[HTML][HTML] Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review

MF Safdar, RM Nowak, P Pałka - Computers in Biology and Medicine, 2024‏ - Elsevier
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the
heart's electrical activity that depicts the movement of cardiac muscles. A review study has …

A survey on agents applications in healthcare: Opportunities, challenges and trends

E Sulis, S Mariani, S Montagna - Computer Methods and Programs in …, 2023‏ - Elsevier
Abstract Background and Objective: The agent abstraction is a powerful one, developed
decades ago to represent crucial aspects of artificial intelligence research. The meaning has …

[HTML][HTML] Towards a goal-oriented methodology for clinical-guideline-based management recommendations for patients with multimorbidity: GoCom and its preliminary …

A Kogan, M Peleg, SW Tu, R Allon, N Khaitov… - Journal of Biomedical …, 2020‏ - Elsevier
Patients with chronic multimorbidity are becoming more common as life expectancy
increases, making it necessary for physicians to develop complex management plans. We …

Clinical guidelines: a crossroad of many research areas. challenges and opportunities in process mining for healthcare

R Gatta, M Vallati, C Fernández-Llatas… - … Workshops: BPM 2019 …, 2019‏ - Springer
Clinical Guidelines, medical protocols, and other healthcare indications, cover a significant
slice of physicians daily routine, as they are used to support clinical choices also with …

Learning to select goals in Automated Planning with Deep-Q Learning

C Núñez-Molina, J Fernández-Olivares… - Expert Systems with …, 2022‏ - Elsevier
In this work we propose a planning and acting architecture endowed with a module which
learns to select subgoals with Deep Q-Learning. This allows us to decrease the load of a …

Using graph rewriting to operationalize medical knowledge for the revision of concurrently applied clinical practice guidelines

M Michalowski, M Rao, S Wilk, W Michalowski… - Artificial Intelligence in …, 2023‏ - Elsevier
Clinical practice guidelines (CPGs) are patient management tools that synthesize medical
knowledge into an actionable format. CPGs are disease specific with limited applicability to …

APPRAISE-RS: Automated, updated, participatory, and personalized treatment recommender systems based on GRADE methodology

B López, O Raya, E Baykova, M Saez, D Rigau… - Heliyon, 2023‏ - cell.com
Abstract Purpose Clinical practice guidelines (CPGs) have become fundamental tools for
evidence-based medicine (EBM). However, CPG suffer from several limitations, including …

Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning

W Van Woensel, SSR Abidi, SR Abidi - Artificial Intelligence in Medicine, 2021‏ - Elsevier
In case of comorbidity, ie, multiple medical conditions, Clinical Decision Support Systems
(CDSS) should issue recommendations based on all relevant disease-related Clinical …

Supporting physicians in the coordination of distributed execution of CIGs to treat comorbid patients

A Bottrighi, L Piovesan, P Terenziani - Artificial Intelligence in Medicine, 2023‏ - Elsevier
Abstract Clinical Practice Guidelines (CPGs) encode the “best” medical practices to treat
patients affected by a specific disease and are widely used in the medical practice. Starting …

Planning to be healthy: towards personalized medication planning

L Alon, H Weitman, A Shleyfman, GA Kaminka - ECAI 2024, 2024‏ - ebooks.iospress.nl
Personalized medication plans determine the selection, dosage, and administration
schedule of medications, to achieve medical goals that are specific to the patient and to its …