Symptom-based data preprocessing for the detection of disease outbreak

Duangchaemkarn, Khanita; Chaovatut, Varin; Wiwatanadate, Phongtape; Boonchieng, Ekkarat

doi:10.1109/embc.2017.8037393

Cited by 5 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The status of the individual patient at any time can be normal (0), infected (1), or suspicious (−1), which comes from alarm management. The user’s geographical location can be geographical coordinates of longitude and latitude [ 98 ], postal code address [ 99 ], or any local reference coordinates. Estimation of user location can be carried out using GPS information from the user’s mobile phone, which can be accessed during each data registration.…”

Section: Discussionmentioning

confidence: 99%

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

Woldaregay¹,

Launonen²,

Årsand³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

Background Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. Objective The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. Methods We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). Results Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. Conclusions We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

show abstract

Section: Discussionmentioning

confidence: 99%

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

Woldaregay¹,

Launonen²,

Årsand³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Based on the results shown in Table 9 , for time-series data, ARIMA and LSTM are the most common machine learning algorithms used to perform the prediction [23] , [25] , [28] , [31] , [32] , [33] , [38] , [39] . On the other hand, the family of ANN and k NN algorithms are widely used in solving the classification tasks [44] , [46] , [48] , [49] , [51] , [55] , [59] .…”

Section: Contents Summarizationmentioning

confidence: 99%

“…Khanita showed another approach that applies symptoms and location based method to detect disease epidemics using a symptom-based approach. k -NN clustering is also helpful to identify a new potential epidemic cluster [59] . SVC with RBF kernel and GridSearch algorithm were used in predicting the outbreak of cardiovascular diseases in Italy and America with accuracy of 95.25% based on 29 features that include Framingham risk factors, Uremic risk factors and inflammatory biomarkers [45] .…”

Section: Reporting Of Review Findingsmentioning

confidence: 99%

“…Khanita showed that k -NN based classification method can be applied to detect disease epidemics using a symptom-based and location-based approach [59] . Vijayakumar et al also have introduced a Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases [49] using a Fuzzy k-Nearest Neighbour (FKNN) classifier with 95.9% classification accuracy.…”

Section: Reporting Of Review Findingsmentioning

confidence: 99%

“…k -Nearest Neighbour ( k -NN) was found to be very effective when applied for infection detection using physiological and social data in social environments [48] , detection and prevention of mosquito-borne diseases [49] and detection of disease epidemics using a symptom-based approach [59] . In short, Neural Network and k -Nearest Neighbor methods were found to be popular and very effective in detecting and predicting disease outbreaks [48] .…”

Section: Reporting Of Review Findingsmentioning

confidence: 99%

See 2 more Smart Citations

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Alfred¹,

Obit²

2021

Heliyon

View full text Add to dashboard Cite

Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning . The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.

show abstract

Text Mining and Machine Learning Algorithms to Identifying Diseases and Providing Repair Action Using ICD-10 Codes

Ramdasi

Sathyalakshmi

2022

ICT Systems and Sustainability

View full text Add to dashboard Cite

Symptom-based data preprocessing for the detection of disease outbreak

Cited by 5 publications

References 10 publications

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review

Text Mining and Machine Learning Algorithms to Identifying Diseases and Providing Repair Action Using ICD-10 Codes

Contact Info

Product

Resources

About