Predictors of pharmacotherapy outcomes for body dysmorphic disorder: a machine learning approach

2022 ◽  
pp. 1-11
Author(s):  
Joshua E. Curtiss ◽  
Emily E. Bernstein ◽  
Sabine Wilhelm ◽  
Katharine A. Phillips
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Treatment Outcomes ◽  
Treatment Response ◽  
Partial Remission ◽  
Body Dysmorphic Disorder ◽  
Demographic Variables ◽  
Support Vector ◽  
Full Remission ◽  
Feature Importance

Abstract Background Serotonin-reuptake inhibitors (SRIs) are first-line pharmacotherapy for the treatment of body dysmorphic disorder (BDD), a common and severe disorder. However, prior research has not focused on or identified definitive predictors of SRI treatment outcomes. Leveraging precision medicine techniques such as machine learning can facilitate the prediction of treatment outcomes. Methods The study used 10-fold cross-validation support vector machine (SVM) learning models to predict three treatment outcomes (i.e. response, partial remission, and full remission) for 97 patients with BDD receiving up to 14-weeks of open-label treatment with the SRI escitalopram. SVM models used baseline clinical and demographic variables as predictors. Feature importance analyses complemented traditional SVM modeling to identify which variables most successfully predicted treatment response. Results SVM models indicated acceptable classification performance for predicting treatment response with an area under the curve (AUC) of 0.77 (sensitivity = 0.77 and specificity = 0.63), partial remission with an AUC of 0.75 (sensitivity = 0.67 and specificity = 0.73), and full remission with an AUC of 0.79 (sensitivity = 0.70 and specificity = 0.79). Feature importance analyses supported constructs such as better quality of life and less severe depression, general psychopathology symptoms, and hopelessness as more predictive of better treatment outcome; demographic variables were least predictive. Conclusions The current study is the first to demonstrate that machine learning algorithms can successfully predict treatment outcomes for pharmacotherapy for BDD. Consistent with precision medicine initiatives in psychiatry, the current study provides a foundation for personalized pharmacotherapy strategies for patients with BDD.

scholarly journals The Comparison and Interpretation of Machine-Learning Models in Post-Stroke Functional Outcome Prediction

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1784
Author(s):  
Shih-Chieh Chang ◽  
Chan-Lin Chu ◽  
Chih-Kuang Chen ◽  
Hsiang-Ning Chang ◽  
Alice M. K. Wong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Superior Performance ◽  
Support Vector ◽  
Balance Test ◽  
Post Stroke ◽  
Feature Importance ◽  
Value Range ◽  
Importance Analysis ◽  
Partial Dependence

Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors’ ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range.

scholarly journals Interpretable Machine Learning for Early Neurological Deterioration Prediction in Atrial Fibrillation-Related Stroke

2021 ◽  
Author(s):  
Seong Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi O ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

Abstract We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multi-center prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanations (SHAP) method to evaluate feature importance. Of the 3,623 stroke patients, the 2,363 who had arrived at the hospital within 24 hours of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.778, 95% CI, 0.726 - 0.830). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the SHAP method can be adjusted to individualize the features’ effects on the predictive power of the model.

scholarly journals Empirically defining treatment response and remission in body dysmorphic disorder

2019 ◽  
pp. 1-7 ◽  
Author(s):  
Lorena Fernández de la Cruz ◽  
Jesper Enander ◽  
Christian Rück ◽  
Sabine Wilhelm ◽  
Katharine A. Phillips ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Treatment Response ◽  
Body Dysmorphic Disorder ◽  
Detection Methods ◽  
Obsessive Compulsive ◽  
Pooled Data ◽  
Randomized Controlled ◽  
Full Remission ◽  
End Of Treatment ◽  
The Usa

