scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

How personalized medical data could improve health care

2014 ◽  
Vol 67 ◽  
pp. 303-305
Author(s):  
Niklas Andersson ◽  
Alice Grinberg ◽  
Niels Okkels
Keyword(s):  
Health Care ◽  
Medical Data ◽  
Improve Health Care ◽  
Improve Health

scholarly journals Machine learning based outlier detection for medical data

2021 ◽  
Vol 24 (1) ◽  
pp. 564
Author(s):  
R. Vijaya Kumar Reddy ◽  
Shaik Subhani ◽  
B. Srinivasa Rao ◽  
N. Lakshmipathi Anantha
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Outlier Detection ◽  
Health Care Industry ◽  
Work Load ◽  
Medical Data ◽  
The Novel ◽  
Cleaning Process ◽  
Care Industry ◽  
Global Data

<p>The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlier finding algorithm given that best accurateness.</p>

scholarly journals Accessing the ECG Data of the Apple Watch and Accomplishing Interoperability Through FHIR

2021 ◽  
Author(s):  
Alexander Bartschke ◽  
Yannick Börner ◽  
Sylvia Thun
Keyword(s):  
Health Care ◽  
Efficient Solution ◽  
Medical Data ◽  
Data Standard ◽  
Reference Number ◽  
Iphone Application ◽  
New Treatment ◽  
User Friendly ◽  
Minimal Effort ◽  
Ecg Data

Medical data generated by wearables and smartphones can add value to health care and medical research. This also applies to the ECG data that is created with Apple Watch 4 or later. However, Apple currently does not provide an efficient solution for accessing and sharing ECG raw data in a standardized data format. Our method aims to provide a solution that enables patients to share their Apple Watch’s ECG data with any health care institution via an iPhone application. We achieved this by implementing a parser in Swift that converts the Apple Watch’s raw ECG data into a FHIR observation. Furthermore, we added the capability of transmitting these observations to a specified server and equipping it with the patient’s reference number. The result is a user-friendly iPhone application, enabling patients to share their Apple Watch’s ECG data in a widely known health data standard with minimal effort. This allows the personnel involved in the patient’s treatment to use data that was previously difficult to access for further analyses and processing. Our solution can facilitate research for new treatment methods, for example, utilizing the Apple Watch for continuous monitoring of heart activity and early detection of heart conditions.

scholarly journals Blockchain Technology Projects to Provide Telemedical Services: Systematic Review

10.2196/17475 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e17475
Author(s):  
Konstantin Koshechkin ◽  
Georgy Lebedev ◽  
George Radzievsky ◽  
Ralf Seepold ◽  
Natividad Madrid Martinez
Keyword(s):  
Health Care ◽  
Web Search ◽  
Medical Service ◽  
Digital Technologies ◽  
Data Access ◽  
Medical Data ◽  
Blockchain Technology ◽  
Wide Range ◽  
Treatment Support ◽  
Diagnostic Support

Background One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment. Objective This paper aims to review the feasibility of blockchain technology for telemedicine. Methods The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex). Results Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%). Conclusions These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.

scholarly journals A Survey on Securing Medical Data in Cloud Using Blockchain

2021 ◽  
Author(s):  
HariPriya K ◽  
Brintha NC ◽  
Yogesh C K
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Deep Understanding ◽  
Medical Data ◽  
Cloud Environment ◽  
Healthcare Data ◽  
The Past ◽  
Blockchain Technology ◽  
Block Chain ◽  
Security Concerns

Security is a major concern in every technology that is introduced newly to facilitate the existing mechanism for better maintenance and handling. This is also the case in electronic health records. The data of the hospitals and the associated patients gets digital in the past few decades. The data is stored in the cloud for various reasons such as convenience of the participating entities to access it, easy maintenance. But, with this there also arises various security concerns. It has been observed from the reason studies that blockchain is used as the means of securing the healthcare data in the cloud environment.This study discusses the following. 1) Applications of blockchain in cloud environment, 2) Applications of blockchain in securing healthcare data 3) General issues and security concerns in blockchain technology and what features of block chain makes it suitable for securing health care a nd what features restricts it from using.This work helps the future researchers in getting a deep understanding of the in and out of applying blockchain in cloud and healthcare environment.

E-Health Security and Privacy

2008 ◽  
pp. 580-588
Author(s):  
Yingge Wang ◽  
Qiang Cheng ◽  
Jie Cheng
Keyword(s):  
Health Care ◽  
Information Technologies ◽  
Medical Data ◽  
Security And Privacy ◽  
The Internet ◽  
Electronic Patient Records ◽  
Communication Techniques ◽  
Cost Efficient ◽  
Use Of The Internet ◽  
Heavy Loads

The widespread and fast-developing information technologies, especially wireless communications and the Internet, have allowed for the realization of greater automation systems than ever in health-care industries: E-health has become an apparent trend, and having a clinic at home or even anywhere at anytime is no longer a dream. E-health, including telemedicine featured by conducting health-care transactions over the Internet, has been revolutionizing the well-being of human society. Traditionally, common practices in the health-care industry place tremendous burdens on both patients and health-care providers, with heavy loads of paper-based documents and inefficient communications through mail or phone calls. The transmission of medical data is even messy for cases in which patients have to transfer between different health providers. In addition, the medical documents prepared manually are prone to errors and delays, which may lead to serious consequences. The time, energy, and resources wasted in such processes are intolerable and unimaginable in any fast-paced society. For these problems, e-health provides powerful solutions to share and exchange information over the Internet in a timely, easy, and safe manner (Balas et al., 1997). Incorporating fast and cost-efficient Internet and wireless communication techniques has enabled the substantial development of e-health. The use of the Internet to transmit sensitive medical data, however, leaves the door open to the threats of information misuse either accidentally or maliciously. Health-care industries need be extremely cautious in handling and delivering electronic patient records using computer networks due to the high vulnerabilities of such information. To this extent, security and privacy issues become two of the biggest concerns in developing e-health infrastructures.

Heuristics in Medical Data Mining

2005 ◽  
pp. 1322-1326
Author(s):  
Susan E. George
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Health Care Providers ◽  
Medical Data ◽  
Clinical Record ◽  
Care Providers ◽  
Medical Data Mining ◽  
Intelligent Interfaces ◽  
Stock Control ◽  
Coding Schemes

Deriving—or discovering—information from data has come to be known as data mining. Within health care, the knowledge from medical mining has been used in tasks as diverse as patient diagnosis (Brameier et al., 2000; Mani et al., 1999; Cao et al., 1998; Henson et al., 1996), inventory stock control (Bansal et al., 2000), and intelligent interfaces for patient record systems (George at al., 2000). It has also been a tool of medical discovery itself (Steven et al., 1996). Yet, it remains true that medicine is one of the last areas of society to be “automated,” with a relatively recent increase in the volume of electronic data, many paper-based clinical record systems in use, a lack of standardisation (for example, among coding schemes), and still some reluctance among health-care providers to use computer technology. Nevertheless, the rapidly increasing volume of electronic medical data is perhaps one of the domain’s current distinguishing characteristics, as one of the last components of society to be “automated.”

Sign in / Sign up

Export Citation Format

Share Document