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  string(91) "Individual Participant-Level Data, which includes Full CSR and all supporting documentation"
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  string(1606) "Background: Finding effective treatment methods for mental disorders is crucial to enhance quality of life. Precision Medicine, a method with momentous potential, involves developing personalized plans for each patient for effective treatments. Additionally, many disorders share common symptoms, making it necessary to explore symptom clusters both within and across diagnosis groups.
Objective: To build a Machine Learning (ML) model that can predict the effectiveness of common treatments across mental disorders using individual patient data; to categorize patients into symptom-clustering groups and discover similarities between disorders.
Study Design: We will train our model on clinical data to predict treatment efficacy for each drug and cross-validate it to assess how the model generalizes to an independent dataset. Using another model, we will analyze datasets across conditions to identify symptom-clustering groups.
Participants: Patients with psychotic, mood, anxiety, or disruptive behaviour disorders, epilepsy, autistic disorder, or ADHD.
Main Outcome Measure: For our predictive model, the outcome measure will be the sensitivity, specificity and predictive power relative to each specific drug; in our symptom-clustering analysis, we will test whether clusters are stable, and how treatment responsiveness differs among clusters.
Statistical Analysis: The relationship between clinical data and treatment outcomes will be examined using ML regression analysis. Clusters of similar symptom profiles will be identified with pattern recognition techniques." ["project_brief_bg"]=> string(2972) "Mental disorders significantly harm a person's quality of life because they can lead to detrimental physiological and psychological states. Therefore, finding efficient treatments is paramount to improving mental health. An approach with significant potential is Precision Medicine (Lu, Fizbein & Opfer, 1987), which involves creating individualized medical plans for each patient with pertinent drugs identified at an early stage. Currently, identifying the most appropriate treatment for a patient often involves a costly process of trial and error (costly in terms of time, money and health); individualized treatment plans offer a potential solution that would greatly improve this process. Recent research, such as the study by Chekroud et al (2016), shows that statistical models constructed from clinical data can enable the prediction of a patient?s drug response. In this project, we will use clinical data to build a Machine Learning model to predict the effectiveness (e.g. improvement of symptoms as measured with clinical assessments, time until relapse or incidence of adverse events) of various treatments for individual patients, which could provide a promising method for future personalized treatment plans.
Many mental disorders share similar symptoms. For example, Major Depressive Disorder and Generalized Anxiety Disorder both involve restlessness and a lack of concentration (Zbozinek et al, 2012). It is important to analyze symptom clusters that may have previously been thought of as belonging to distinct disorders in order to develop wide-ranging treatments. A paper by Chekroud et al (2017), demonstrated that the researchers were able to cluster empirically defined symptoms into groups with different responsiveness to treatments, both within and across antidepressant medications. In our project, we will create a ML model to categorize patients into symptom-clustering groups and both within and across disorders. Our investigation into similarities between disorders will contribute to discovering potential connections between mental illnesses.
This project will materially enhance our scientific knowledge of treatment efficacy and different outcomes across the treatment and placebo groups for specific medications (such as Risperidone, Paliperidone, or Topiramate). It will also provide more information about symptom clusters across the disorders that these medications are prescribed for. In terms of this project's relevance to public health, it will contribute to the growing efficiency, personalization, and precision of treatment applications and the potential use of treatments for multiple disorders. We recognize that this project is ambitious, nevertheless, collecting data about several mental disorders can allow us to evaluate the use of ML techniques for finding novel clusters, which could potentially alter their categorization in the future. Therefore, this project acts as a proof-of-concept pilot study." ["project_specific_aims"]=> string(964) "The first aim of this project is to construct least absolute shrinkage and selection operator (LASSO) regression models from individual patient clinical data. These models will provide identification of patients who will or will not respond positively based on clinical measures (such as YMRS in Bipolar Disorder (Young et al, 1978) and PANSS in Schizophrenia (Kay, Fizbein & Opfer, 1987)) to specific treatments for multiple mental disorders as compared to placebo.
The second aim of this project is to build a k-nearest neighbour (KNN) ML model that clusters symptoms across all mental disorders which will be visualised using t-Distributed Stochastic Neighbour Embedding (t-SNE) in order to identify possible novel associations across clinical categories.
We hypothesize that our models will reliably predict treatment outcomes and that in investigating symptom-clustering groups we will discover novel similarities across clinical classifications." ["project_study_design"]=> string(0) "" ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(5) { [0]=> array(2) { ["value"]=> string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations" ["label"]=> string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations" } [1]=> array(2) { ["value"]=> string(36) "Participant-level data meta-analysis" ["label"]=> string(36) "Participant-level data meta-analysis" } [2]=> array(2) { ["value"]=> string(66) "Summary-level data meta-analysis using only data from YODA Project" ["label"]=> string(66) "Summary-level data meta-analysis using only data from YODA Project" } [3]=> array(2) { ["value"]=> string(28) "Research on comparison group" ["label"]=> string(28) "Research on comparison group" } [4]=> array(2) { ["value"]=> string(50) "Research on clinical prediction or risk prediction" ["label"]=> string(50) "Research on clinical prediction or risk prediction" } } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> array(2) { ["value"]=> string(7) "RStudio" ["label"]=> string(7) "RStudio" } ["project_software_used_exp"]=> string(0) "" ["project_research_methods"]=> string(751) "Data Source: All available digital data (clinical, biochemical, cognitive, sociodemographic, etc.) from phase 3 and 4 randomized and/or open-label datasets for Schizophrenia, Schizoaffective Disorder, Psychosis, Bipolar Disorder, Major Depressive Disorder, Attention Deficit Hyperactivity Disorder, Disruptive Behaviour Disorders, Anxiety Disorders, Autistic Spectrum Disorder, and Epilepsy (as a reference).
Exclusion Criteria: In order to develop an ML model with strong predictive power, the model must be trained on a large dataset; the more variance captured in the clinical data, the more accurate our models will be at predicting the treatment effectiveness of unseen patients. We will, for this reason, include all patients in our study." ["project_main_outcome_measure"]=> string(709) "We will measure the sensitivity, specificity and predictive power of our ML models in predicting treatment effectiveness, relative to each specific drug through a cross-validation method (described below in the Statistical Analysis section). Our measure of treatment effectiveness will depend on the primary endpoints of the trials, with examples including:
? Time until remission (days)
? Change from baseline (using a clinically relevant measure such as PANSS (Kay, Fizbein & Opfer, 1987), or YMRS (Young et al, 1978))
? Survival (yes/no)
? High vs. low quality of life scores (such as the Short Form-36 (Ware & Sherbourne, 1992) or WHOQOL-BREF (Skevington, Lofty, & O?Connel, 2004))" ["project_main_predictor_indep"]=> string(1168) "For each of our ML models, our main independent variable will be treatment allocation and will be defined as a binary dummy variable.
Our predictor variables of interest are all digitally archived information, which includes patient profile characteristics such as variables of the demographic, clinical, cognitive, genetic, lab-test, and free-text survey information, as well as the characteristics of the trials, such as when and where the trial was performed, the number of subjects and the intervention used. The more moderating variables available in our data, the better our model will be able to make personalized predictions. The following list just provides a few potential example predictor variables that could be included in our analysis:
? Age (years)
? Sex (male/female/intersex)
? Race (Caucasian, African American, etc.)
? BMI (continuous)
? Smoker (yes/no)
? Time since diagnosis (years)
? Previous treatments (yes/no, name, dose)
? Additional Diseases / Comorbidities (yes/no, name)
? Measures of psychopathology (for example, YMRS)
? Relapse occurrence/time to relapse (yes/no, days)" ["project_other_variables_interest"]=> string(33) "All digitally archived variables." ["project_stat_analysis_plan"]=> string(2329) "? A descriptive analysis of all demographic, clinical, and pharmaceutical characteristics of participants. ANOVA and chi-square tests will be conducted to determine whether the distribution of continuous and categorical factors, respectively, are distributed equally among patients. Results will be displayed as the median and interquartile range (IQR) for continuous variables and as number and percentage frequency for categorical variables.
Aim One:
? All ML models will be developed and appraised with k-fold cross-validation (Pedregosa et al, 2011), partitioning the entirety of the relevant constructed dataset into k disjoint subsets, with the model trained on k-1 of the subsets and the model?s predictive power tested on the remaining subset.
? The least absolute shrinkage and selection operator (LASSO) regression analysis method will be performed to determine the relationship between patient profile and treatment outcome. LASSO regression can obtain the subset of predictors that minimizes prediction error for a quantitative response variable, which, for our project, will be the measure of the treatment outcome relative to each disorder (e.g. time until remission) (Santosa & Symes, 1986).
Aim Two:
? A descriptive analysis of all demographic, clinical, and pharmaceutical characteristics of participants will be undertaken, with ANOVA and chi-square tests conducted to determine whether the distribution of continuous and categorical factors, respectively, are distributed equally among patients. Results will be displayed as the median and interquartile range (IQR) for continuous variables and as number and percentage frequency for categorical variables.
? k-nearest neighbours algorithm (KNN) will be used to cluster patient profiles across all conditions of interest to discover previously unidentified symptom-clustering across mental disorders. KNN is a non-parametric pattern recognition method that can assign each patient profile to a particular cluster or group based on similarities across all patients and mental disorders (Altman, 1992).
? t-Distributed Stochastic Neighbour Embedding (t-SNE), an ML algorithm for dimensionality reduction, will be used to visualize the symptom-clustering groups in a low-dimensional space (van der Maaten & Hinton, 2008)." ["project_timeline"]=> string(210) "Project start date: August 2019
Initial Analysis completion date: May 2020
Manuscript Drafted: June 2020
Manuscript submitted for publication: July 2020
Report back to YODA: August 2020" ["project_dissemination_plan"]=> string(500) "We anticipate the generation of at least two manuscripts from this project on our models' ability to predict treatment outcomes and recluster patient profiles. The target audience would be physicians as well as psychiatry and pharmacology researchers. Potentially suitable journals for these manuscripts include Neuropsychopharmacology, Journal of Psychiatric Research, The Canadian Journal of Psychiatry, JAMA Psychiatry, Journal of Machine Learning Research and Artificial Intelligence in Medicine." ["project_bibliography"]=> string(3082) "

