Thai COVID-19 patient clustering for monitoring and prevention: data mining techniques

This research aims to optimize emerging infectious disease monitoring techniques in Thailand, which will be extremely valuable to the government, doctors, police, and others involved in understanding the seriousness of the spread of novel coronavirus to improve government policies, decisions, medical facilities, treatment. The data mining techniques included cluster analysis using K-means clustering. The infection data were obtained from the open data of the digital government development agency, Thailand. The dataset consisted of 1,893,941 cumulative cases from January 2020 to October 2021 of the outbreak. The results from clustering consisted of 8 groups. Clustering results determined the three largest, three medium-sized, and the two most minor numbers of infected people, respectively. These clusters represent their activities, namely touching an infected person and checking themselves. The components of emerging diseases in Thailand are closely related to waves, gender, age, nationality, career, behavioral risk, and region. The province of onset was mainly in Bangkok and its vicinity or central Thailand, as well as industrial areas. Adult workers aged 19 to 27 years and 43 to 54 years or over were seeds of new infection sources

2022 SDSU Data Science Symposium Program

https://openprairie.sdstate.edu/ds_symposium_programs/1003/thumbnail.jp

Finding patterns in cardiologic diseases using a data-driven approach

Globally, cardiovascular disease (CD) is the leading cause of death. Several guidelines for the treatment of CD have been published with the aim of improving the quality of care and reducing costs. Thus, it is increasingly important to detect and diagnose cardiovascular diseases early. This study aims to build an algorithm to predict whether a patient will exceed their heart rate. In addition, the goal was to build an alert system that monitors the patient's clinical status and, whenever there is a change, according to some parameters, the doctor receives a message automatically. This study was based on a set of data from Santa Maria Hospital in Lisbon, obtained through Digital Services Agreements developed under the FCT project DSAIPA/AI/0122/2020 AIMHealth - Artificial Intelligence Based Mobile Applications for Public Health Response. The data-centric method followed the CRISP-DM Data Mining (DM) methodology. Based on the dataset it was possible, following this methodology, to develop a Machine Learning (ML) algorithm that could predict in advance whether the patient would exceed the interquartile range of their heart rate. We found that our ML algorithm was able to predict cardiac problems in 90% of the cases and that our alert system was effective in early detection of cardiac problems in patients. This study has shown that using ML is a valuable tool for detecting the worsening of a patient's health condition.A nível mundial, as doenças cardiovasculares (DC) são a principal causa de morte. Foram publicadas várias diretrizes para o tratamento das DC com o objetivo de melhorar a qualidade dos cuidados e reduzir os custos. Assim, é cada vez mais importante detetar e diagnosticar precocemente as doenças cardiovasculares. Este estudo tem como objetivo construir um algoritmo que permita prever se o doente vai ultrapassar a sua frequência cardíaca. Para além disso, o objetivo foi construir um sistema de alerta que monitoriza o estado clínico do doente e, sempre que houver uma alteração, de acordo com alguns parâmetros, o médico recebe uma mensagem automaticamente. Este estudo teve como base um conjunto de dados do Hospital Santa Maria em Lisboa, obtidos através de Acordos de Prestação de Serviços Digitais desenvolvidos no âmbito do projeto FCT DSAIPA/AI/0122/2020 AIMHealth - Aplicações Móveis Baseadas em Inteligência Artificial para Resposta de Saúde Pública. O método centrado nos dados seguiu a metodologia de Mineração de Dados (MD) CRISP-DM. Com base no conjunto de dados foi possível, seguindo esta metodologia, desenvolver um algoritmo de Aprendizagem Automática (AA) que pudesse prever antecipadamente se o doente iria exceder o intervalo interquartil da sua frequência cardíaca. Verificámos que o nosso algoritmo de AA conseguiu prever problemas cardíacos em 90% dos casos e que o nosso sistema de alerta foi eficaz na deteção precoce de problemas cardíacos nos doentes. Este estudo demonstrou que a utilização de AA é uma ferramenta valiosa para detetar o agravamento do estado de saúde de um doente

