Literature DB >> 33430240

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

Oliwia Koteluk1, Adrian Wartecki1, Sylwia Mazurek2,3, Iga Kołodziejczak4, Andrzej Mackiewicz2,3.   

Abstract

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.

Entities:  

Keywords:  algorithm; artificial intelligence; bioinformatics; data mining; data processing; decision making; machine learning; medicine; personalized medicine; personalized treatment

Year:  2021        PMID: 33430240      PMCID: PMC7825660          DOI: 10.3390/jpm11010032

Source DB:  PubMed          Journal:  J Pers Med        ISSN: 2075-4426


  75 in total

Review 1.  Machine Learning in Medicine.

Authors:  Alvin Rajkomar; Jeffrey Dean; Isaac Kohane
Journal:  N Engl J Med       Date:  2019-04-04       Impact factor: 91.245

2.  A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

Authors:  David Silver; Thomas Hubert; Julian Schrittwieser; Ioannis Antonoglou; Matthew Lai; Arthur Guez; Marc Lanctot; Laurent Sifre; Dharshan Kumaran; Thore Graepel; Timothy Lillicrap; Karen Simonyan; Demis Hassabis
Journal:  Science       Date:  2018-12-07       Impact factor: 47.728

3.  Adversarial attacks on medical machine learning.

Authors:  Samuel G Finlayson; John D Bowers; Joichi Ito; Jonathan L Zittrain; Andrew L Beam; Isaac S Kohane
Journal:  Science       Date:  2019-03-22       Impact factor: 47.728

4.  Multiple Deep Learning Architectures Achieve Superior Performance Diagnosing Autism Spectrum Disorder Using Features Previously Extracted from Structural and Functional MRI.

Authors:  Cooper Mellema; Alex Treacher; Kevin Nguyen; Albert Montillo
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2019-07-11

5.  Machine learning for prediction of septic shock at initial triage in emergency department.

Authors:  Joonghee Kim; HyungLan Chang; Doyun Kim; Dong-Hyun Jang; Inwon Park; Kyuseok Kim
Journal:  J Crit Care       Date:  2019-10-22       Impact factor: 3.425

6.  Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data.

Authors:  Mark C Hornbrook; Ran Goshen; Eran Choman; Maureen O'Keeffe-Rosetti; Yaron Kinar; Elizabeth G Liles; Kristal C Rust
Journal:  Dig Dis Sci       Date:  2017-08-23       Impact factor: 3.199

7.  A neural network-based software to recognise blepharospasm symptoms and to measure eye closure time.

Authors:  Gianpaolo F Trotta; Roberta Pellicciari; Antonio Boccaccio; Antonio Brunetti; Giacomo D Cascarano; Vito M Manghisi; Michele Fiorentino; Antonio E Uva; Giovanni Defazio; Vitoantonio Bevilacqua
Journal:  Comput Biol Med       Date:  2019-07-31       Impact factor: 4.589

8.  Long short-term memory RNN for biomedical named entity recognition.

Authors:  Chen Lyu; Bo Chen; Yafeng Ren; Donghong Ji
Journal:  BMC Bioinformatics       Date:  2017-10-30       Impact factor: 3.169

9.  Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Authors:  Du Lei; Walter H L Pinaya; Jonathan Young; Therese van Amelsvoort; Machteld Marcelis; Gary Donohoe; David O Mothersill; Aiden Corvin; Sandra Vieira; Xiaoqi Huang; Su Lui; Cristina Scarpazza; Celso Arango; Ed Bullmore; Qiyong Gong; Philip McGuire; Andrea Mechelli
Journal:  Hum Brain Mapp       Date:  2019-11-18       Impact factor: 5.399

10.  Deep reinforcement learning for de novo drug design.

Authors:  Mariya Popova; Olexandr Isayev; Alexander Tropsha
Journal:  Sci Adv       Date:  2018-07-25       Impact factor: 14.136

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  13 in total

1.  Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study.

Authors:  Li-Ying Huang; Fang-Yu Chen; Mao-Jhen Jhou; Chun-Heng Kuo; Chung-Ze Wu; Chieh-Hua Lu; Yen-Lin Chen; Dee Pei; Yu-Fang Cheng; Chi-Jie Lu
Journal:  J Clin Med       Date:  2022-06-24       Impact factor: 4.964

2.  An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections.

Authors:  Gouse Baig Mohammad; Sirisha Potluri; Ashwani Kumar; Ravi Kumar A; Dileep P; Rajesh Tiwari; Rajeev Shrivastava; Sheo Kumar; K Srihari; Kenenisa Dekeba
Journal:  Comput Intell Neurosci       Date:  2022-05-18

3.  General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology.

Authors:  Yasmine Makhlouf; Manuel Salto-Tellez; Jacqueline James; Paul O'Reilly; Perry Maxwell
Journal:  Diagnostics (Basel)       Date:  2022-05-20

4.  Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study.

Authors:  Fabio Massimo Ulivieri; Luca Rinaudo; Carmelo Messina; Luca Petruccio Piodi; Davide Capra; Barbara Lupi; Camilla Meneguzzo; Luca Maria Sconfienza; Francesco Sardanelli; Andrea Giustina; Enzo Grossi
Journal:  Eur Radiol Exp       Date:  2021-10-19

5.  Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values.

Authors:  Junhak Lee; Jinwoo Jeong; Sungji Jung; Jihoon Moon; Seungmin Rho
Journal:  J Pers Med       Date:  2022-01-31

6.  Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method.

Authors:  Yanfeng Wang; Xisha Miao; Gang Xiao; Chun Huang; Junwei Sun; Ying Wang; Panlong Li; Xu You
Journal:  Front Genet       Date:  2022-04-26       Impact factor: 4.772

7.  Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine.

Authors:  Haseeb Sultan; Muhammad Owais; Chanhum Park; Tahir Mahmood; Adnan Haider; Kang Ryoung Park
Journal:  J Pers Med       Date:  2021-05-27

8.  An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios.

Authors:  Kaixiang Su; Jiao Wu; Dongxiao Gu; Shanlin Yang; Shuyuan Deng; Aida K Khakimova
Journal:  Diagnostics (Basel)       Date:  2021-12-07

Review 9.  Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer.

Authors:  Hang Qiu; Shuhan Ding; Jianbo Liu; Liya Wang; Xiaodong Wang
Journal:  Curr Oncol       Date:  2022-03-07       Impact factor: 3.677

Review 10.  Machine Learning and Smart Devices for Diabetes Management: Systematic Review.

Authors:  Mohammed Amine Makroum; Mehdi Adda; Abdenour Bouzouane; Hussein Ibrahim
Journal:  Sensors (Basel)       Date:  2022-02-25       Impact factor: 3.576

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