Altman, N. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 46(3), pp. 175 ? 185. doi:10.1080/00031305.1992.10475879. hdl:1813/31637
Chekroud, A., Gueorguieva, R., Krumholz, H., Trivedi, M., Krystal, J., & McCarthy, G. (2017). Reevaluating the efficacy and predictability of antidepressant treatments: A system Clustering Approach. JAMA Psychiatry 74(4), pp. 370-378. doi:10.1001/jamapsychiatry.2017.0025
Chekroud, A., Zotti, R., Shehzad, Z., Gueorguieva, R., Johnson, M., Trivedi, M., . . . Corlett, P. (2016). Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3, pp. 243-250. doi: https://doi.org/10.1016/S2215-0366(15)00471-X
Kay, S., Fizbein, A., & Opfer, L. (1987). The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Schizophrenia Bulletin, 13(2), 261-276. Retrieved from http://dx.doi.org/10.1093/schbul/13.2.261
Lu, Y., Goldstein, D., Angrist, M., & Cavalleri, G. (2014). Personalized Medicine and Human Genetic Diversity. Cold Spring Harbor Perspectives in Medicine, 4(9). doi:10.1101/cshperspect.a008581
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, O., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. JMLR, 12, pp. 2825-2830. Retrieved from http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
Santosa, F., & Symes, W. (1986). Linear inversion of band-limited reflection seismograms. SIAM Journal on Scientific and Statistical Computing. 7(4), pp. 1307?1330. doi:10.1137/0907087
Skevington, S., Lofty, M., & O?Connell, K. (2004). The World Health Organization?s WHOQOL-BREF quality of life assessment: Psychometric properties and results of the international field trial. Quality of Life Research, 13, pp. 299-310. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.463.2578&rep=re…
van der Maaten, L., & Hinton, G. (2008). Visualizing Data Using t-SNE. Journal of Machine Learning Research. 9, pp. 2579?2605. Retrieved from http://jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
Ware, J., & Sherbourne, C. (1992). The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30(6), pp. 473-483. Retrieved from https://pdfs.semanticscholar.org/06cb/0076e310136d0ca8b56cc8585ec2bf43e0…
Young, R., Biggs, J., Ziegler, V., & Meyer, D. (1978). A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry. 133(5), pp. 429-35. doi:10.1192/bjp.133.5.429
Zbozinek, T., Rose, R., Wolitzky-Taylor, K., Sherbourne, C., Sullivan, G., Stein, M., Roy-Burne, P., & Craske, M. (2012). Diagnostic overlap of generalized anxiety disorder in a primary care sample. Depression & Anxiety, 29(12), 1065-1071. doi:1002/da.22026

