Case StudyAIMental HealthBringing Treatment-Resistant Depression to Light: AI-Driven Patient Discovery in Real-World DataRead more
PublicationAIMental HealthAI-Based Estimation of the CGI-I to Address Gaps in Real-World Data and Increase Study Sample SizeDownload
PublicationDataMental HealthClinical burden of major depressive disorder with versus without prominent anhedonia using a real-world electronic health records and claims linked databaseDownload
PublicationDataMental HealthReal-world effectiveness of cariprazine in major depressive disorder and bipolar I disorder in the United StatesRead more
PublicationAIMental HealthDevelopment of a Machine Learning Model for Estimating CGI-I Scores Using Electronic Medical Records from Real-World Data Sources in Schizophrenia, Bipolar I and Major Depressive DisorderDownload
PublicationDataMental HealthSuicidal Ideation in Patients Treated with Glucagon-like Peptide 1 Receptor (GLP1) Agonists: A Retrospective Real-World AnalysisDownload
PublicationAIMental HealthA machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patientsDownload
PublicationDataMental HealthReductions in Depressive Symptoms After Brexpiprazole Augmentation Among Patients with Major Depressive Disorder Receiving Antidepressant Therapy in Real-World SettingsDownload
PublicationDataMental Healthhe Association Between Adherence to Esketamine Nasal Spray Therapy Dosing Regimen and Changes in Depressive Symptoms Among Patients With Treatment-Resistant Depression in the U.S.Download
PublicationDataMental HealthAddressing Gaps in Real-World Data Sources through Automated Extraction of Psychiatric Comorbidities and Medication Side Effects from Clinical NotesDownload
PublicationAIMental HealthIdentification of Treatment Resistant Major Depressive Disorder using a Machine Learning AlgorithmDownload
PublicationAIMental HealthUse of Medical Language Processing in Real-World Data to Understand Antidepressant Side Effects and Improve Research in Mental HealthDownload
PublicationAIMental HealthDevelopment of a Machine Learning Model for Estimating PHQ-9 Scores Using Clinical Notes from Real-World Data SourcesDownload
PublicationAIMental HealthThe Association Between Credit Risk Score and Major Depressive Disorder Burden Using a Machine Learning Estimation of the PHQ-9 in a Real-World CohortDownload
PublicationDataMental HealthWeight Gain and Comorbidities Associated With Oral Second-Generation Antipsychotics: Analysis of Real-World Data for Patients With Bipolar I Disorder or SchizophreniaDownload
PublicationDataMental HealthHarmonized Outcome Measures for Use in Depression Patient Registries and Clinical PracticesDownload
PublicationDataMental HealthThe Association Between Race, Social Determinants of Health, and Treatment for Major Depressive Disorder (MDD) in a Real-World CohortDownload
PublicationDataMental HealthChanges in Weight Across the Life Span in Patients with Bipolar Disorder I Treated with Second Generation AntipsychoticsDownload
PublicationAIMental HealthUse of a Natural Language Processing-Based Approach to Extract Depression Symptom Severity and Suicide Ideation from Clinical Notes to Support Depression ResearchDownload
PublicationDataMental HealthWeight Gain and Treatment Interruptions With Second-Generation Oral Antipsychotics: Analysis of Real-World Data Among Patients With Schizophrenia or Bipolar I DisorderDownload
PublicationDataMental HealthEvaluating the Feasibility of Capturing a Core Set of Harmonized Depression Outcome Measures in Primary Care and Mental Health Patient RegistriesDownload
PublicationDataMental HealthDepression and Patient Outcomes Among Rheumatoid Arthritis Patients in a Large US-Based Real World CohortDownload
PublicationDataMental HealthPatient & Provider Characteristics Associated with PHQ-9 Administration and Outcomes Assessment in Routine Clinical Practice: Findings from the OM1 Intelligent Data CloudDownload
Webinar & VideoAIMental HealthPredicting Response and Personalizing Treatment Decisions in Mental Health: New Opportunities with AI and Real-World DataRead more
Webinar & VideoAICardioMental HealthBehind the Insights: Ep 04 GLP-1s & Suicidal Ideation, Jessica Probst, MPH & Carl Marci, MDRead more
News & ArticleAIMental HealthUnderstanding the link between GLP-1 therapies and suicidal ideation using real-world dataRead more
News & ArticleDataMental HealthOM1 to Present Groundbreaking Research on GLP-1 Agonists and Suicidal Ideation at ISPOR 2025Read more
News & ArticleDataCardioMental HealthOM1 to Present Groundbreaking Research on GLP-1 Agonists and Suicidal Ideation at ISPOR 2025.Read more
News & ArticleDataMental HealthOM1 expands mental health network to over six million patients, enhancing real-world evidence and personalized care capabilitiesRead more
News & ArticleDataMental HealthOM1 Adds 1 Million Patients to Its Mental Health Network, Significantly Expanding Specialty Coverage and Driving New Possibilities for Real-World Research and Personalized CareRead more
Webinar & VideoAIMental HealthBehind the Insights: Ep 03 Decoding Treatment-Resistant Depression with Carl Marci, MDRead more
News & ArticleDataMental HealthMDMA Therapy for Mental Health Conditions: Do the Benefits Outweigh the Risks?Read more
News & ArticleAIMental HealthPeople are using ChatGPT as a therapist. Mental health experts have some concernsRead more
Webinar & VideoAIMental HealthUnveiling the Unseen: AI's Role in Identifying Treatment-Resistant DepressionRead more
News & ArticleAIMental HealthIntegrating Ambient Clinical Voice Technology – The Impacts, Challenges, and BenefitsRead more
News & ArticleDataMental HealthEXPERT PRAISES PROPOSED FEDERAL RULE TO STRENGTHEN BEHAVIORAL HEALTH PARITYRead more
News & ArticleAIMental HealthMachine learning model accurately estimates PHQ-9 scores from clinical notesRead more
News & ArticleAIMental Health“I Know It When I See It”: Definitions of Treatment-Resistant DepressionRead more
News & ArticleDataMental HealthHow Real-World Data Can Contribute to Improved Mental Health TreatmentRead more
News & ArticleDataMental HealthOM1 Launches Analytics to Deliver Mental Health & Immunology Insights on Prescriber Trends and Therapy EffectivenessRead more
News & ArticleAIMental HealthAs suicide rates spike, new AI platform could ‘fill the gap’ in mental health care, say Boston researchersRead more
News & ArticleAIMental HealthHard to Define, Harder to Find Patients: Using AI & Real-World Data to Understand Treatment Resistant DepressionRead more
News & ArticleDataMental HealthOM1 Expands Mental Health Offering With Real-World Datasets Including Schizophrenia and Bipolar DisorderRead more
News & ArticleDataMental HealthReal-World Data Delivers Dose of Hope for Mental Health in the U.S.Read more
![MIAMI BEACH, Fla. — A novel machine learning model accurately estimated scores from a depression questionnaire from complete and partial clinical notes, per a poster at the American Society of Clinical Psychopharmacology annual meeting.“In the real world, there’s a lot of missing data and [Patient Health Questionnaire-9] scores are commonly used and patients record them, but not](/_next/image/?url=https%3A%2F%2Fimages.ctfassets.net%2Fddhe5ahaolzf%2F2wrdaT8JqEIhQnO9DOVyde%2Fc04bff599118eda62ea91d22920aeb7e%2Fdoctor-at-computer_2020.jpg&w=3840&q=75)
