Web Information Extraction for Finding Remedy Based on a Patient-Authored Text: A Study on Homeopathy

Priyadarshi, Ankur; Saha, Sujan Kumar

doi:10.1007/s13721-019-0216-2

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Health organizations require accurate and timely disease surveillance techniques to respond to various emerging issues. Therefore, there has been increasing attention on the use of social media and the Internet as important resources for general disease surveillance (Priyadarshi and Saha 2020). This, in turn, implies a new generation of surveillance strategies that were developed to assist the automatic detection process of the emerging infections (Zadeh et al 2019) that could be effectively analyzed via machine learning algorithms, due to their capability in handling big data and extracting the latent information (Richter and Khoshgoftaar 2020).…”

Section: Introductionmentioning

confidence: 99%

A real-time biosurveillance mechanism for early-stage disease detection from microblogs: a case study of interconnection between emotional and climatic factors related to migraine disease

Sarsam

Al-Samarraie

Ismail

et al. 2020

Netw Model Anal Health Inform Bioinforma

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For many years, certain climatic factors have been used to predict potential disease outcomes of relevance to humans. This is because early discovery of disease (or its symptoms) would help people or healthcare professionals to take the necessary precautions. Since microblogs can be used to create new connections and maintain existing relationships, disease detection in microblogs is still considered a serious problem for many healthcare systems, especially for establishing a successful epidemic recognition procedure. To tackle this issue, this study proposed a novel tracking approach to diagnose illnesses in microblogs. It is based on the interconnection between certain emotional type and climatic factors associated with a specific disease (e.g., migraine). In this study, detailed migraine data were collected from Twitter. We used K-means and Apriori algorithms to extract migraine-related emotions and investigate the potential associations between migraine symptoms and climatic factors. The results showed that sad emotions were highly interrelated with migraine symptoms. The classification results showed that Sequential Minimal Optimization (SMO) was efficient (95.53% accuracy) in detecting the migraine symptoms from Twitter. The proposed mechanism can be used efficiently in biosurveillance systems due to its capability in identifying the hidden symptoms of a sickness on microblogs. This study paves the way to discover disease-related features using both emotional and climatic factors.

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Section: Introductionmentioning

confidence: 99%

A real-time biosurveillance mechanism for early-stage disease detection from microblogs: a case study of interconnection between emotional and climatic factors related to migraine disease

Sarsam

Al-Samarraie

Ismail

et al. 2020

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

show abstract

“…Classification algorithms have been extensively used by many scholars to process social media content and predict a specific outcome (Choubey, Kumar, Tripathi, & Kumar, 2020). In a health context, there has been an increasing attention on the use of social media and the Internet for general disease surveillance (Priyadarshi & Saha, 2020). For instance, a study by Sarsam, Al-Samarraie, Ismail, Zaqout, and Wright (2020b) proposed a real-time bio surveillance mechanism in order to predict the early-stage of migraine disease.…”

Section: Introductionmentioning

confidence: 99%

Disease discovery-based emotion lexicon: a heuristic approach to characterise sicknesses in microblogs

Sarsam

Al-Samarraie

Al-Sa’di

2020

Netw Model Anal Health Inform Bioinforma

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The analysis of microblogging data has been widely used to discover valuable resources for timely identification of critical illness-related incidents and serious epidemics. Despite the numerous efforts made in this field, making an accurate and timely prediction of incidents and outbreaks based on certain clinical symptoms remains a great challenge. Hence, providing an investigative method can be crucial in characterising a disease state. This study proposes a heuristic mechanism by using an unsupervised learning technique to efficiently detect disease incidents and outbreaks from the tweet content. We categorised the types of emotions that are highly linked to a specific disease and its related terminologies. Emotions (anger, fear, sadness, and joy) and diabetes-related terminologies were extracted using the NRC Affect Intensity Lexicon and a part-of-speech tagging tool. A two-cluster solution was established and validated.The classification result showed that K-means clustering with 2 centroids had the highest classification accuracy (96.53%). The relationship between diabetes-related terms (in the form of tweets) and emotions were established and assessed using the association rules mining technique.The results showed that diabetes-related terms were exclusively associated with fear emotions.This study offers a novel mechanism for disease recognition and outbreak detection in microblogs which is useful in making informed decisions about a disease state.

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Effect of hospital architecture, computer games, and nurses’ behavior on the effectiveness of the treatment process of adolescent cancer patients

Zheng

Sedeh

2020

Netw Model Anal Health Inform Bioinforma

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Web Information Extraction for Finding Remedy Based on a Patient-Authored Text: A Study on Homeopathy

Cited by 5 publications

References 35 publications

A real-time biosurveillance mechanism for early-stage disease detection from microblogs: a case study of interconnection between emotional and climatic factors related to migraine disease

A real-time biosurveillance mechanism for early-stage disease detection from microblogs: a case study of interconnection between emotional and climatic factors related to migraine disease

Disease discovery-based emotion lexicon: a heuristic approach to characterise sicknesses in microblogs

Effect of hospital architecture, computer games, and nurses’ behavior on the effectiveness of the treatment process of adolescent cancer patients

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