An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study.

BACKGROUND: A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs. OBJECTIVE: We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities. METHODS: Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model. RESULTS: In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the "NWO Navigate Stroke" system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes. CONCLUSIONS: The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.

Social media-based COVID-19 sentiment classification model using Bi-LSTM.

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples' concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.

A deep learning segmentation-classification pipeline for X-ray-based COVID-19 diagnosis.

Over the past year, the AI community has constructed several deep learning models for diagnosing COVID-19 based on the visual features of chest X-rays. While deep learning researchers have commonly focused much of their attention on designing deep learning classifiers, only a fraction of these same researchers have dedicated effort to including a segmentation module in their system. This is unfortunate since other applications in radiology typically require segmentation as a necessary prerequisite step in building truly deployable clinical models. Differentiating COVID-19 from other pulmonary diseases can be challenging as various lung diseases share common visual features with COVID-19. To help clarify the diagnosis of suspected COVID-19 patients, we have designed our deep learning pipeline with a segmentation module and ensemble classifier. Following a detailed description of our deep learning pipeline, we present the strengths and shortcomings of our approach and compare our model with other similarly constructed models. While doing so, we focus our attention on widely circulated public datasets and describe several fallacies we have noticed in the literature concerning them. After performing a thorough comparative analysis, we demonstrate that our best model can successfully obtain an accuracy of 91 percent and sensitivity of 92 percent.

Automated clinical pathway standardization using SNOMED CT- based semantic relatedness.

The increasing number of patients and heavy workload drive health care institutions to search for efficient and cost-effective methods to deliver optimal care. Clinical pathways are promising care plans that proved to be efficient in reducing costs and optimizing resource usage. However, most clinical pathways are circulated in paper-based formats. Clinical pathway computerization is an emerging research field that aims to integrate clinical pathways with health information systems. A key process in clinical pathway computerization is the standardization of clinical pathway terminology to comply with digital terminology systems. Since clinical pathways include sensitive medical terms, clinical pathway standardization is performed manually and is difficult to automate using machines. The objective of this research is to introduce automation to clinical pathway standardization. The proposed approach utilizes a semantic score-based algorithm that automates the search for SNOMED CT terms. The algorithm was implemented in a software system with a graphical user interface component that physicians can use to standardize clinical pathways by searching for and comparing relevant SNOMED CT retrieved automatically by the algorithm. The system has been tested and validated on SNOMED CT ontology. The experimental results show that the system reached a maximum search space reduction of 98.9% within any single iteration of the algorithm and an overall average of 71.3%. The system enables physicians to locate the proper terms precisely, quickly, and more efficiently. This is demonstrated using case studies, and the results show that human-guided automation is a promising methodology in the field of clinical pathway standardization and computerization.

COV-SNET: A deep learning model for X-ray-based COVID-19 classification.

The AI research community has recently been intensely focused on diagnosing COVID-19 by applying deep learning technology to the X-ray scans taken of COVID-19 patients. Differentiating COVID-19 from other pneumonia-inducing illnesses is a highly challenging task as it shares many of the same imaging characteristics as other pulmonary diseases. This is especially true given the small number of COVID-19 X-rays that are publicly available. Deep learning experts commonly use transfer learning to offset the small number of images typically available in medical imaging tasks. Our COV-SNET model is a deep neural network that was pretrained on over one hundred thousand X-ray images. In this paper, we designed two COV-SNET models with the purpose of diagnosing COVID-19. The experimental results demonstrate the robustness of our deep learning models, ultimately achieving sensitivities of 95% for our three-class and two-class models. We also discuss the strengths and weaknesses of such an approach, focusing mainly on the limitations of public X-ray datasets on current COVID-19 deep learning models. Finally, we conclude with possible future directions for this research.

Ontological framework for standardizing and digitizing clinical pathways in healthcare information systems.

BACKGROUND AND OBJECTIVE: Most healthcare institutions are reorganizing their healthcare delivery systems based on Clinical Pathways (CPs). CPs are novel medical management plans to standardize medical activities, reduce cost, optimize resource usage, and improve the quality of service. However, most CPs are still paper-based and not fully integrated with Health Information Systems (HIS). More CP computerization research is therefore needed to fully benefit from CP's practical potentials. A major contribution of this research is the vision that CP systems deserve to be placed at the centre of HIS, because within CPs lies the very heart of medical planning, treatment and impressions, including healthcare quality and cost factors. METHODS: An important contribution to the realization of this vision is to fully standardize and digitize CPs so that they become machine-readable and smoothly linkable across various HIS. To achieve this goal, this research proposes a framework for (i) CP knowledge representation and sharing using ontologies, (ii) CP standardization based on SNOMED CT and HL7, and (iii) CP digitization based on a novel coding system to encode CP data. To show the feasibility of the proposed framework we developed a prototype clinical pathway management system (CPMS) based on CPs currently in use at hospitals. RESULTS: The results show that CPs can be fully standardized and digitized using SNOMED CT terms and codes, and the CPMS can work as an independent system, performing novel CP-related functions, including useful data analytics. CPs can be compared easily for auditing and quality management. Furthermore, the CPMS was smoothly linked to a hospital EMR and CP data were captured in EMR without any loss. CONCLUSION: The proposed framework is promising and contributes toward solving major challenges related to CP standardization, digitization, and inclusion in today's modern computerized hospitals.

Emerging trends for open access learning.

This special issue is dedicated to recent opportunities, trends, and expectations that the emergent number of institutions and governments, exploiting the open learning concept, face in designing and providing open education that is striving to shape the new environment for formal and informal education. The open learning concept embraces not only various definitions but also diverse directions conveying many opportunities for educational arrangements. Facing the need for a sustainable economy, and higher employability, governments progressively experience the pressure toward ensuring a qualified and retaining competitive workforce. There is a high demand for education settings where learners are not able to formally attend courses but experience the need to enhance their knowledge and skills. The open learning concept reflects not only educational but also business and societal issues, as well as visions and expectations. We address with this special issue innovative solutions and emerging trends in the area of open learning that comprise complex multidisciplinary fields of knowledge drawing a line between the needs of various learners in terms of accrediting current and desired level of skills and knowledge as well as of numerous institutions striving to provide education on a broad and competitive basis.

Developing a semantic web model for medical differential diagnosis recommendation.

In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.