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"https://yoda.yale.edu/wp/wp-includes/images/media/document.png" } ["project_highlight_button"]=> string(0) "" ["search_order"]=> string(5) "-6980" } data partner
array(1) { [0]=> string(15) "johnson-johnson" }

pi country
array(1) { [0]=> string(6) "Canada" }

pi affil
array(1) { [0]=> string(8) "Academia" }

products
array(7) { [0]=> string(8) "concerta" [1]=> string(6) "invega" [2]=> string(15) "invega-sustenna" [3]=> string(16) "risperdal-consta" [4]=> string(9) "risperdal" [5]=> string(7) "topamax" [6]=> string(8) "spravato" }

num of trials
array(1) { [0]=> string(3) "117" }

res
array(1) { [0]=> string(1) "3" }

2019-3958

General Information

How did you learn about the YODA Project?: Data Holder (Company)

Conflict of Interest

Request Clinical Trials

Associated Trial(s):
  1. NCT00307684 - An Open International Multicentre Long-Term Follow Up Study to Evaluate Safety of Prolonged Release OROS Methlyphenidate in Adults With Attention Deficit Hyperactivity Disorder
  2. NCT00326300 - An Open-Label, Dose-Titration, Long-Term Safety Study to Evaluate CONCERTA (Methylphenidate HCL) Extended-release Tablets at Doses of 36 mg, 54 mg, 72 mg, 90 mg, and 108 mg Per Day in Adults With Attention Deficit Hyperactivity Disorder
  3. NCT00246220 - A Multicentre, Randomised, Double-Blind, Placebo-Controlled, Parallel Group, Dose-Response Study To Evaluate the Safety And Efficacy Of Prolonged Release OROS Methylphenidate Hydrochloride (18, 36 and 72 mg/Day), With Open-Label Extension, In Adults With Attention Deficit/Hyperactivity Disorder
  4. NCT00488319 - A 2-Year, Open-Label, Single-Arm Safety Study of Flexibly Dosed Paliperidone Extended Release (1.5-12 mg/day) in the Treatment of Adolescents (12 to 17 Years of Age) With Schizophrenia
  5. NCT01009047 - A Randomized, Multicenter, Double-Blind, Active-Controlled, Flexible-Dose, Parallel-Group Study of the Efficacy and Safety of Prolonged Release Paliperidone for the Treatment of Symptoms of Schizophrenia in Adolescent Subjects, 12 to 17 Years of Age
  6. NCT00645099 - A Prospective Randomized Open-label 6-Month Head-To-Head Trial to Compare Metabolic Effects of Paliperidone ER and Olanzapine in Subjects With Schizophrenia
  7. NCT00518323 - A Randomized, Multicenter, Double-Blind, Weight-Based, Fixed-Dose, Parallel-Group, Placebo-Controlled Study of the Efficacy and Safety of Extended Release Paliperidone for the Treatment of Schizophrenia in Adolescent Subjects, 12 to 17 Years of Age
  8. NCT01606228 - An Open-Label Prospective Trial to Explore the Tolerability, Safety and Efficacy of Flexibly-Dosed Paliperidone ER among Treatment-Naive and Newly Diagnosed Patients with Schizophrenia
  9. NCT00334126 - A Randomized, Double-blind, Placebo-controlled, Parallel Group Study to Evaluate the Efficacy and Safety of Paliperidone ER Compared to Quetiapine in Subjects With an Acute Exacerbation of Schizophrenia
  10. NCT00086320 - A Randomized, Double-blind, Placebo-controlled, Parallel-group Study With an Open-label Extension Evaluating Paliperidone Extended Release Tablets in the Prevention of Recurrence in Subjects With Schizophrenia
  11. NCT00589914 - A Randomized, Double-Blind, Parallel-Group, Comparative Study of Flexible Doses of Paliperidone Palmitate and Flexible Doses of Risperidone Long-Acting Intramuscular Injection in Subjects With Schizophrenia
  12. NCT00604279 - A Randomized, Open-Label, Parallel Group Comparative Study of Paliperidone Palmitate (50, 100, 150 mg eq) and Risperidone LAI (25, 37.5, or 50 mg) in Subjects with Schizophrenia
  13. NCT00590577 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose Response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (25 mg eq., 100 mg eq., and 150 mg eq.) of Paliperidone Palmitate in Subjects With Schizophrenia
  14. NCT00111189 - A Randomized Double-blind Placebo-controlled Parallel Group Study Evaluating Paliperidone Palmitate in the Prevention of Recurrence in Patients With Schizophrenia. Placebo Consists of 20% Intralipid (200 mg/mL) Injectable Emulsion
  15. NCT00210717 - A Randomized, Double-Blind, Parallel Group, Comparative Study of Flexibly Dosed Paliperidone Palmitate (25, 50, 75, or 100 mg eq.) Administered Every 4 Weeks and Flexibly Dosed RISPERDAL CONSTA (25, 37.5, or 50 mg) Administered Every 2 Weeks in Subjects With Schizophrenia
  16. NCT00119756 - A Randomized, Crossover Study to Evaluate the Overall Safety and Tolerability of Paliperidone Palmitate Injected in the Deltoid or Gluteus Muscle in Patients With Schizophrenia
  17. NCT00210548 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose-Response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (50 mg eq., 100 mg eq., and 150 mg eq.) of Paliperidone Palmitate in Subjects With Schizophrenia
  18. NCT00101634 - A Randomized, Double-blind, Placebo-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Doses (25 mg eq, 50 mg eq, and 100 mg eq) of Paliperidone Palmitate in Patients With Schizophrenia
  19. NCT00391222 - A Randomized, Double Blind, Placebo and Active Controlled Parallel Group Study to Evaluate the Efficacy and Safety of Risperidone Long-acting Injectable (LAI) for the Prevention of Mood Episodes in the Treatment of Subjects With Bipolar I Disorder
  20. NCT00034749 - The Efficacy and Safety of Risperidone in Adolescents With Schizophrenia: a Comparison of Two Dose Ranges of Risperidone