Abstract Background The number of clinical trials in body dysmorphic disorder (BDD) has steadily increased in recent years. As the number of studies grows, it is important to define the most empirically useful definitions for response and remission in order to enhance field-wide consistency and comparisons of treatment outcomes across studies. In this study, we aim to operationally define treatment response and remission in BDD. Method We pooled data from three randomized controlled trials of cognitive-behavior therapy (CBT) for BDD (combined n = 153) conducted at four academic sites in Sweden, the USA, and England. Using signal detection methods, we examined the Yale-Brown Obsessive Compulsive Scale modified for BDD (BDD–YBOCS) score that most reliably identified patients who responded to CBT and those who achieved remission from BDD symptoms at the end of treatment. Results A BDD–YBOCS reduction ⩾30% was most predictive of treatment response as defined by the Clinical Global Impression (CGI) – Improvement scale (sensitivity 0.89, specificity 0.91, 91% correctly classified). At post-treatment, a BDD–YBOCS score ⩽16 was the best predictor of full or partial symptom remission (sensitivity 0.85, specificity 0.99, 97% correctly classified), defined by the CGI – Severity scale. Conclusion Based on these results, we propose conceptual and operational definitions of response and full or partial remission in BDD. A consensus regarding these constructs will improve the interpretation and comparison of future clinical trials, as well as improve communication among researchers, clinicians, and patients. Further research is needed, especially regarding definitions of full remission, recovery, and relapse.

scholarly journals Tiling the genome into consistently named subsequences enables precision medicine and machine learning with millions of complex individual data-sets

2015 ◽  
Author(s):  
Sarah Guthrie ◽  
Abram Connelly ◽  
Peter Amstutz ◽  
Adam F. Berrey ◽  
Nicolas Cesar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Blood Type ◽  
Whole Genome Sequence ◽  
Medical Community ◽  
Support Vector ◽  
Personal Genome ◽  
Whole Genome ◽  
Genome Sequences ◽  
Global Alliance

The scientific and medical community is reaching an era of inexpensive whole genome sequencing, opening the possibility of precision medicine for millions of individuals. Here we present tiling: a flexible representation of whole genome sequences that supports simple and consistent names, annotation, queries, machine learning, and clinical screening. We partitioned the genome into 10,655,006 tiles: overlapping, variable-length sequences that begin and end with unique 24-base tags. We tiled and annotated 680 public whole genome sequences from the 1000 Genomes Project Consortium (1KG) and Harvard Personal Genome Project (PGP) using ClinVar database information. These genomes cover 14.13 billion tile sequences (4.087 trillion high quality bases and 0.4321 trillion low quality bases) and 251 phenotypes spanning ICD-9 code ranges 140-289, 320-629, and 680-759. We used these data to build a Global Alliance for Genomics and Health Beacon and graph database. We performed principal component analysis (PCA) on the 680 public whole genomes, and by projecting the tiled genomes onto their first two principal components, we replicated the 1KG principle component separation by population ethnicity codes. Interestingly, we found the PGP self reported ethnicities cluster consistently with 1KG ethnicity codes. We built a set of support-vector ABO blood-type classifiers using 75 PGP participants who had both a whole genome sequence and a self-reported blood type. Our classifier predicts A antigen presence to within 1% of the current state-of-the art for in silico A antigen prediction. Finally, we found six PGP participants with previously undiscovered pathogenic BRCA variants, and using our tiling, gave them simple, consistent names, which can be easily and independently re-derived. Given the near-future requirements of genomics research and precision medicine, we propose the adoption of tiling and invite all interested individuals and groups to view, rerun, copy, and modify these analyses at https://curover.se/su92l- j7d0g-swtofxa2rct8495

Precision Medicine for the Treatment of Depression

Depression ◽  
2019 ◽  
pp. 475-486
Author(s):  
Madhukar H. Trivedi ◽  
Farra Kahalnik ◽  
Tracy L. Greer
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Precision Medicine ◽  
Treatment Outcomes ◽  
Treatment Response ◽  
Successful Treatment ◽  
Biological Markers ◽  
Major Depressive ◽  
Ongoing Research ◽  
Treatment Of Depression

Although in recent years we have gained a deeper understanding of the pathophysiology of major depressive disorder, this improved understanding has not translated into improved treatment outcomes. Therefore, the screening of putative biological markers may be crucial to facilitate more rapid, successful treatment. Ongoing research has explored the importance of studying physiological biomarkers, including neuroimaging, neurophysiology, genomics, proteomics, and metabolomics, as well as cognition, to gain a better understanding of subtypes of depression and treatment response. However, only through an integrated, multimodal biomarker approach can we truly achieve better outcomes.

scholarly journals Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection

2020 ◽  
Vol 10 (3) ◽  
pp. 934 ◽  
Author(s):  
Eufemia Lella ◽  
Angela Lombardi ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Tommaso Maggipinto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Imaging Modality ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Whole Brain ◽  
Feature Importance ◽  
Importance Analysis ◽  
The Brain

Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer’s disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions.

