An Intelligent Recommender System Based on Short-Term Risk Prediction for Heart Disease Patients

Lafta, Raid; Zhang, Ji; Tao, Xiaohui; Li, Yan; Tseng, Vincent S.

doi:10.1109/wi-iat.2015.47

Cited by 31 publications

(16 citation statements)

References 21 publications

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“…Pattaraintakorn et al (2007) have used rough sets, survival analysis, and patient data to recommend clinical examinations to improve early diagnostics. Lafta et al (2015) have used techniques from recommender systems to predict short-term risk for heart disease patients from personal health records. The field of diagnostics is naturally high in risk; thus most approaches are more decision-support tools than recommendation tools.…”

Section: Health Recommender Systems (Hrs)mentioning

confidence: 99%

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Valdez

Ziefle

2019

International Journal of Human-Computer Studies

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Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n = 521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.

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Section: Health Recommender Systems (Hrs)mentioning

confidence: 99%

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Valdez

Ziefle

2019

International Journal of Human-Computer Studies

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“…Besides recommendation techniques, machine learning approaches have been employed to generate disease predictions. For instance, Lafta et al (Lafta et al 2015) proposed an innovative time series prediction algorithm to support the decision making process of heartdisease patients. Particularly, the algorithm helps to decide whether a medical measurement, such as a heart-rate test, needs to be taken today based on the patient's measurement readings for the past k days.…”

Section: Health Status Predictionmentioning

confidence: 99%

Recommender systems in the healthcare domain: state-of-the-art and research issues

et al. 2020

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Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.

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“…In References 30,31, the authors utilizes algorithm approach for information handling. In Reference 30, the authors utilizes an imaginative time series prediction algorithm calculation to give proposals for coronary illness patients in the well‐being condition. In view of examination of every victim's medicinal tests reports, the framework furnishes restorative examine reports of the patient.…”

Section: Related Workmentioning

confidence: 99%

Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

Sharma

Aujla

Bajaj

2020

Trans Emerging Tel Tech

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Internet of Things (IoT) and Data science have revolutionized the entire technological landscape across the globe. Because of it, the health care ecosystems are adopting the cutting‐edge technologies to provide assistive and personalized care to the patients. But, this vision is incomplete without the adoption of data‐focused mechanisms (like machine learning, big data analytics) that can act as enablers to provide early detection and treatment of patients even without admission in the hospitals. Recently, there has been an increasing trend of providing assistive recommendation and timely alerts regarding the severity of the disease to the patients. Even, remote monitoring of the present day health situation of the patient is possible these days though the analysis of the data generated using IoT devices by doctors. Motivated from these facts, we design a health care recommendation system that provides a multilevel decision‐making related to the risk and severity of the patient diseases. The proposed systems use an all‐disease classification mechanism based on convolutional neural networks to segregate different diseases on the basis of the vital parameters of a patient. After classification, a fuzzy inference system is used to compute the risk levels for the patients. In the last step, based on the information provided by the risk analysis, the patients are provided with the potential recommendation about the severity staging of the associated diseases for timely and suitable treatment. The proposed work has been evaluated using different datasets related to the diseases and the outcomes seem to be promising.

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An Intelligent Recommender System Based on Short-Term Risk Prediction for Heart Disease Patients

Cited by 31 publications

References 21 publications

The users’ perspective on the privacy-utility trade-offs in health recommender systems

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Recommender systems in the healthcare domain: state-of-the-art and research issues

Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

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