishwar Wagholikar ◽  
...  
Keyword(s):  
Predictive Models ◽  
Medical Records ◽  
Prediction Models ◽  
Evaluation Studies ◽  
Protected Group ◽  
Increased Risk ◽  
Using Data ◽  
Bias Evaluation ◽  
Algorithmic Bias

The growing recognition of algorithmic bias has spurred discussions about fairness in artificial intelligence (AI) / machine learning (ML) algorithms. The increasing translation of predictive models into clinical practice brings an increased risk of direct harm from algorithmic bias; however, bias remains incompletely measured in many medical AI applications. Using data from over 56 thousand Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in four AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. We discuss that while a model can be biased against certain protected groups (i.e., perform worse) in certain tasks, it can be at the same time biased towards another protected group (i.e., perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. If the goal is to make a change in a positive way, the underlying roots of bias need to be fully explored in medical AI. Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

scholarly journals Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

BMJ ◽  
2020 ◽  
pp. m958 ◽  
Author(s):  
Elham Mahmoudi ◽  
Neil Kamdar ◽  
Noa Kim ◽  
Gabriella Gonzales ◽  
Karandeep Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Language Processing ◽  
Predictive Models ◽  
Hospital Readmission ◽  
Prediction Models ◽  
Cox Regression ◽  
Patient Specific ◽  
Day Hospital ◽  
Significant Difference

Abstract Objective To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission. Design Systematic review. Data source Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019. Eligibility criteria for selecting studies All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data. Outcome measures Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models. Results Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77). Conclusions On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

MackDroid - An Android based Application to monitor devices

2016 ◽  
Vol 9 ◽  
pp. 1
Author(s):  
Maaz Sirkhot ◽  
Ekta Sirwani ◽  
Aishwarya Kourani ◽  
Akshit Batheja ◽  
Kajal Jethanand Jewani
Keyword(s):  
Operating System ◽  
Open Source ◽  
Vital Role ◽  
Mobile Users ◽  
Android Application ◽  
Web Page ◽  
Technological World ◽  
Monitoring Service ◽  
User Friendly

In this technological world, smartphones can be considered as one of the most far-reaching inventions. It plays a vital role in connecting people socially. The number of mobile users using an Android based smartphone has increased rapidly since last few years resulting in organizations, cyber cell departments, government authorities feeling the need to monitor the activities on certain targeted devices in order to maintain proper functionality of their respective jobs. Also with the advent of smartphones, Android became one of the most popular and widely used Operating System. Its highlighting features are that it is user friendly, smartly designed, flexible, highly customizable and supports latest technologies like IoT. One of the features that makes it exclusive is that it is based on Linux and is Open Source for all the developers. This is the reason why our project Mackdroid is an Android based application that collects data from the remote device, stores it and displays on a PHP based web page. It is primarily a monitoring service that analyzes the contents and distributes it in various categories like Call Logs, Chats, Key logs, etc. Our project aims at developing an Android application that can be used to track, monitor, store and grab data from the device and store it on a server which can be accessed by the handler of the application.

scholarly journals Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware

2021 ◽  
Author(s):  
Jonathan Shapey ◽  
Thomas Dowrick ◽  
Rémi Delaunay ◽  
Eleanor C. Mackle ◽  
Stephen Thompson ◽  
...  
Keyword(s):  
Skull Base ◽  
Open Source ◽  
Open Source Software ◽  
Skull Base Surgery ◽  
Patient Specific ◽  
Software Platform ◽  
Image Guided ◽  
Phantom Model ◽  
Technical Requirements ◽  
Navigated Ultrasound

Abstract Purpose Image-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery. Methods We established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system’s ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model. Results Experimental validation of the system’s navigated ultrasound component demonstrated accuracy of $$<4.5\,\hbox {mm}$$ < 4.5 mm and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of $$<15\,\hbox {min}$$ < 15 min and performed with a clinically acceptable level of accuracy. Conclusion We present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements.

scholarly journals A closed-loop optogenetic screen for neurons controlling feeding in Drosophila

2021 ◽  
Author(s):  
Celia K S Lau ◽  
Meghan Jelen ◽  
Michael D Gordon
Keyword(s):  
Drosophila Melanogaster ◽  
Expression Pattern ◽  
Sensory Neurons ◽  
Closed Loop ◽  
Higher Order ◽  
Taste Detection ◽  
Proof Of Principle ◽  
Feeding Circuits

Abstract Feeding is an essential part of animal life that is greatly impacted by the sense of taste. Although the characterization of taste-detection at the periphery has been extensive, higher order taste and feeding circuits are still being elucidated. Here, we use an automated closed-loop optogenetic activation screen to detect novel taste and feeding neurons in Drosophila melanogaster. Out of 122 Janelia FlyLight Project GAL4 lines preselected based on expression pattern, we identify six lines that acutely promote feeding and 35 lines that inhibit it. As proof of principle, we follow up on R70C07-GAL4, which labels neurons that strongly inhibit feeding. Using split-GAL4 lines to isolate subsets of the R70C07-GAL4 population, we find both appetitive and aversive neurons. Furthermore, we show that R70C07-GAL4 labels putative second-order taste interneurons that contact both sweet and bitter sensory neurons. These results serve as a resource for further functional dissection of fly feeding circuits.

