scholarly journals Adoption of machine learning for medical diagnosis

2021 ◽  
Author(s):  
Mohammed Yousef Shaheen
Keyword(s):  
Machine Learning ◽  
Chronic Diseases ◽  
Medical Diagnosis ◽  
Patient Data ◽  
Healthcare Industry ◽  
Early Adopter ◽  
Medical Treatments ◽  
Health Related ◽  
Detection And Diagnosis ◽  
Diagnosis Of Diseases

The healthcare industry has historically been an early adopter of technologyadvancements and has reaped significant benefits. Machine learning (an artificialintelligence subset) is being used in a variety of health-related fields, including theinvention of new medical treatments, the management of patient data and records, andthe treatment of chronic diseases. One of the most important uses of machine learningin healthcare is the detection and diagnosis of diseases and conditions that areotherwise difficult to identify. This can range from tumors that are difficult to detect intheir early stages to other hereditary illnesses. This research identifies and discussesthe various usages of machine learning in medical diagnosis.

scholarly journals Adoption of machine learning for medical diagnosis

2021 ◽  
Author(s):  
Mohammed Yousef Shaheen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Chronic Diseases ◽  
Medical Diagnosis ◽  
Healthcare Industry ◽  
Early Adopter ◽  
Medical Treatments ◽  
Health Related ◽  
Detection And Diagnosis ◽  
Diagnosis Of Diseases

The healthcare industry has historically been an early adopter of technology advancements and has reaped significant benefits. Machine learning (an artificial intelligence subset) is being used in a variety of health-related fields, including the invention of new medical treatments, the management of patient data and records, and the treatment of chronic diseases. One of the most important uses of machine learning in healthcare is the detection and diagnosis of diseases and conditions that are otherwise difficult to identify. This can range from tumors that are difficult to detect in their early stages to other hereditary illnesses. This research identifies and discusses the various usages of machine learning in medical diagnosis.

Diagnosis Rule Extraction from Patient Data for Chronic Kidney Disease Using Machine Learning

2020 ◽  
pp. 1165-1174
Author(s):  
Alexander Arman Serpen
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Learning Algorithm ◽  
Patient Data ◽  
Actual Patient ◽  
C4.5 Decision Tree ◽  
Health Related ◽  
Pedal Edema

This research study employed a machine learning algorithm on actual patient data to extract decision making rules that can be used to diagnose chronic kidney disease. The patient data set entails a number of health-related attributes or indicators and contains 250 patients positive for chronic kidney disease. The C4.5 decision tree algorithm was applied to the patient data to formulate a set of diagnosis rules for chronic kidney disease. The C4.5 algorithm utilizing 3-fold cross validation achieved 98.25% prediction accuracy and thus correctly classified 393 instances and incorrectly classified 7 instances for a total patient count of 400. The extracted rule set highlighted the need to monitor serum creatinine levels in patients as the primary indicator for the presence of disease. Secondary indicators were pedal edema, hemoglobin, diabetes mellitus and specific gravity. The set of rules provides a preliminary screening tool towards conclusive diagnosis of the chronic kidney disease by nephrologists following timely referral by the primary care providers or decision-making algorithms.

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

2020 ◽  
Vol 21 ◽  
Author(s):  
Sukanya Panja ◽  
Sarra Rahem ◽  
Cassandra J. Chu ◽  
Antonina Mitrofanova
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Therapeutic Response ◽  
Patient Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Human Machine Interaction ◽  
Data Repositories ◽  
Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

scholarly journals Author Correction: Improving the accuracy of medical diagnosis with causal machine learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan G. Richens ◽  
Ciarán M. Lee ◽  
Saurabh Johri
Keyword(s):  
Machine Learning ◽  
Medical Diagnosis

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21494-9

scholarly journals The impact of multimorbidity patterns on health-related quality of life in the general population: results of the Belgian Health Interview Survey

2021 ◽  
Author(s):  
Lisa Van Wilder ◽  
Brecht Devleesschauwer ◽  
Els Clays ◽  
Stefanie De Buyser ◽  
Johan Van der Heyden ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Chronic Diseases ◽  
Disease Burden ◽  
Health Related Quality ◽  
Health Interview Survey ◽  
Related Quality ◽  
Health Related ◽  
Interview Survey ◽  
The Impact

Abstract Background Chronic diseases and multimorbidity are a major cause of disease burden—for patients, caregivers, and society. Little is known however about potential interaction effects between specific disease combinations. Besides an additive effect, the presence of multiple conditions could also act synergistically or antagonistically regarding the impact on patients’ health-related quality of life (HRQoL). The aim was to estimate the impact of coexisting chronic diseases on HRQoL of the adult general Belgian population. Methods The Belgian Health Interview Survey 2018 provided data on self-reported chronic conditions and HRQoL (EQ-5D-5L) for a nationally representative sample. Linear mixed models were used to analyze two-way and three-way interactions of disease combinations on HRQoL. Results Multimorbidity had a prevalence of 46.7% (≥ 2 conditions) and 29.7% (≥ 3 conditions). HRQoL decreased considerably with the presence of multiple chronic diseases. 14 out of 41 dyad combinations and 5 out of 13 triad combinations showed significant interactions, with a dominant presence of negative/synergistic effects. Positive/antagonistic effects were found in more subjective chronic diseases such as depression and chronic fatigue. Conditions appearing the most frequently in significant disease pair interactions were dorsopathies, respiratory diseases, and arthropathies. Conclusions Diverse multimorbidity patterns, both dyads and triads, were synergistically or antagonistically associated with lower HRQoL. Tackling the burden of multimorbidity is needed, especially because most disease combinations affect each other synergistically, resulting in a greater reduction in HRQoL. Further knowledge about those multimorbidity patterns with a greater impact on HRQoL is needed to better understand disease burden beyond mortality and morbidity data.

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