  21. NCT00076115 - Research on the Effectiveness of Risperidone in Bipolar Disorder in Adolescents and Children (REACH): A Double-Blind, Randomized, Placebo-Controlled Study of the Efficacy and Safety of Risperidone for the Treatment of Acute Mania in Bipolar I Disorder
  22. NCT00132678 - A Randomized, Double-blind, Placebo-controlled Study to Explore the Efficacy and Safety of Risperidone Long-acting Intramuscular Injectable in the Prevention of Mood Episodes in Bipolar 1 Disorder, With Open-label Extension
  23. NCT00094926 - A Prospective, Randomized, Double-blind, Placebo-controlled Study of the Effectiveness and Safety of RISPERDAL CONSTA Augmentation in Adult Patients With Frequently-relapsing Bipolar Disorder
  24. NCT00210782 - A Double-blind Trial Comparing the Efficacy, Tolerability and Safety of Monotherapy Topiramate Versus Phenytoin in Subjects With Seizures Indicative of New Onset Epilepsy
  25. NCT00714688 - A Multicentre, Randomized, Double-Blind, Placebo-Controlled, Parallel Group, Dose-Response Study to Evaluate Efficacy and Safety of Prolonged Release (PR) OROS Methylphenidate (54 and 72 mg/Day) in Adults With Attention Deficit/Hyperactivity Disorder
  26. NCT00866996 - A Multi-center Randomized Parallel Group Study Evaluating Treatment Outcomes of Concerta (Extended Release Methylphenidate) and Strattera (Atomoxetine) in Children With Attention-deficit/Hyperactivity Disorder
  27. NCT00269815 - Long-term Safety and Effectiveness of OROS (Methylphenidate HCl) in Children With ADHD
  28. NCT00397033 - A Randomized, Double-blind, Placebo-controlled, Parallel-group Study to Evaluate the Efficacy and Safety of Two Dosages of Paliperidone ER in the Treatment of Patients With Schizoaffective Disorder
  29. NCT00412373 - A Randomized, Double-blind, Placebo-controlled, Parallel- Group Study to Evaluate the Efficacy and Safety of Flexible-dose Paliperidone ER in the Treatment of Patients With Schizoaffective Disorder
  30. NCT00236444 - Risperidone in the Prevention of Relapse: a Randomized, Double-blind, Placebo-controlled Trial in Children and Adolescents With Conduct and Other Disruptive Behavior Disorders
  31. NCT00236470 - Risperidone in the Treatment of Children and Adolescents With Conduct and Other Disruptive Behavior Disorders - an Open Label Follow-up Trial of CR002020
  32. NCT00250354 - The Safety And Efficacy Of Risperidone Versus Placebo In Conduct Disorder In Mild, Moderate And Borderline Mentally Retarded Children Aged 5 To 12 Years
  33. NCT00266552 - The Safety And Efficacy Of Risperidone Versus Placebo In Conduct Disorder and Other Disruptive Behavior Disorders In Mild, Moderate And Borderline Mentally Retarded Children Aged 5 To 12 Years
  34. NCT00237289 - Topiramate Versus Placebo as add-on Treatment in Patients With Bipolar Disorder in the Outpatient Setting
  35. NCT00240721 - A Randomized, Double-Blind, Multicenter, Placebo-Controlled 12-Week Study Of The Safety And Efficacy Of Two Doses Of Topiramate For The Treatment Of Acute Manic Or Mixed Episodes In Subjects With Bipolar I Disorder With An Optional Open-Label Extension
  36. NCT00037674 - A Randomized, Double-Blind, Multicenter, Placebo-Controlled 12-Week Study of the Safety and Efficacy of Two Doses of Topiramate for the Treatment of Acute Manic or Mixed Episodes in Patients With Bipolar I Disorder With an Optional Open-Label Extension
  37. NCT00035230 - A Randomized, Double-Blind, Multicenter, Placebo-Controlled 12-Week Study of the Safety and Efficacy of Topiramate in Patients With Acute Manic or Mixed Episodes of Bipolar I Disorder With an Optional Open-Label Extension
  38. A Randomized, Double-Blind, Multicenter, Placebo-Controlled, 21-Day Study of the Safety and Efficacy of Topiramate for the Treatment of Acute Manic or Mixed Episodes in Subjects With Bipolar I Disorder With an Optional Open-Label Extension
  39. NCT00799409 - The ABC Study: A Double-Blind, Randomized, Placebo-Controlled, Crossover Study Evaluating the Academic, Behavioral, and Cognitive Effects of CONCERTA on Older Children With ADHD
  40. NCT00799487 - Double-Blind, Randomized, Placebo-Controlled, Crossover Study Evaluating the Academic, Behavioral and Cognitive Effects of CONCERTA on Older Children With ADHD (The ABC Study)
  41. NCT00249132 - A Canadian multicenter placebo-controlled study of fixed doses of risperidone and haloperidol in the treatment of chronic schizophrenic patients
  42. NCT00216476 - CONSTATRE: Risperdal® Consta® Trial of Relapse Prevention and Effectiveness
  43. NCT00216580 - An Open-label Trial of Risperidone Long-acting Injectable in the Treatment of Subjects With Recent Onset Psychosis
  44. NCT00253162 - The Efficacy And Safety Of Flexible Dose Ranges Of Risperidone Versus Placebo Or Haloperidol In The Treatment Of Manic Episodes Associated With Bipolar I Disorder
  45. NCT00378092 - A Prospective Study of the Clinical Outcome Following Treatment Discontinuation After Remission in First-Episode Schizophrenia
  46. NCT00299715 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Dose-Response, Multicenter Study to Evaluate the Efficacy and Safety of Three Fixed Doses of Extended-Release Paliperidone in the Treatment of Subjects With Acute Manic and Mixed Episodes Associated With Bipolar I Disorder
  47. NCT00309699 - A Randomized, Double-Blind, Active- and Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy and Safety of Flexibly-Dosed, Extended-Release Paliperidone Compared With Flexibly-Dosed Quetiapine and Placebo in the Treatment of Acute Manic and Mixed Episodes Associated With Bipolar I Disorder
