scholarly journals The Role of Machine Learning in Diagnosing Bipolar Disorder: A Scoping Review (Preprint)

10.2196/29749 ◽  
2021 ◽  
Author(s):  
Zainab Jan ◽  
Noor AI Ansari ◽  
Osama Mousa ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Scoping Review

The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model

2021 ◽  
Vol 145 ◽  
pp. 104311
Author(s):  
Harun Olcay Sonkurt ◽  
Ali Ercan Altınöz ◽  
Emre Çimen ◽  
Ferdi Köşger ◽  
Gürkan Öztürk
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Cognitive Functions ◽  
Learning Model ◽  
Machine Learning Model

scholarly journals The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review

2022 ◽  
Vol 98 ◽  
pp. 103574
Author(s):  
Victor C.H. Chan ◽  
Gwyneth B. Ross ◽  
Allison L. Clouthier ◽  
Steven L. Fischer ◽  
Ryan B. Graham
Keyword(s):  
Machine Learning ◽  
Primary Prevention ◽  
Musculoskeletal Disorders ◽  
Scoping Review ◽  
Work Related ◽  
Work Related Musculoskeletal Disorders

scholarly journals The future of General Movement Assessment: The role of computer vision and machine learning – A scoping review

2021 ◽  
Vol 110 ◽  
pp. 103854
Author(s):  
Nelson Silva ◽  
Dajie Zhang ◽  
Tomas Kulvicius ◽  
Alexander Gail ◽  
Carla Barreiros ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Scoping Review ◽  
The Future ◽  
Movement Assessment ◽  
General Movement

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review

2021 ◽  
pp. 1-11
Author(s):  
Stephanie M. Helman ◽  
Elizabeth A. Herrup ◽  
Adam B. Christopher ◽  
Salah S. Al-Zaiti
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Clinical Applications ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Support Vector ◽  
Learning Techniques ◽  
Machine Learning Applications

Abstract Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review (Preprint)

2021 ◽  
Author(s):  
Zainab Jan ◽  
Noor AI Ansari 2nd ◽  
Osama Mousa 3rd ◽  
Ala Ali E.Abd-Alrazaq 5th ◽  
Mowafa Househ ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Scoping Review ◽  
Process Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Inclusion Criteria ◽  
Bipolar Patients ◽  
Machine Learning Models

BACKGROUND Bipolar disorder (BD) is the tenth common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. BD patients have 9–17 years lower lifetime as compared to the normal population. It is a predominant mental disorder but misdiagnosed as depressive disorder that leads to difficulties in the treatment of affected patients. 60% of patients with bipolar disorder are looking for the treatment of depression. However, machine learning provides advanced skills and techniques for the better diagnosis of bipolar disorder. OBJECTIVE This review aims to explore the machine learning algorithms for the detection and diagnosis of bipolar disorder and its subtypes. METHODS The study protocol adapts PRISMA extension guidelines. It explores three databases, which were Google scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, two levels of screening were carried out: the title and abstract review and the full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed. RESULTS 573 potential articles were retrieved from three databases. After pre-processing and screening, only 33 articles were identified, which met our inclusion criteria. The most commonly used data belonged to the clinical category (n=22, 66.66%). We identified 8 machine learning models used in the selected studies, Support-vector machines (n=9, 27%), Artificial neural network (n=4, 12.12%) , Linear regression (n=3, 0.9%) , Gaussian process model (n=2, 0.6%), Ensemble model (n=2, 0.6%) , Natural language processing (n=1, 0.3%), Probabilistic Methods (n=1, 0.3%), and Logistic regression (n=1, 0.35%). The most common data utilized was magnetic resonance imaging (MRI) for classifying bipolar patients compared to other groups (n=11, 34%) while the least common utilized data was microarray expression dataset and genomic data. The maximum ratio of accuracy was 98% while the minimum accuracy range was 64%. CONCLUSIONS This scoping review provides an overview of recent studies based on machine learning models used to diagnose bipolar disorder patients regardless of their demographics or if they were assessed compared to patients with psychiatric diagnoses. Further research can be conducted for clinical decision support in the health industry. CLINICALTRIAL Null

Better Understanding the Role of Informant Credibility in the Diagnosis of Pediatric Bipolar Disorder

2011 ◽  
Author(s):  
Eric A. Youngstrom ◽  
Melissa M. Jenkins ◽  
Jennifer Kogos Youngstrom ◽  
Jason J. Washburn ◽  
Robert L. Findling
Keyword(s):  
Bipolar Disorder ◽  
Pediatric Bipolar Disorder

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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