Heart Failure Artificial Intelligence-Based Computer Aided Diagnosis Telecare System

Guidi, G.; Iadanza, Ernesto; Pettenati, Maria Chiara; Milli, Massimo; Pavone, Francesco S.; Gentili, G. Biffi

doi:10.1007/978-3-642-30779-9_44

Cited by 24 publications

(17 citation statements)

References 9 publications

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“…Guidi et al 2012 [31] developed a computer aided telecare system aiming to assist in the clinical decision of non-specialist personnel involved in the management of HF patients. Among the functionalities of the telecare system is the characterization of patients as mild, moderate or severe.…”

Section: Severity Estimation Of Hfmentioning

confidence: 99%

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Tripoliti

Papadopoulos

Karanasiou

et al. 2017

Computational and Structural Biotechnology Journal

179

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Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

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Section: Severity Estimation Of Hfmentioning

confidence: 99%

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Tripoliti

Papadopoulos

Karanasiou

et al. 2017

Computational and Structural Biotechnology Journal

179

View full text Add to dashboard Cite

show abstract

“…In the future it will be the starting point for many specialized clinical and non clinical applications, such as chronic care management [3,4], telemedicine application [5,6,7], patients tracking [8,9,10], technology management [11] and drugs smart administration [12,13].…”

Section: Discussionmentioning

confidence: 99%

Careggi Smart hospital: A mobile app for patients, citizens and healthcare staff

Luschi

Belardinelli

Marzi

et al. 2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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Abstract-This paper presents a mobile app called "Careggi Smart Hospital" which has been developed for the Careggi Polyclinic in Florence. The application is designed for Android smartphones and tablets and it is freely downloadable from the Google Play Store. It provides various useful tools to the hospital's users such as personnel and structures finding, wayfinding and the possibility to access personal medical records collected on regional electronic health record.

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“…In [13] neural networks are used as the most suitable solution to outcome prediction tasks in postoperative cardiac patients. An AI-based Computer Aided Diagnosis system is designed in [26] to assist the clinical decision of nonspecialist staff in the analysis of heart failure patients. The system computes the patient's pathological condition and highlights possible aggravations, using four AI-based techniques: a Neural Network, a support vector machine, a decision tree and a fuzzy expert system whose rules are produced by a Genetic Algorithm.…”

Section: Artificial Intelligence In Cardiologymentioning

confidence: 99%

CardioSmart365: Artificial Intelligence in the Service of Cardiologic Patients

Sourla

Sioutas

Syrimpeis

et al. 2012

Advances in Artificial Intelligence

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Artificial intelligence has significantly contributed in the evolution of medical informatics and biomedicine, providing a variety of tools available to be exploited, from rule-based expert systems and fuzzy logic to neural networks and genetic algorithms. Moreover, familiarizing people with smartphones and the constantly growing use of medical-related mobile applications enables complete and systematic monitoring of a series of chronic diseases both by health professionals and patients. In this work, we propose an integrated system for monitoring and early notification for patients suffering from heart diseases. CardioSmart365 consists of web applications, smartphone native applications, decision support systems, and web services that allow interaction and communication among end users: cardiologists, patients, and general doctors. The key features of the proposed solution are (a) recording and management of patients' measurements of vital signs performed at home on regular basis (blood pressure, blood glucose, oxygen saturation, weight, and height), (b) management of patients' EMRs, (c) cardiologic patient modules for the most common heart diseases, (d) decision support systems based on fuzzy logic, (e) integrated message management module for optimal communication between end users and instant notifications, and (f) interconnection to Microsoft HealthVault platform. CardioSmart365 contributes to the effort for optimal patient monitoring at home and early response in cases of emergency.

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Heart Failure Artificial Intelligence-Based Computer Aided Diagnosis Telecare System

Cited by 24 publications

References 9 publications

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Careggi Smart hospital: A mobile app for patients, citizens and healthcare staff

CardioSmart365: Artificial Intelligence in the Service of Cardiologic Patients

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