Supplemental Material for Personalized Prognostic Prediction of Treatment Outcome for Depressed Patients in a Naturalistic Psychiatric Hospital Setting: A Comparison of Machine Learning Approaches

2020 ◽  
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Psychiatric Hospital ◽  
Hospital Setting ◽  
Learning Approaches ◽  
Depressed Patients ◽  
Prognostic Prediction

scholarly journals Machine learning approaches in Covid-19 severity risk prediction in Morocco

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Mariam Laatifi ◽  
Samira Douzi ◽  
Abdelaziz Bouklouz ◽  
Hind Ezzine ◽  
Jaafar Jaafari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Biological Data ◽  
Topological Data Analysis ◽  
Test Machine ◽  
Learning Approaches ◽  
Reactive Protein ◽  
Severity Prediction ◽  
New Feature ◽  
Prognostic Prediction

AbstractThe purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

scholarly journals Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning

Molecules ◽  
2019 ◽  
Vol 24 (6) ◽  
pp. 1075 ◽  
Author(s):  
Radosław Chaber ◽  
Christopher Arthur ◽  
Kornelia Łach ◽  
Anna Raciborska ◽  
Elżbieta Michalak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Ewing Sarcoma ◽  
Bone Tumour ◽  
Ftir Spectra ◽  
Support Vector ◽  
Learning Approaches ◽  
Linear Discriminant ◽  
Before And After ◽  
Leave One Out

Background: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma—the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) spectroscopy of tissues allows the bulk biochemical content of a biological sample to be probed and makes possible the study and diagnosis of disease. Methods: In this retrospective study, FTIR spectra of sections of biopsy-obtained bone tissue were recorded. Twenty-seven patients (between 5 and 20 years of age) with newly diagnosed Ewing sarcoma of bone were included in this study. The prognostic value of FTIR spectra obtained from Ewing sarcoma (ES) tumours before and after neoadjuvant chemotherapy were analysed in combination with various data-reduction and machine learning approaches. Results: Random forest and linear discriminant analysis supervised learning models were able to correctly predict patient mortality in 92% of cases using leave-one-out cross-validation. The best performing model for predicting patient relapse was a linear Support Vector Machine trained on the observed spectral changes as a result of chemotherapy treatment, which achieved 92% accuracy. Conclusion: FTIR spectra of tumour biopsy samples may predict treatment outcome in paediatric Ewing sarcoma patients with greater than 92% accuracy.

Scrutiny of Mental Depression through Smartphone Sensors Using Machine Learning Approaches

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abdul Kadar Muhammad Masum ◽  
Erfanul Hoque Bahadur ◽  
Forhada Akther Ruhi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Strategies ◽  
Sensor Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Validation Data ◽  
Data Set ◽  
Mental Depression ◽  
Depressed Patients

In addition to a variety of exceptional sensors, Smartphones now facilitates vigorous open entries in data mining and machine learning to scrutinize the Human Activity Recognition (HAR) system. The follow-up to the treatment of diseases, HAR monitoring system, can be used to recognize mental depression that until now has been overlooked for HAR applications. In this scrutinize, Smartphone sensor data were collected in the 1 Hz frequency from 20 data subjects of different ages. We drove the HAR by using basic machine learning strategies, namely Support Vector Machine, Random Forest, K-Nearest Neighbors, and Artificial Neural Network to recognize physical activities which are associated with mental depression. Random Forest outperformed to recognize daily patterns of activities with 99.80% accuracy of the validation data set. Along with, sensors data was amassed regarding the activities performed over the most recent 14 days continuously from target subjects’ Smartphone. This data was fed to the optimized Random Forest model and quantified the duration of each symptomatic activity of mental depression. Here, a push was connected to figure the risk factor for the probability that an individual has been encountering mental depression. So, a questionnaire was surveyed to collect data from 50 patients who were suffering from mental depression. The questionnaire enquires for the duration of activities related to mental depression. Then, the similarity of these experimental subjects’ activity pattern was measured with those of 50 depressed patients. Finally, data was collected from target subjects’ and applied similarity approach to induce the relation between the target subjects’ and depressed patients. Average similarity value of 90.94% for the depressing subject and 34.99% of the typical subject justifies that this robust system was able to achieve a good performance in terms of measurement of risk factors.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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