scholarly journals Risk Factors for Failure in Conservatively Treated Osteoporotic Vertebral Fractures: A Systematic Review

2021 ◽  
pp. 219256822098227
Author(s):  
Max J. Scheyerer ◽  
Ulrich J. A. Spiegl ◽  
Sebastian Grueninger ◽  
Frank Hartmann ◽  
Sebastian Katscher ◽  
...  
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Treatment Failure ◽  
Patient Specific ◽  
Mineral Density ◽  
Radiographic Findings ◽  
Specific Risk ◽  
Fracture Development ◽  
Predict Treatment Failure

Study Design: Systematic review. Objectives: Osteoporosis is one of the most common diseases of the elderly, whereby vertebral body fractures are in many cases the first manifestation. Even today, the consequences for patients are underestimated. Therefore, early identification of therapy failures is essential. In this context, the aim of the present systematic review was to evaluate the current literature with respect to clinical and radiographic findings that might predict treatment failure. Methods: We conducted a comprehensive, systematic review of the literature according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) checklist and algorithm. Results: After the literature search, 724 potentially eligible investigations were identified. In total, 24 studies with 3044 participants and a mean follow-up of 11 months (range 6-27.5 months) were included. Patient-specific risk factors were age >73 years, bone mineral density with a t-score <−2.95, BMI >23 and a modified frailty index >2.5. The following radiological and fracture-specific risk factors could be identified: involvement of the posterior wall, initial height loss, midportion type fracture, development of an intravertebral cleft, fracture at the thoracolumbar junction, fracture involvement of both endplates, different morphological types of fractures, and specific MRI findings. Further, a correlation between sagittal spinal imbalance and treatment failure could be demonstrated. Conclusion: In conclusion, this systematic review identified various factors that predict treatment failure in conservatively treated osteoporotic fractures. In these cases, additional treatment options and surgical treatment strategies should be considered in addition to follow-up examinations.

scholarly journals Brain Tumor Resection in Elderly Patients: Potential Factors of Postoperative Worsening in a Predictive Outcome Model

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2320
Author(s):  
Paolo Ferroli ◽  
Ignazio Gaspare Vetrano ◽  
Silvia Schiavolin ◽  
Francesco Acerbi ◽  
Costanza Maria Zattra ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Postoperative Complications ◽  
Elderly Patients ◽  
Performance Status ◽  
Tumor Resection ◽  
Patient Specific ◽  
Invasive Treatment ◽  
Tertiary Referral Center ◽  
Surgical Complexity

The decision of whether to operate on elderly patients with brain tumors is complex, and influenced by pathology-related and patient-specific factors. This retrospective cohort study, based on a prospectively collected surgical database, aims at identifying possible factors predicting clinical worsening after elective neuro-oncological surgery in elderly patients. Therefore, all patients ≥65 years old who underwent BT resection at a tertiary referral center between 01/2018 and 12/2019 were included. Age, smoking, previous radiotherapy, hypertension, preoperative functional status, complications occurrence, surgical complexity and the presence of comorbidities were prospectively collected and analyzed at discharge and the 3-month follow-up. The series included 143 patients (mean 71 years, range 65–86). Sixty-five patients (46%) had at least one neurosurgical complication, whereas 48/65 (74%) complications did not require invasive treatment. Forty-two patients (29.4%) worsened at discharge; these patients had a greater number of complications compared to patients with unchanged/improved performance status. A persistent worsening at three months of follow-up was noted in 20.3% of patients; again, this subgroup presented more complications than patients who remained equal or improved. Therefore, postoperative complications and surgical complexity seem to influence significantly the early outcome in elderly patients undergoing brain tumor surgery. In contrast, postoperative complications alone are the only factor with an impact on the 3-month follow-up.

scholarly journals How to Use Online Tools to Generate New Hypotheses for Mammary Gland Biology Research: A Case Study for Wnt7b

2021 ◽  
Author(s):  
Yorick Bernardus Cornelis van de Grift ◽  
Nika Heijmans ◽  
Renée van Amerongen
Keyword(s):  
Mammary Gland ◽  
In Silico ◽  
Online Tools ◽  
Mammary Gland Biology ◽  
User Friendly ◽  
In Silico Analyses ◽  
Biology Research ◽  
Selection Of

AbstractAn increasing number of ‘-omics’ datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Fortunately, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach. Using regulation of Wnt7b expression as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific ‘-omics’ datasets and thereby expand the in silico toolbox.

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