Machine learning in handling disease outbreaks: a comprehensive review

The changes in the global environment have made impact on the evolution of infectious diseases, virus mutations, or new diseases which are challenging to be tackled with new technological advances. This work aims to identify and analyze previous studies on machine learning applications in handling disease outbreaks. Bibliometric analysis was conducted on 3,447 scientific articles selected from the Scopus database. Further, latent dirichlet analysis (LDA) method was applied to identify the topic hotspots in attempting to deepen the analysis. The LDA results identified twelve topic hotspots that can be classified into three themes: COVID-19 disease, miscellaneous diseases, and public opinion on disease outbreaks for discussion. The study reveals that the scientific structure of this domain is dominated by machine learning research on COVID-19 diseases and miscellaneous diseases caused by pathogens or some genetic factors. A huge amount of multimodal medical data was used by previous studies for prediction, forecasting, classification, or screening purposes to resolve many problems of diseases, including epidemiological surveillance, diagnosis, treatment, health monitoring, epidemic management, viral infection, and pathogenesis. Public opinions toward new diseases are also an interesting topic in addition to the public perceptions in response to the health protocol and policies

DeepCOVID: a deep learning approach for accurate COVID-19 detection in point-of-care lung ultrasound

Sickness still continued to spread through several countries when it first appeared in China. The number of COVID-19 cases is rising daily worldwide, posing a severe threat to the government and the populace. As a result of the virus’s rapid spread, doctors are having trouble recognizing positive cases. It is obvious that computer-based diagnosis must be developed to get results at a reasonable cost. The classic convolutional neural network (CNN) is used for this, utilizing the CT dataset, and the upgraded CNN model is used with the lung ultrasound (LUS) dataset. The CT and LUS COVID imaging datasets are compared in the model. The accuracy of both deep learning models is higher. We customized ResNet50, a pre-trained deep learning architecture, for a web application paradigm. First, we suggest a method for normalizing data that addresses its variability because it is collected in hospitals using various CT scanners and ultrasound machines. Second, we identify COVID-19 patients using U-Net segmentation and classification. The CNN architecture is added for deep learning purposes, and Res-Net 50 offers incredible accuracy

Developing Smart Hospital Management Systems with IoT and Big Data

As integration of IoT and big data analytics in hospital management, healthcare has been revolutionized by the improvement in patient monitoring, resources optimization, and making of predictive decisions. This research proposes a new conceptualized Smart Hospital Management System (SHMS) based on Artificial Intelligence deployed algorithms such as Random Forest, K-Means clustering, Long Short Term Memory (LSTM), and Genetic Algorithm from the perspective of analyzing real time healthcare data to enhance hospital work flows. To test the system, it was applied to 500,000 patient records and real time IoT sensor data. It was shown that Random Forest has 94.3% accuracy in ICU admission prediction, K-Means clustering maximized hospital bed utilization by 87%, LSTM improved patient deterioration forecasting by 92.1%, and the Genetic Algorithm reduced emergency response time by 35%. The proposed AI powered model of the hospital management system is found to reduce cost of operations as well as efficiency in contrast to the traditional hospital management system. The superiority of the proposed approach to real time decision making and automation of the hospital is then compared with already existing methods. This research lays a foundation for the development of scalable, AI driven smart healthcare infrastructures while challenges of data privacy and interoperability remain. In future work we will seek to increase security, model scalability, and deployment for real world application to make hospitals more efficient and streamline patient care

Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning

Cardiovascular diseases (CVD) have recently outdid all other reasons of death universal in both developing and developed nations. Initial detection of cardiac conditions and continuing therapeutic supervision by experts can lower the death rate. However, accurate diagnosis of cardiac issues in all circumstances and 24-hour patient consultation by a doctor are still not feasible due to the increased intellect, effort, and expertise required. In this study, a basic concept for an Machine Learning (ML)-based heart disease prediction system was presented to identify impending heart disease using Machine Learning techniques. Despite the increasing number of empirical studies in this topic, particularly from underdeveloped countries, here lack many synthesised research articles in the field. In a time when the amount of data available is constantly increasing, predictive analytics has become more and more important as a tool for heart welfare services and human protection.  By utilising data collected from previous events to predict future patterns and outcomes, this state-of-the-art technology assists heart-care agencies in making more informed decisions about how to best serve their clients. However, as with any other data-driven technology, predictive analytics must be used appropriately to guarantee effective and ethical business operations. Healthcare forecasting has gained importance in recent years due to the growing popularity of AI (Artificial Intelligence) and ML (Machine Learning). In the healthcare sector, forecasting can also aid physicians in providing more precise and timely diagnoses. By anticipating likely medical events, medical staff can identify and treat individuals with greater efficiency and precision. This could lead to better patient outcomes and even cost savings.  These systems provide excellent therapeutic support and have the ability to diagnose illnesses by mimicking human cognition.  This study's included studies focus on forecasting the heart healthcare system (HHS) using machine learning algorithms. We implemented the system using the K-means Elbow technique for registration and notification, a decision tree for HHS, and MySQL for immunisation reminders

Big Data Analytics Challenges and Opportunities in Heart Disease Recognition: Novel Dimensionality Reduction with Classification Approach

Due to the current technological growth, a number of strategies have been developed, and more are being developed to eliminate problems that arise in many fields. Big Data techniques are employed to effectively stored health data due to the continual and massive volume of data created by the human body. Furthermore, the most important procedure is the classification of health data since it must be carried out precisely in order to diagnose cardiac disease early. The database images are various in size to reduce the dimension the Modified Principal Component Analysis (MPCA) Algorithm is used. the proposed MPCA algorithm is act as a feature selection model to pick features. One of the best and most effective techniques for classifying medical data is the Modified Deep Convolutional Neural Network (MDCNN). It has been shown to work for a variety of hospitalized patients. Consequently, the simulation results show that this proposal enhances classification accuracy in experimental research for the detection of heart ailment.  Hence, the proposed method leads to an efficient usage of the resources and cost reduction. This approach assists the physician in taking suitable decision for giving a better treatment at right moment

Data modeling COVID-19 patients in Thailand: data mining techniques

This study aimed to investigate the characteristics of COVID-19 patients in Thailand and develop a data model for analyzing these characteristics. A total of 1,888,941 cases from the Thailand Department of Disease Control website from January 12, 2020, to October 29, 2021, were analyzed, and 20,110 cases were selected for further analysis. The two-step cluster analysis method was used to cluster the data according to four variables: nationality, occupation, patient type, and risk groups. The results showed the presence of three groups of COVID-19 patients. Group 1 consisted of 5,883 workers in trade and service occupations who had contact with the public and were either Thai nationals or from abroad. Group 2 was the largest cluster, consisting of 7,420 migrant workers classified as foreigners and working in industrial settings. Group 3 consisted of 6,870 cases of indirect transmission, with individuals in this group infected through close contact with family members or individuals in the first two groups. This clustering approach offers valuable insights for pandemic management, aiding in identifying high-risk groups and developing tailored interventions. In future outbreaks with similar characteristics, such as airborne transmission, contact infection, or super spreader events, our model can serve as a valuable tool for devising effective management plans and countermeasures. In conclusion, this study emphasizes the significance of cluster analysis in understanding the dynamics of COVID-19 transmission and highlights its potential for informing effective pandemic management strategies. It also outlines promising avenues for further research to enhance our knowledge of COVID-19's impact on specific populations and inform future public health efforts

Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter

COVID-19 has ruptured routines and caused breakdowns in what had been conventional practice and custom: everything from going to work and school and shopping in the supermarket to socializing with friends and taking holidays. Nonetheless, COVID-19 does provide an opportunity to study how people make sense of radically changing circumstances over time. In this paper we demonstrate how Twitter affords this opportunity by providing data in real time, and over time. In the present research, we collect a large pool of COVID-19 related tweets posted by New Zealanders-citizens of a country successful in containing the coronavirus-from the moment COVID-19 became evident to the world in the last days of 2019 until 19 August 2020. We undertake topic modeling on the tweets to foster understanding and sensemaking of the COVID-19 tweet landscape in New Zealand and its temporal development and evolution over time. This information can be valuable for those interested in how people react to emergent events, including researchers, governments, and policy makers.fals