A pilot study of a machine-learning tool to assist in the diagnosis of hand arthritis (Preprint)

2020 ◽  
Author(s):  
Mark Daly Reed ◽  
Timothy James Le Souef ◽  
Elliot Rampono
Keyword(s):  
Machine Learning ◽  
Inflammatory Arthritis ◽  
Machine Learning Techniques ◽  
Patient Questionnaire ◽  
Machine Learning Methods ◽  
Applied Machine Learning ◽  
Learning Techniques ◽  
Examination Result ◽  
Delays In Diagnosis ◽  
Rheumatology Practice

BACKGROUND Arthritis is a common condition, which frequently involves the hands. Patients with inflammatory arthritis have been shown to experience significant delays in diagnosis. OBJECTIVE We sought to develop and test a screening tool combining an image of a patient’s hands, a short series of questions, and a single examination technique, to determine the most likely diagnosis in a patient presenting with hand arthritis. Machine learning techniques were used to develop separate algorithms for each component, which were combined to produce a diagnosis. METHODS 280 consecutive new patients presenting to a Rheumatology practice with hand arthritis were enrolled. Each patient completed a 9-part questionnaire, had photographs taken of each hand, and had a single examination result recorded. The Rheumatologist diagnosis was recorded following a 45-minute consultation. The photograph algorithm was developed from a library of 1000 images, and machine learning techniques were applied to the questionnaire results, training several models against the diagnosis from the Rheumatologist. RESULTS The combined algorithms in this study were able to predict inflammatory arthritis with an accuracy, precision, recall and specificity of 96·8%, 97·2%, 98·6% and 90·5% respectively. Similar results were found when inflammatory arthritis was subclassified into rheumatoid arthritis and psoriatic arthritis. The corresponding figures for osteoarthritis were 79·6%, 85·9%, 61·9% and 92·6%. CONCLUSIONS This study demonstrates a novel application of a combined image-processing and a patient questionnaire with applied machine-learning methods, to facilitate the diagnosis of patients presenting with hand arthritis. Preliminary results are encouraging for the application of such techniques in clinical practice. CLINICALTRIAL Not applicable.

Application of Machine Learning Techniques in Drug-Target Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Shengli Zhang ◽  
Jiesheng Wang ◽  
Zhenhui Lin ◽  
Yunyun Liang
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Drug Repositioning ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Machine Learning Methods ◽  
Applied Machine Learning ◽  
Lab Experiments ◽  
Learning Techniques ◽  
Supervised Methods

Background: Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field. Results: The machine learning methods can be divided into three categories basically: Supervised methods, SemiSupervised methods and Unsupervised methods. We reviewed recent representative methods applying machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used in drug discovery. In addition, we compared the advantages and limitations of these methods in each category. Conclusion: Every prediction model has its both strengths and weaknesses and should be adopted in proper ways. Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, overoptimistic results due to the biases and the exploiting of regression models on DTIs prediction should be seriously considered.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Combining Correlation-Based Feature and Machine Learning for Sensory Evaluation of Saigon Beer

2020 ◽  
Vol 11 (2) ◽  
pp. 71-85
Author(s):  
Nhat-Vinh Lu ◽  
Trong-Nhan Vuong ◽  
Duy-Tai Dinh
Keyword(s):  
Machine Learning ◽  
Sensory Evaluation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Methods ◽  
Feature Selection Technique ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Correlation Based Feature Selection ◽  
Positive Results

Sensory evaluation plays an important role in the food and consumer goods industry. In recent years, the application of machine learning techniques to support food sensory evaluation has become popular. Many different machine learning methods have been applied and produced positive results in this field. In this article, the authors propose a new method to support sensory evaluation on multiple criteria based on the use of a correlation-based feature selection technique, combined with machine learning methods such as linear regression, multilayer perceptron, support vector machine, and random forest. Experimental results are based on considering the correlation between physicochemical components and sensory factors on the Saigon beer dataset.

scholarly journals Modern machine learning outperforms GLMs at predicting spikes

2017 ◽  
Author(s):  
Ari S. Benjamin ◽  
Hugo L. Fernandes ◽  
Tucker Tomlinson ◽  
Pavan Ramkumar ◽  
Chris VerSteeg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Activity ◽  
Linear Models ◽  
Feedforward Neural Networks ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Neural Spiking ◽  
Modern Machine

AbstractNeuroscience has long focused on finding encoding models that effectively ask “what predicts neural spiking?” and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a GLM. Here we compared the predictive performance of GLMs to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. In general, the modern methods (particularly XGBoost and the ensemble) produced more accurate spike predictions and were less sensitive to the preprocessing of features. This discrepancy in performance suggests that standard feature sets may often relate to neural activity in a nonlinear manner not captured by GLMs. Encoding models built with machine learning techniques, which can be largely automated, more accurately predict spikes and can offer meaningful benchmarks for simpler models.

scholarly journals Identification of Duplication in Questions Posed on Knowledge Sharing Platform Quora using Machine Learning Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 2444-2451
Keyword(s):  
Machine Learning ◽  
Question Answering ◽  
Contextual Information ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Comparison Methods ◽  
Learning Techniques ◽  
Letter Comparison ◽  
Lower Accuracy

Quora, an online question-answering platform has a lot of duplicate questions i.e. questions that convey the same meaning. Since it is open to all users, anyone can pose a question any number of times this increases the count of duplicate questions. This paper uses a dataset comprising of question pairs (taken from the Quora website) in different columns with an indication of whether the pair of questions are duplicates or not. Traditional comparison methods like Sequence matcher perform a letter by letter comparison without understanding the contextual information, hence they give lower accuracy. Machine learning methods predict the similarity using features extracted from the context. Both the traditional methods as well as the machine learning methods were compared in this study. The features for the machine learning methods are extracted using the Bag of Words models- Count-Vectorizer and TFIDF-Vectorizer. Among the traditional comparison methods, Sequence matcher gave the highest accuracy of 65.29%. Among the machine learning methods XGBoost gave the highest accuracy, 80.89% when Count-Vectorizer is used and 80.12% when TFIDF-Vectorizer is used.

scholarly journals Meta-analysis of voice disorders databases and applied machine learning techniques

2020 ◽  
Vol 17 (6) ◽  
pp. 7958-7979
Author(s):  
Sidra Abid Syed ◽  
◽  
Munaf Rashid ◽  
Samreen Hussain ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Voice Disorders ◽  
Machine Learning Techniques ◽  
Applied Machine Learning ◽  
Learning Techniques

Business Processes, Dynamic Contexts, Learning

2014 ◽  
pp. 407-417
Author(s):  
Michael M. Richter
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Machine Learning Techniques ◽  
Underlying Structure ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Open World ◽  
Learning Techniques

In this article we present relations between complex business processes and machine learning techniques. The processes considered here are mostly related to planning. Planning takes place in preparing many decisions and often it is encountered with a rapidly changing context that constitutes an open world. The underlying structure and preconditions of the processes is quite often not known and hence the processes are regarded as stochastic. One can only observe the processes. Such observations deliver data and these data contain some knowledge about the processes in a hidden form. As a consequence, machine learning methods are involved here. The idea is to give the business persons an overview of quite different machine learning techniques so that they can select suitable ones. We provide a number of examples for business processes that we use for illustrations.

Sign in / Sign up

Export Citation Format

Share Document