Literature DB >> 32655135

Future possibilities for artificial intelligence in the practical management of hypertension.

Hiroshi Koshimizu1,2, Ryosuke Kojima1, Yasushi Okuno3.   

Abstract

The use of artificial intelligence in numerous prediction and classification tasks, including clinical research and healthcare management, is becoming increasingly more common. This review describes the current status and a future possibility for artificial intelligence in blood pressure management, that is, the possibility of accurately predicting and estimating blood pressure using large-scale data, such as personal health records and electronic medical records. Individual blood pressure continuously changes because of lifestyle habits and the environment. This review focuses on two topics regarding controlling changing blood pressure: a novel blood pressure measurement system and blood pressure analysis using artificial intelligence. Regarding the novel blood pressure measurement system, we compare the conventional cuff-less method with the analysis of pulse waves using artificial intelligence for blood pressure estimation. Then, we describe the prediction of future blood pressure values using machine learning and deep learning. In addition, we summarize factor analysis using "explainable AI" to solve a black-box problem of artificial intelligence. Overall, we show that artificial intelligence is advantageous for hypertension management and can be used to establish clinical evidence for the practical management of hypertension.

Entities:  

Keywords:  Artificial intelligence; Blood pressure management; Blood pressure measurement; Blood pressure prediction; Machine learning

Year:  2020        PMID: 32655135     DOI: 10.1038/s41440-020-0498-x

Source DB:  PubMed          Journal:  Hypertens Res        ISSN: 0916-9636            Impact factor:   3.872


  37 in total

Review 1.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

2.  Clinical Decision Support in the Era of Artificial Intelligence.

Authors:  Edward H Shortliffe; Martin J Sepúlveda
Journal:  JAMA       Date:  2018-12-04       Impact factor: 56.272

3.  Deep Learning-A Technology With the Potential to Transform Health Care.

Authors:  Geoffrey Hinton
Journal:  JAMA       Date:  2018-09-18       Impact factor: 56.272

4.  Blood pressure categories and long-term risk of cardiovascular disease according to age group in Japanese men and women.

Authors:  Akira Fujiyoshi; Takayoshi Ohkubo; Katsuyuki Miura; Yoshitaka Murakami; Shin-Ya Nagasawa; Tomonori Okamura; Hirotsugu Ueshima
Journal:  Hypertens Res       Date:  2012-06-28       Impact factor: 3.872

5.  Big Data and Machine Learning in Health Care.

Authors:  Andrew L Beam; Isaac S Kohane
Journal:  JAMA       Date:  2018-04-03       Impact factor: 56.272

Review 6.  Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

Authors:  Seong Ho Park; Kyunghwa Han
Journal:  Radiology       Date:  2018-01-08       Impact factor: 11.105

7.  An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.

Authors:  Zachi I Attia; Peter A Noseworthy; Francisco Lopez-Jimenez; Samuel J Asirvatham; Abhishek J Deshmukh; Bernard J Gersh; Rickey E Carter; Xiaoxi Yao; Alejandro A Rabinstein; Brad J Erickson; Suraj Kapa; Paul A Friedman
Journal:  Lancet       Date:  2019-08-01       Impact factor: 79.321

8.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

Authors:  Awni Y Hannun; Pranav Rajpurkar; Masoumeh Haghpanahi; Geoffrey H Tison; Codie Bourn; Mintu P Turakhia; Andrew Y Ng
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

Review 9.  Artificial intelligence in healthcare: past, present and future.

Authors:  Fei Jiang; Yong Jiang; Hui Zhi; Yi Dong; Hao Li; Sufeng Ma; Yilong Wang; Qiang Dong; Haipeng Shen; Yongjun Wang
Journal:  Stroke Vasc Neurol       Date:  2017-06-21

10.  International evaluation of an AI system for breast cancer screening.

Authors:  Scott Mayer McKinney; Marcin Sieniek; Varun Godbole; Jonathan Godwin; Natasha Antropova; Hutan Ashrafian; Trevor Back; Mary Chesus; Greg S Corrado; Ara Darzi; Mozziyar Etemadi; Florencia Garcia-Vicente; Fiona J Gilbert; Mark Halling-Brown; Demis Hassabis; Sunny Jansen; Alan Karthikesalingam; Christopher J Kelly; Dominic King; Joseph R Ledsam; David Melnick; Hormuz Mostofi; Lily Peng; Joshua Jay Reicher; Bernardino Romera-Paredes; Richard Sidebottom; Mustafa Suleyman; Daniel Tse; Kenneth C Young; Jeffrey De Fauw; Shravya Shetty
Journal:  Nature       Date:  2020-01-01       Impact factor: 49.962

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  2 in total

1.  Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.

Authors:  Yogesh Kumar; Apeksha Koul; Ruchi Singla; Muhammad Fazal Ijaz
Journal:  J Ambient Intell Humaniz Comput       Date:  2022-01-13

2.  A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR.

Authors:  Mengran Zhou; Kai Bian; Feng Hu; Wenhao Lai
Journal:  Front Bioeng Biotechnol       Date:  2022-07-11
  2 in total

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