Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records

Jonnagaddala, Jitendra; Liaw, Siaw Teng; Ray, Pradeep; Kumar, M. Anand; Dai, Hong Jie; Hsu, Chien Yeh

doi:10.1155/2015/636371

Cited by 29 publications

(33 citation statements)

References 19 publications

(27 reference statements)

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“…It is worth noting that DM complications are far less common and severe in people with well-controlled blood glucose levels. Many of those complications have been studied through machine learning and data mining applications [78], [79], [80], [81], [82], [83], [84], [85], [87], [88], [89], [90], [92], [94], [95], [96], [97].…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…In fact, two out of three people with diabetes die from heart disease or stroke, also called cardiovascular disease. In [92], researchers developed a hybrid approach, partially based on conditional random field classifier, to extract related information on heart disease risk factors from longitudinal unstructured EHRs.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

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Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,012

530

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The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

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Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,012

530

View full text Add to dashboard Cite

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“…Detailed evaluation results were reported in our previous work [26]. Likewise, specific methods related to smoking status detection are reported in [25].…”

Section: Resultsmentioning

confidence: 99%

“…In the field of suicidal behavior, major depression, impulsiveness, and aggressiveness are some of the main factors associated with suicide [37, 38]. [26]. …”

Section: Discussionmentioning

confidence: 99%

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Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients

Dai

Uddin

et al. 2017

Journal of Biomedical Informatics

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Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3 days following a hospital admission. It has also been proven that mental disorders may be a consistent risk factor for the occurrence of violence. In order to better understand violent behaviors of patients with psychiatric disorders, it is important to investigate both the clinical symptoms and psychosocial factors that accompany violence in these patients. In this study, we utilized a dataset released by the Partners Healthcare and Neuropsychiatric Genome-scale and RDoC Individualized Domains project of Harvard Medical School to develop a unique text mining pipeline that processes unstructured clinical data in order to recognize clinical and social parameters such as age, gender, history of alcohol use, violent behaviors, etc., and explored the associations between these parameters and violent behaviors of patients with psychiatric disorders. The aim of our work was to demonstrate the feasibility of mining factors that are strongly associated with violent behaviors among psychiatric patients from unstructured psychiatric evaluation records using clinical text mining. Experiment results showed that stimulants, followed by a family history of violent behavior, suicidal behaviors, and financial stress were strongly associated with violent behaviors. Key aspects explicated in this paper include employing our text mining pipeline to extract clinical and social factors linked with violent behaviors, generating association rules to uncover possible associations between these factors and violent behaviors, and lastly the ranking of top rules associated with violent behaviors using statistical analysis and interpretation.

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