An intelligent recommender system based on predictive analysis in telehealthcare environment

Lafta, Raid; Zhang, Ji; Tao, Xiaohui; Li, Yan; Tseng, Vincent S.; Luo, Yonglong; Chen, Fulong

doi:10.3233/web-160348

Cited by 23 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in the context of HF, limited studies apply predictive models. Lafta et al [24] is one of these studies that using several telemonitored attributes (i.e., heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, and oxygen saturation) applied basic time series prediction algorithm, regression-based time series prediction algorithm, and hybrid time series prediction algorithm. e obtained results showed that up to 75% and 98% of accuracy values could be obtained across different patients under three algorithms, but still the accuracy value is not objective enough to determine how well the system performs.…”

Section: Predictive Models On Telemedicine Systemsmentioning

confidence: 99%

Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model

Larburu

Artetxe

Escolar

et al. 2018

Mobile Information Systems

View full text Add to dashboard Cite

Rapid advances in ICT and collection of large amount of mobile health data are giving room to new ways of treating patients. Studies suggest that telemonitoring systems and predictive models for clinical support and patient empowerment may improve several pathologies, such as heart failure, which admissions rate is high. In the current medical practice, clinicians make use of simple rules that generate large number of false alerts. In order to reduce the false alerts, in this study, the predictive models to prevent decompensations that may lead into admissions are presented. They are based on mobile clinical data of 242 heart failure (HF) patients collected for a period of 44 months in the public health service of Basque Country (Osakidetza). The best predictive model obtained is a combination of alerts based on monitoring data and a questionnaire with a Naive Bayes classifier using Bernoulli distribution. This predictive model performs with an AUC = 67% and reduces the false alerts per patient per year from 28.64 to 7.8. This way, the system predicts the risk of admission of ambulatory patients with higher reliability than current alerts.

show abstract

Section: Predictive Models On Telemedicine Systemsmentioning

confidence: 99%

Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model

Larburu

Artetxe

Escolar

et al. 2018

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…For the success of the recommender system, it is very important to choose what type of criteria are used to evaluate the recommender system. Conventionally, recommender systems were evaluated based on criteria borrowed from information retrieval [9,31,36]. Common metrics used in the evaluation are:…”

Section: Evaluation Of Hrsmentioning

confidence: 99%

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

et al. 2019

View full text Add to dashboard Cite

In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

show abstract

“…The increasing amount of data available to medical professionals for diagnosing diseases and developing treatment plans for their patients raise the importance of having suitable tools to harness such data and transform them into meaningful information. Such tools are also useful for evidence-based decision making within the healthcare domain [14]. Health-care professionals can no longer solely rely on pen and paper as more and more data are in digital form.…”

Section: Introductionmentioning

confidence: 99%

Feature-Based Learning in Drug Prescription System for Medical Clinics

Goh

Tao

Zhang

et al. 2020

Neural Process Lett

Self Cite

View full text Add to dashboard Cite

Rapid increases in data volume and variety pose a challenge to safe drug prescription for health professionals like doctors and dentists. This is addressed by our study, which presents innovative approaches in mining data from drug corpus and extracting feature vectors to combine this knowledge with individual patient medical profiles. Within our three-tiered frameworkthe prediction layer, the knowledge layer and the presentation layer-we describe multiple approaches in computing similarity ratios from the feature vectors, illustrated with an example of applying the framework in a typical medical clinic. Experimental evaluation shows that the word embedding model performs better than the adverse network model, with a F score of 0.75. The F score is a common metrics used for evaluating the performance of classification algorithms. Similarity to a drug the patient is allergic to or is taking are important considerations for the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors thereby increasing patient health outcomes. Keywords Feature vector • Similarity ratio • Word embedding • Adverse network model • Personalised drug prescription This work is partially supported by Glory Dental Surgery Pte Ltd and undertaken collaboratively with their panel of dentists. We would like to thank Dr. Xueling Oh and Dr. Elizabeth Goh for enriching the authors understanding of drug prescription within the medical domain.

show abstract

An intelligent recommender system based on predictive analysis in telehealthcare environment

Cited by 23 publications

References 28 publications

Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model

Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model

DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering

Feature-Based Learning in Drug Prescription System for Medical Clinics

Contact Info

Product

Resources

About