  48. NCT00309686 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Multicenter Study to Evaluate the Efficacy and Safety of Flexibly-Dosed Extended-Release Paliperidone as Adjunctive Therapy to Mood Stabilizers in the Treatment of Acute Manic and Mixed Episodes Associated With Bipolar I Disorder
  49. NCT00752427 - 24 week extension of NCT00085748: A Randomized, 6-Week Double-Blind, Placebo-Controlled Study With an Optional 24-Week Open-Label Extension to Evaluate the Safety and Tolerability of Flexible Doses of Paliperidone Extended Release in the Treatment of Geriatric Patients With Schizophrenia
  50. NCT00077714 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 2 Fixed Dosages of Paliperidone Extended Release Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia
  51. NCT00083668 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Dosages of Paliperidone Extended Release (ER) Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia
  52. NCT00074477 - A Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of 50 and 100 Mg-eq of Paliperidone Palmitate in Patients With Schizophrenia
  53. NCT00078039 - Trial Evaluating Three Fixed Dosages of Paliperidone Extended-Release (ER) Tablets and Olanzapine in the Treatment of Patients With Schizophrenia
  54. NCT00085748 - A Randomized, 6-Week Double-Blind, Placebo-Controlled Study With an Optional 24-Week Open-Label Extension to Evaluate the Safety and Tolerability of Flexible Doses of Paliperidone Extended Release in the Treatment of Geriatric Patients With Schizophrenia
  55. NCT00937040 - A Placebo Controlled Double-Blind, Parallel Group, Individualizing Dosing Study Optimizing Treatment of Adults With Attention Deficit Hyperactivity Disorder to an Effective Response With OROS Methylphenidate
  56. NCT00261508 - Efficacy And Safety Of Risperidone In The Treatment Of Children With Autistic Disorder And Other Pervasive Developmental Disorders: A Canadian, Multicenter, Double-Blind, Placebo-Controlled Study
  57. NCT00249236 - The Efficacy And Safety Of Flexible Dosage Ranges Of Risperidone Versus Placebo In The Treatment Of Manic Or Mixed Episodes Associated With Bipolar I Disorder
  58. NCT00250367 - The Safety And Efficacy Of Risperdal (Risperidone) Versus Placebo As Add-On Therapy To Mood Stabilizers In The Treatment Of The Manic Phase Of Bipolar Disorder
  59. NCT00088075 - A Randomized, Double-Blind, Placebo-Controlled Clinical Study of the Efficacy and Safety of Risperidone for the Treatment of Schizophrenia in Adolescents
  60. Risperidone versus haloperidol versus placebo in the treatment of schizophrenia
  61. NCT00253149 - The Safety And Efficacy Of Risperdal (Risperidone) Versus Placebo Versus Haloperidol As Add-On Therapy To Mood Stabilizers In The Treatment Of The Manic Phase Of Bipolar Disorder
  62. NCT00253136 - Risperidone Depot (Microspheres) vs. Placebo in the Treatment of Subjects With Schizophrenia
  63. A double-blind, placebo-controlled study of risperidone in children and adolescents with autistic disorder
  64. NCT00257075 - The Efficacy And Safety Of Flexible Dosage Ranges Of Risperidone Versus Placebo In The Treatment Of Manic Episodes Associated With Bipolar I Disorder
  65. The efficacy and safety of flexible dose ranges of risperidone vs. Placebo or divalproex sodium in the treatment of manic or mixed episodes associated with bipolar 1 disorder
  66. The safety and efficacy of risperidone 8 mg qd and 4 mg qd compared to placebo in the treatment of schizophrenia
  67. NCT01529515 - A Randomized, Multicenter, Double-Blind, Relapse Prevention Study of Paliperidone Palmitate 3 Month Formulation for the Treatment of Subjects With Schizophrenia
  68. NCT01193153 - A Randomized, Double-Blind, Placebo-Controlled, Parellel-Group Study of Paliperidone Palmitate Evaluating Time to Relapse in Subjects With Schizoaffective Disorder
  69. NCT01662310 - Paliperidone Extended Release Tablets for the Prevention of Relapse in Subjects With Schizophrenia: A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Study
  70. NCT00490971 - A Randomized, Double-Blind, Active- and Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Efficacy and Safety of Extended-Release Paliperidone as Maintenance Treatment After an Acute Manic or Mixed Episode Associated With Bipolar I Disorder
  71. NCT00524043 - A Randomized, Double-Blind, Placebo- and Active-Controlled, Parallel-Group Study to Evaluate the Efficacy and Safety of a Fixed Dosage of 1.5 mg/Day of Paliperidone Extended Release (ER) in the Treatment of Subjects With Schizophrenia
  72. NCT00105326 - A Double-blind, Placebo-controlled, Randomized Study Evaluating the Effect of Paliperidone ER Compared With Placebo on Sleep Architecture in Subjects With Schizophrenia
  73. A double-blind, randomized trial of topiramate as adjunctive therapy for partial-onset seizures in children
  74. NCT00326391 - A Placebo-Controlled, Double-Blind, Parallel-Group, Dose-Titration Study to Evaluate the Efficacy and Safety of CONCERTA (Methylphenidate HCl) Extended-release Tablets in Adults With Attention Deficit Hyperactivity Disorder at Doses of 36 mg, 54 mg, 72 mg, 90 mg, or 108 mg Per Day
  75. NCT01323192 - A Double-blind, Placebo-controlled, Parallel-Group Study to Evaluate the Efficacy and Safety of JNS001 in Adults With Attention-Deficit/Hyperactivity Disorder at Doses of 18 mg, 36 mg, 54 mg, or 72 mg Per Day
