Artificial Intelligence versus Doctors' Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography
| Autor: | Raffaele Abate, Ilie Bucur, Mihaela Rusu, Victor Ponomariov, Liviu Chirila, Elisa A. Liehn, Florentina-Mihaela Apipie, Zhuojun Wu |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Artificial Intelligence System
Computer science business.industry Human intelligence multiple processing layers autonomic learning Computational intelligence Marketing and artificial intelligence Review Article Artificial psychology Symbolic artificial intelligence Machine learning computer.software_genre machine intelligence algorithms artificial intelligence machine learning computational models Artificial intelligence Hyper-heuristic business Automated ECG interpretation computer |
| Zdroj: | Discoveries |
| ISSN: | 2359-7232 |
| Popis: | Computational machine learning, especially self-enhancing algorithms, prove remarkable effectiveness in applications, including cardiovascular medicine. This review summarizes and cross-compares the current machine learning algorithms applied to electrocardiogram interpretation. In practice, continuous real-time monitoring of electrocardiograms is still difficult to realize. Furthermore, automated ECG interpretation by implementing specific artificial intelligence algorithms is even more challenging. By collecting large datasets from one individual, computational approaches can assure an efficient personalized treatment strategy, such as a correct prediction on patient-specific disease progression, therapeutic success rate and limitations of certain interventions, thus reducing the hospitalization costs and physicians' workload. Clearly such aims can be achieved by a perfect symbiosis of a multidisciplinary team involving clinicians, researchers and computer scientists. Summarizing, continuous cross-examination between machine intelligence and human intelligence is a combination of precision, rationale and high-throughput scientific engine integrated into a challenging framework of big data science. |
| Databáze: | OpenAIRE |
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