Coronary artery disease risk assessment from unstructured electronic health records using text mining

Jonnagaddala, Jitendra; Liaw, Siaw‐Teng; Ray, Pradeep; Kumar, Manish; Chang, Nen Chung; Dai, Hong-Jie

doi:10.1016/j.jbi.2015.08.003

Cited by 69 publications

(51 citation statements)

References 35 publications

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“…Text mining has been applied in several fields such as health [13], telecommunications and marketing [14]. While, in Indonesian language text mining has also been implemented in some cases, such as sentiment analysis on social media [15], health services [16] and security [17].…”

Section: Text Miningmentioning

confidence: 99%

Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging

Costa

Tjandrasa

Djanali

2018

IJECE

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To overcome pests and diseases of rice farming, farmers always rely on information and knowledge from agricultural experts for decision making. The problem is that experts are not always available when the farmers need and the cost is quite high. Pests and diseases elimination is hard to be done individually since the farmers are lack of knowledge about the pest types that attack the rice fields. The objective of this study is to build a knowledgebased system that can identify pests and diseases interactively based on the information that has been told by the farmers using SMS communication services. The system can provide a convenience way to the farmers in delivering pests and disease problem information using a natural language. The text mining method performs tokenizing, filtering and porter stemming that used to extract important information sent by a SMS service. The method of Jaccard Similarity Coefficient (JSC) was used to calculate similarities of each pest and disease based on symptoms that are sent by the farmers through SMS. The corpus database usedin this study consists of 28.526 root words, 1.309 stop wordsand 180 words list. Pest and disease database reference in this study was obtained from the Ministry of Agriculture and Fisher (MAF) Timor-Leste. The result of the experiment shows that the system is able to identify the symptoms based on the keywords identified with the accuracy of 81%. The result of pest and disease identification has the accuracy of 86%. Keyword:Interactive text messaging Knowledge-based system Rice pests and diseases Text mining Timor Leste

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Section: Text Miningmentioning

confidence: 99%

Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging

Costa

Tjandrasa

Djanali

2018

IJECE

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“…Jonnagaddala, et al [7] developed a system for determining and extract coronary artery disease (CAD) risk factors from unstructured electronic health records. Using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients.…”

Section: Template Based Summarizationmentioning

confidence: 99%

Template based Medical Reports Summarization

El-Qumsan¹,

El-Halees²

2018

IJCA

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The torrential information in the medical records is considered a great problem because it difficult to distinguish the needed and necessary information from the huge quantity of data. As a result, the importance of summarize medical reports is growing day after day. Medical information extraction is one of the important topics that aim to identify medical information and detect hidden relations. This topic is considered one of the most important topics in the field of text mining where is used to process unstructured texts and extract meaningful information which is hidden in the unstructured texts.The information extracted from medical reports is very useful to medical staff to detect hidden relations between medical information, and making decisions that will improve the medical service for patients, in addition to saving time and effort.In our paper, an approach that use template based medical reports summarization has been developed to transfer medical reports from semi structured and unstructured form to structured form. It classifies the identified entities then extracts important information such as diseases, medical procedures, and drugs. After that, it can discovery hidden relationship between medical information by using association rules. The dataset used in this paper was collected from the Palestinian Ministry of Health.To evaluate the performance and effectiveness of our model, human expert has been used as a reference to measure the degree of acceptance of the extracted association rules which have been extracted from the dataset. So, Likert's scale has been used for evaluation. After the data analysis obtained from the questionnaire. It shows us that the proportion of accuracy association rules, which have been extracted is about 80%.

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“…The accuracy and robustness of the model is currently being refined conceptually and testing with larger data sets, including natural language processing of text data 26 – 28…”

Section: Introductionmentioning

confidence: 99%

An “integrated health neighbourhood” framework to optimise the use of EHR data

Liaw¹,

Lusignan²

2016

jhi

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General practice should become the hub of integrated health neighbourhoods (IHNs), which involves sharing of information to ensure that medical homes are also part of learning organisations that use electronic health record (EHR) data for care, decision making, teaching and learning, quality improvement and research.The IHN is defined as the primary and ambulatory care services in a locality that relates largely to a single hospital-based secondary care service provider and is the logical denominator and unit of comparison for the optimal use of EHR data and health information exchange (HIE) to facilitate integration and coordination of care. Its size may vary based on the geography and requirements of the population, for example between city, suburban and rural areas.The conceptual framework includes context; integration of data, information and knowledge; integration of clinical workflow and practice; and inter-professional integration to ensure coordinated shared care to deliver safe and effective services that are equitable, accessible and culturally respectful.We illustrate how this HIE-supported IHN vision may be achieved with an Australian case study demonstrating the integration of linked pseudonymised records with knowledge-and evidence-based guidelines using semantic web tools and informatics-based methods, researching causal links bewteen data quality and quality of care and the key issues to address.

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Coronary artery disease risk assessment from unstructured electronic health records using text mining

Cited by 69 publications

References 35 publications

Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging

Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging

Template based Medical Reports Summarization

An “integrated health neighbourhood” framework to optimise the use of EHR data

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