  76. NCT00246246 - A Randomized, Open-label Trial of RISPERDAL® CONSTA™ Versus Oral Antipsychotic Care in Subjects With Bipolar Disorder
  77. NCT00044681 - A Study to Evaluate the Efficacy, Safety and Maintenance Effect of Risperidone Augmentation of SSRI Monotherapy in Young and Older Adult Patients With Unipolar Treatment-Resistant Depression
  78. NCT00249223 - Risperidone Depot (Microspheres) vs. Risperidone Tablets - a Non-inferiority, Efficacy Trial in Subjects With Schizophrenia
  79. NCT01157351 - A Fifteen-month, Prospective, Randomized, Active-controlled, Open-label, Flexible Dose Study of Paliperidone Palmitate Compared With Oral Antipsychotic Treatment in Delaying Time to Treatment Failure in Adults With Schizophrenia Who Have Been Incarcerated
  80. NCT01081769 - A 24-month, Prospective, Randomized, Active-Controlled, Open-Label, Rater-Blinded, Multicenter, International Study of the Prevention of Relapse Comparing Long-Acting Injectable Paliperidone Palmitate to Treatment as Usual With Oral Antipsychotic Monotherapy in Adults With Schizophrenia
  81. NCT01281527 - A 6-month, Open Label, Prospective, Multicenter, International, Exploratory Study of a Transition to Flexibly Dosed Paliperidone Palmitate in Patients With Schizophrenia Previously Unsuccessfully Treated With Oral or Long-acting Injectable Antipsychotics
  82. NCT01051531 - Safety, Tolerability, and Treatment Response of Paliperidone Palmitate in Subjects With Schizophrenia When Switching From Oral Antipsychotics
  83. NCT01527305 - An Open-Label, Prospective, Non-Comparative Study to Evaluate the Efficacy and Safety of Paliperidone Palmitate in Subjects With Acute Schizophrenia
  84. NCT01299389 - A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Fixed-Dose, Multicenter Study of JNS010 (Paliperidone Palmitate) in Patients With Schizophrenia
  85. NCT01258920 - A Long-Term, Open-Label Study of Flexibly Dosed Paliperidone Palmitate Long-Acting Intramuscular Injection in Japanese Patients With Schizophrenia
  86. NCT00216671 - Early Versus Late Initiation of Treatment With Risperdal Consta in Subjects With Schizophrenia After an Acute Episode
  87. NCT00369239 - Is Premorbid Functioning a Predictor of Outcome in Patients With Early Onset Psychosis Treated With Risperdal Consta?
  88. NCT00216632 - Treatment Success in Patients Requiring Treatment Change From Olanzapine to Risperidone Long Acting Injectable (TRESOR)
  89. NCT00236379 - A Six-month, Double-blind, Randomized, International, Multicenter Trial to Evaluate the Glucoregulatory Effects of Risperidone and Olanzapine in Subjects With Schizophrenia or Schizoaffective Disorder
  90. NCT00576732 - Risperidone in the Treatment of Children and Adolescents With Autistic Disorder: A Double-Blind, Placebo-Controlled Study of Efficacy and Safety, Followed by an Open-Label Extension Study of Safety
  91. NCT01050582 - Evaluation of Growth, Sexual Maturation, and Prolactin-Related Adverse Events in the Pediatric Population Exposed to Atypical Antipsychotic Drugs
  92. NCT00086112 - A Double-blind, Randomized, Prospective Study to Evaluate Adjunctive Risperidone Versus Adjunctive Placebo in Generalized Anxiety Disorder Sub-optimally Responsive to Standard Psychotropic Therapy
  93. NCT00216528 - A Prospective, Open-Label Study to Evaluate Symptomatic Remission in Schizophrenia With Long Acting Risperidone Microspheres (Risperdal Consta)
  94. NCT00269919 - Effect on Efficacy, Safety and Quality of Life by Long-Term Treatment of Long-Acting Risperidone Microspheres in Patients With Schizophrenia
  95. NCT00992407 - A Randomized, Open-label, Active-controlled Study to Evaluate Social Functioning of Long Acting Injectable Risperidone and Oral Risperidone in the Treatment of Subjects With Schizophrenia or Schizoaffective Disorder
  96. NCT00236353 - An Open-label Study of the Efficacy and Safety of RISPERDAL Long-acting Microspheres (RISPERDAL CONSTA) Administered Once Monthly in Adults With Schizophrenia or Schizoaffective Disorder
  97. NCT00495118 - Risperidone Depot (Microspheres) in the Treatment of Subjects With Schizophrenia or Schizoaffective Disorder - an Open-label Follow-up Trial of RIS-INT-62 and RIS-INT-85
  98. NCT01855074 - Evaluation of Efficacy and Safety of Risperidone in Long-acting Microspheres in Patients With Schizophrenia, Schizophreniform or Schizoaffective Disorders Diagnosed According to the DSM-IV Criteria, After Switching Treatment With Any Antipsychotic Therapy With Long-acting Microspheres of Risperidone
  99. NCT00236457 - Randomized, Multi-center, Open Label Trial Comparing Risperidone Depot (Microspheres) and Olanzapine Tablets in Patients With Schizophrenia or Schizoaffective Disorder
  100. NCT00236587 - An Open Label, Long Term Trial of Risperidone Long Acting Microspheres in the Treatment of Patients Diagnosed With Schizophrenia
  101. NCT00297388 - A 52-wk Prospective, Randomized, Double-blind, Multicenter Study of Relapse Following Transition From Oral Antipsychotic Medication to 2 Different Doses (25 or 50 mg Every 2 Wks) of Risperidone Long-acting Microspheres (RISPERDAL CONSTA) in Adults With Schizophrenia or Schizoaffective Disorder
  102. NCT00821600 - Single-Dose, Open-Label Pilot Study to Explore the Pharmacokinetics, Safety and Tolerability of a Gluteal Intramuscular Injection of a 4-Week Long-Acting Injectable Formulation of Risperidone in Patients With Chronic Stable Schizophrenia
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Research Proposal