scholarly journals Permutation-based identification of important biomarkers for complex diseases via machine learning models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xinlei Mi ◽  
Baiming Zou ◽  
Fei Zou ◽  
Jianhua Hu
Keyword(s):  
Machine Learning ◽  
Human Disease ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Individual Feature ◽  
Learning Models ◽  
Efficient Manner ◽  
Feature Importance ◽  
Machine Learning Models

AbstractStudy of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods have been developed and widely used to alleviate some analytic challenges in complex human disease studies. While enjoying the modeling flexibility and robustness, these model frameworks suffer from non-transparency and difficulty in interpreting each individual feature due to their sophisticated algorithms. However, identifying important biomarkers is a critical pursuit towards assisting researchers to establish novel hypotheses regarding prevention, diagnosis and treatment of complex human diseases. Herein, we propose a Permutation-based Feature Importance Test (PermFIT) for estimating and testing the feature importance, and for assisting interpretation of individual feature in complex frameworks, including deep neural networks, random forests, and support vector machines. PermFIT (available at https://github.com/SkadiEye/deepTL) is implemented in a computationally efficient manner, without model refitting. We conduct extensive numerical studies under various scenarios, and show that PermFIT not only yields valid statistical inference, but also improves the prediction accuracy of machine learning models. With the application to the Cancer Genome Atlas kidney tumor data and the HITChip atlas data, PermFIT demonstrates its practical usage in identifying important biomarkers and boosting model prediction performance.

scholarly journals Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 79-89
Author(s):  
Ashfaq Ali Kashif ◽  
Birra Bakhtawar ◽  
Asma Akhtar ◽  
Samia Akhtar ◽  
Nauman Aziz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatitis C ◽  
Treatment Response ◽  
Nearest Neighbor ◽  
Response Prediction ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Treatment Response Prediction

The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus (HCV) is high in Pakistan as well as all over the world. During the treatment of any disease, prediction of treatment response against any particular medicine is difficult. This paper focuses on predicting the treatment response of a drug: “L-ornithine L-Aspartate (LOLA)” in hepatitis c patients. We have used various machine learning techniques for the prediction of treatment response, including: “K Nearest Neighbor, kStar, Naive Bayes, Random Forest, Radial Basis Function, PART, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Performance measures used to analyze the performance of used machine learning techniques include, “Accuracy, Recall, Precision, and F-Measure”.

A 4-year prospective observational follow-up study of course and predictors of course in body dysmorphic disorder

2012 ◽  
Vol 43 (5) ◽  
pp. 1109-1117 ◽  
Author(s):  
K. A. Phillips ◽  
W. Menard ◽  
E. Quinn ◽  
E. R. Didie ◽  
R. L. Stout
Keyword(s):  
Mental Health Treatment ◽  
Partial Remission ◽  
Body Dysmorphic Disorder ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Cumulative Probability ◽  
Full Remission ◽  
Kaplan Meier ◽  
Dsm Iv

BackgroundThis report prospectively examines the 4-year course, and predictors of course, of body dysmorphic disorder (BDD), a common and often severe disorder. No prior studies have prospectively examined the course of BDD in individuals ascertained for BDD.MethodThe Longitudinal Interval Follow-Up Evaluation (LIFE) assessed weekly BDD symptoms and treatment received over 4 years for 166 broadly ascertained adults and adolescents with current BDD at intake. Kaplan–Meier life tables were constructed for time to remission and relapse. Full remission was defined as minimal or no BDD symptoms, and partial remission as less than full DSM-IV criteria, for at least 8 consecutive weeks. Full relapse and partial relapse were defined as meeting full BDD criteria for at least 2 consecutive weeks after attaining full or partial remission respectively. Cox proportional hazards regression examined predictors of remission and relapse.ResultsOver 4 years, the cumulative probability was 0.20 for full remission and 0.55 for full or partial remission from BDD. A lower likelihood of full or partial remission was predicted by more severe BDD symptoms at intake, longer lifetime duration of BDD, and being an adult. Among partially or fully remitted subjects, the cumulative probability was 0.42 for subsequent full relapse and 0.63 for subsequent full or partial relapse. More severe BDD at intake and earlier age at BDD onset predicted full or partial relapse. Eighty-eight percent of subjects received mental health treatment during the follow-up period.ConclusionsIn this observational study, BDD tended to be chronic. Several intake variables predicted greater chronicity of BDD.

scholarly journals Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seong-Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi Oh ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

AbstractWe aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715–0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.

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