Project Title: Machine Learning for predicting treatment efficacy and clinical categorizations across mental health disorders.

Scientific Abstract: Background: Finding effective treatment methods for mental disorders is crucial to enhance quality of life. Precision Medicine, a method with momentous potential, involves developing personalized plans for each patient for effective treatments. Additionally, many disorders share common symptoms, making it necessary to explore symptom clusters both within and across diagnosis groups.
Objective: To build a Machine Learning (ML) model that can predict the effectiveness of common treatments across mental disorders using individual patient data; to categorize patients into symptom-clustering groups and discover similarities between disorders.
Study Design: We will train our model on clinical data to predict treatment efficacy for each drug and cross-validate it to assess how the model generalizes to an independent dataset. Using another model, we will analyze datasets across conditions to identify symptom-clustering groups.
Participants: Patients with psychotic, mood, anxiety, or disruptive behaviour disorders, epilepsy, autistic disorder, or ADHD.
Main Outcome Measure: For our predictive model, the outcome measure will be the sensitivity, specificity and predictive power relative to each specific drug; in our symptom-clustering analysis, we will test whether clusters are stable, and how treatment responsiveness differs among clusters.
Statistical Analysis: The relationship between clinical data and treatment outcomes will be examined using ML regression analysis. Clusters of similar symptom profiles will be identified with pattern recognition techniques.

Brief Project Background and Statement of Project Significance: Mental disorders significantly harm a person's quality of life because they can lead to detrimental physiological and psychological states. Therefore, finding efficient treatments is paramount to improving mental health. An approach with significant potential is Precision Medicine (Lu, Fizbein & Opfer, 1987), which involves creating individualized medical plans for each patient with pertinent drugs identified at an early stage. Currently, identifying the most appropriate treatment for a patient often involves a costly process of trial and error (costly in terms of time, money and health); individualized treatment plans offer a potential solution that would greatly improve this process. Recent research, such as the study by Chekroud et al (2016), shows that statistical models constructed from clinical data can enable the prediction of a patient?s drug response. In this project, we will use clinical data to build a Machine Learning model to predict the effectiveness (e.g. improvement of symptoms as measured with clinical assessments, time until relapse or incidence of adverse events) of various treatments for individual patients, which could provide a promising method for future personalized treatment plans.
Many mental disorders share similar symptoms. For example, Major Depressive Disorder and Generalized Anxiety Disorder both involve restlessness and a lack of concentration (Zbozinek et al, 2012). It is important to analyze symptom clusters that may have previously been thought of as belonging to distinct disorders in order to develop wide-ranging treatments. A paper by Chekroud et al (2017), demonstrated that the researchers were able to cluster empirically defined symptoms into groups with different responsiveness to treatments, both within and across antidepressant medications. In our project, we will create a ML model to categorize patients into symptom-clustering groups and both within and across disorders. Our investigation into similarities between disorders will contribute to discovering potential connections between mental illnesses.
This project will materially enhance our scientific knowledge of treatment efficacy and different outcomes across the treatment and placebo groups for specific medications (such as Risperidone, Paliperidone, or Topiramate). It will also provide more information about symptom clusters across the disorders that these medications are prescribed for. In terms of this project's relevance to public health, it will contribute to the growing efficiency, personalization, and precision of treatment applications and the potential use of treatments for multiple disorders. We recognize that this project is ambitious, nevertheless, collecting data about several mental disorders can allow us to evaluate the use of ML techniques for finding novel clusters, which could potentially alter their categorization in the future. Therefore, this project acts as a proof-of-concept pilot study.

Specific Aims of the Project: The first aim of this project is to construct least absolute shrinkage and selection operator (LASSO) regression models from individual patient clinical data. These models will provide identification of patients who will or will not respond positively based on clinical measures (such as YMRS in Bipolar Disorder (Young et al, 1978) and PANSS in Schizophrenia (Kay, Fizbein & Opfer, 1987)) to specific treatments for multiple mental disorders as compared to placebo.
The second aim of this project is to build a k-nearest neighbour (KNN) ML model that clusters symptoms across all mental disorders which will be visualised using t-Distributed Stochastic Neighbour Embedding (t-SNE) in order to identify possible novel associations across clinical categories.
We hypothesize that our models will reliably predict treatment outcomes and that in investigating symptom-clustering groups we will discover novel similarities across clinical classifications.

Study Design:

What is the purpose of the analysis being proposed? Please select all that apply.: New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations Participant-level data meta-analysis Summary-level data meta-analysis using only data from YODA Project Research on comparison group Research on clinical prediction or risk prediction

Software Used: RStudio

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: Data Source: All available digital data (clinical, biochemical, cognitive, sociodemographic, etc.) from phase 3 and 4 randomized and/or open-label datasets for Schizophrenia, Schizoaffective Disorder, Psychosis, Bipolar Disorder, Major Depressive Disorder, Attention Deficit Hyperactivity Disorder, Disruptive Behaviour Disorders, Anxiety Disorders, Autistic Spectrum Disorder, and Epilepsy (as a reference).
Exclusion Criteria: In order to develop an ML model with strong predictive power, the model must be trained on a large dataset; the more variance captured in the clinical data, the more accurate our models will be at predicting the treatment effectiveness of unseen patients. We will, for this reason, include all patients in our study.

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: We will measure the sensitivity, specificity and predictive power of our ML models in predicting treatment effectiveness, relative to each specific drug through a cross-validation method (described below in the Statistical Analysis section). Our measure of treatment effectiveness will depend on the primary endpoints of the trials, with examples including:
? Time until remission (days)
? Change from baseline (using a clinically relevant measure such as PANSS (Kay, Fizbein & Opfer, 1987), or YMRS (Young et al, 1978))
? Survival (yes/no)
? High vs. low quality of life scores (such as the Short Form-36 (Ware & Sherbourne, 1992) or WHOQOL-BREF (Skevington, Lofty, & O?Connel, 2004))

Main Predictor/Independent Variable and how it will be categorized/defined for your study: For each of our ML models, our main independent variable will be treatment allocation and will be defined as a binary dummy variable.
Our predictor variables of interest are all digitally archived information, which includes patient profile characteristics such as variables of the demographic, clinical, cognitive, genetic, lab-test, and free-text survey information, as well as the characteristics of the trials, such as when and where the trial was performed, the number of subjects and the intervention used. The more moderating variables available in our data, the better our model will be able to make personalized predictions. The following list just provides a few potential example predictor variables that could be included in our analysis:
? Age (years)
? Sex (male/female/intersex)
? Race (Caucasian, African American, etc.)
? BMI (continuous)
? Smoker (yes/no)
? Time since diagnosis (years)
? Previous treatments (yes/no, name, dose)
? Additional Diseases / Comorbidities (yes/no, name)
? Measures of psychopathology (for example, YMRS)
? Relapse occurrence/time to relapse (yes/no, days)

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: All digitally archived variables.

Statistical Analysis Plan: ? A descriptive analysis of all demographic, clinical, and pharmaceutical characteristics of participants. ANOVA and chi-square tests will be conducted to determine whether the distribution of continuous and categorical factors, respectively, are distributed equally among patients. Results will be displayed as the median and interquartile range (IQR) for continuous variables and as number and percentage frequency for categorical variables.
Aim One:
? All ML models will be developed and appraised with k-fold cross-validation (Pedregosa et al, 2011), partitioning the entirety of the relevant constructed dataset into k disjoint subsets, with the model trained on k-1 of the subsets and the model?s predictive power tested on the remaining subset.
? The least absolute shrinkage and selection operator (LASSO) regression analysis method will be performed to determine the relationship between patient profile and treatment outcome. LASSO regression can obtain the subset of predictors that minimizes prediction error for a quantitative response variable, which, for our project, will be the measure of the treatment outcome relative to each disorder (e.g. time until remission) (Santosa & Symes, 1986).
Aim Two:
? A descriptive analysis of all demographic, clinical, and pharmaceutical characteristics of participants will be undertaken, with ANOVA and chi-square tests conducted to determine whether the distribution of continuous and categorical factors, respectively, are distributed equally among patients. Results will be displayed as the median and interquartile range (IQR) for continuous variables and as number and percentage frequency for categorical variables.
? k-nearest neighbours algorithm (KNN) will be used to cluster patient profiles across all conditions of interest to discover previously unidentified symptom-clustering across mental disorders. KNN is a non-parametric pattern recognition method that can assign each patient profile to a particular cluster or group based on similarities across all patients and mental disorders (Altman, 1992).
? t-Distributed Stochastic Neighbour Embedding (t-SNE), an ML algorithm for dimensionality reduction, will be used to visualize the symptom-clustering groups in a low-dimensional space (van der Maaten & Hinton, 2008).

Narrative Summary: We will use Machine Learning (ML)to predict the effectiveness of common drugs used in the treatment of several mental disorders such as Schizophrenia and Major Depressive Disorder. Additionally, we will examine similarities between patient profiles to identify symptom clusters across disorders. This project has the potential to materially enhance our scientific knowledge of treatment accuracy and the categorization of mental disorders. Regarding public health, prospective identification of treatment accuracy and applicability can lead to optimal (precise, efficient, and individually-tailored) treatment plans for each patient, enhancing wellbeing.

Project Timeline: Project start date: August 2019
Initial Analysis completion date: May 2020
Manuscript Drafted: June 2020
Manuscript submitted for publication: July 2020
Report back to YODA: August 2020

Dissemination Plan: We anticipate the generation of at least two manuscripts from this project on our models' ability to predict treatment outcomes and recluster patient profiles. The target audience would be physicians as well as psychiatry and pharmacology researchers. Potentially suitable journals for these manuscripts include Neuropsychopharmacology, Journal of Psychiatric Research, The Canadian Journal of Psychiatry, JAMA Psychiatry, Journal of Machine Learning Research and Artificial Intelligence in Medicine.

Bibliography:

Altman, N. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 46(3), pp. 175 ? 185. doi:10.1080/00031305.1992.10475879. hdl:1813/31637
Chekroud, A., Gueorguieva, R., Krumholz, H., Trivedi, M., Krystal, J., & McCarthy, G. (2017). Reevaluating the efficacy and predictability of antidepressant treatments: A system Clustering Approach. JAMA Psychiatry 74(4), pp. 370-378. doi:10.1001/jamapsychiatry.2017.0025
Chekroud, A., Zotti, R., Shehzad, Z., Gueorguieva, R., Johnson, M., Trivedi, M., . . . Corlett, P. (2016). Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3, pp. 243-250. doi: https://doi.org/10.1016/S2215-0366(15)00471-X
Kay, S., Fizbein, A., & Opfer, L. (1987). The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Schizophrenia Bulletin, 13(2), 261-276. Retrieved from http://dx.doi.org/10.1093/schbul/13.2.261
Lu, Y., Goldstein, D., Angrist, M., & Cavalleri, G. (2014). Personalized Medicine and Human Genetic Diversity. Cold Spring Harbor Perspectives in Medicine, 4(9). doi:10.1101/cshperspect.a008581
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, O., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. JMLR, 12, pp. 2825-2830. Retrieved from http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
Santosa, F., & Symes, W. (1986). Linear inversion of band-limited reflection seismograms. SIAM Journal on Scientific and Statistical Computing. 7(4), pp. 1307?1330. doi:10.1137/0907087
Skevington, S., Lofty, M., & O?Connell, K. (2004). The World Health Organization?s WHOQOL-BREF quality of life assessment: Psychometric properties and results of the international field trial. Quality of Life Research, 13, pp. 299-310. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.463.2578&rep=re…
van der Maaten, L., & Hinton, G. (2008). Visualizing Data Using t-SNE. Journal of Machine Learning Research. 9, pp. 2579?2605. Retrieved from http://jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
Ware, J., & Sherbourne, C. (1992). The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30(6), pp. 473-483. Retrieved from https://pdfs.semanticscholar.org/06cb/0076e310136d0ca8b56cc8585ec2bf43e0…
Young, R., Biggs, J., Ziegler, V., & Meyer, D. (1978). A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry. 133(5), pp. 429-35. doi:10.1192/bjp.133.5.429
Zbozinek, T., Rose, R., Wolitzky-Taylor, K., Sherbourne, C., Sullivan, G., Stein, M., Roy-Burne, P., & Craske, M. (2012). Diagnostic overlap of generalized anxiety disorder in a primary care sample. Depression & Anxiety, 29(12), 1065-1071. doi:1002/da.22026