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Rathnam S, Hart KL, Sharma A, Verhaak PF, McCoy TH, Doshi-Velez F, Perlis RH. Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians. JAMA Psychiatry 2024; 81:1003-1009. [PMID: 38985482 PMCID: PMC11238069 DOI: 10.1001/jamapsychiatry.2024.1778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/16/2024] [Indexed: 07/11/2024]
Abstract
Importance While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. Objective To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. Design, Setting, and Participants This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Main Outcomes and Measures Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Results Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Conclusions and Relevance Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification.
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Affiliation(s)
- Sarah Rathnam
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts
| | - Kamber L. Hart
- Center for Quantitative Health, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Abhishek Sharma
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts
| | - Pilar F. Verhaak
- Center for Quantitative Health, Massachusetts General Hospital, Boston
| | - Thomas H. McCoy
- Center for Quantitative Health, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Finale Doshi-Velez
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts
| | - Roy H. Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
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McCoy TH, Perlis RH. Dimensional Measures of Psychopathology in Children and Adolescents Using Large Language Models. Biol Psychiatry 2024:S0006-3223(24)01299-X. [PMID: 38866172 DOI: 10.1016/j.biopsych.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/04/2024] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND To enable greater use of National Institute of Mental Health Research Domain Criteria (RDoC) in real-world settings, we applied large language models (LLMs) to estimate dimensional psychopathology from narrative clinical notes. METHODS We conducted a cohort study using health records from individuals age ≤18 years evaluated in the psychiatric emergency department of a large academic medical center between November 2008 and March 2015. Outcomes were hospital admission and length of emergency department stay. RDoC domains were estimated using a Health Insurance Portability and Accountability Act-compliant LLM (gpt-4-1106-preview) and compared with a previously validated token-based approach. RESULTS The cohort included 3059 individuals (median age 16 years [interquartile range, 13-18]; 1580 [52%] female, 1479 [48%] male; 105 [3.4%] identified as Asian, 329 [11%] as Black, 288 [9.4%] as Hispanic, 474 [15%] as other race, and 1863 [61%] as White), of whom 1695 (55%) were admitted. Correlation between LLM-extracted RDoC scores and the token-based scores ranged from small to medium as assessed by Kendall's tau (0.14-0.22). In logistic regression models adjusting for sociodemographic and clinical features, admission likelihood was associated with greater scores on all domains, with the exception of the sensorimotor domain, which was inversely associated (p < .001 for all adjusted associations). Tests for bias suggested modest but statistically significant differences in positive valence scores by race (p < .05 for Asian, Black, and Hispanic individuals). CONCLUSIONS An LLM extracted estimates of 6 RDoC domains in an explainable manner, which were associated with clinical outcomes. This approach can contribute to a new generation of prediction models or biological investigations based on dimensional psychopathology.
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Affiliation(s)
- Thomas H McCoy
- Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Roy H Perlis
- Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
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Lee DY, Kim C, Kim J, Yun J, Lee Y, Chui CSL, Son SJ, Park RW, You SC. Comparative estimation of the effects of antihypertensive medications on schizophrenia occurrence: a multinational observational cohort study. BMC Psychiatry 2024; 24:128. [PMID: 38365637 PMCID: PMC10870661 DOI: 10.1186/s12888-024-05578-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/01/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND The association between antihypertensive medication and schizophrenia has received increasing attention; however, evidence of the impact of antihypertensive medication on subsequent schizophrenia based on large-scale observational studies is limited. We aimed to compare the schizophrenia risk in large claims-based US and Korea cohort of patients with hypertension using angiotensin-converting enzyme (ACE) inhibitors versus those using angiotensin receptor blockers (ARBs) or thiazide diuretics. METHODS Adults aged 18 years who were newly diagnosed with hypertension and received ACE inhibitors, ARBs, or thiazide diuretics as first-line antihypertensive medications were included. The study population was sub-grouped based on age (> 45 years). The comparison groups were matched using a large-scale propensity score (PS)-matching algorithm. The primary endpoint was incidence of schizophrenia. RESULTS 5,907,522; 2,923,423; and 1,971,549 patients used ACE inhibitors, ARBs, and thiazide diuretics, respectively. After PS matching, the risk of schizophrenia was not significantly different among the groups (ACE inhibitor vs. ARB: summary hazard ratio [HR] 1.15 [95% confidence interval, CI, 0.99-1.33]; ACE inhibitor vs. thiazide diuretics: summary HR 0.91 [95% CI, 0.78-1.07]). In the older subgroup, there was no significant difference between ACE inhibitors and thiazide diuretics (summary HR, 0.91 [95% CI, 0.71-1.16]). The risk for schizophrenia was significantly higher in the ACE inhibitor group than in the ARB group (summary HR, 1.23 [95% CI, 1.05-1.43]). CONCLUSIONS The risk of schizophrenia was not significantly different between the ACE inhibitor vs. ARB and ACE inhibitor vs. thiazide diuretic groups. Further investigations are needed to determine the risk of schizophrenia associated with antihypertensive drugs, especially in people aged > 45 years.
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Affiliation(s)
- Dong Yun Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, 164, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea
| | - Chungsoo Kim
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Jiwoo Kim
- Big Data Department, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Jeongwon Yun
- Big Data Department, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Yujin Lee
- Big Data Department, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Celine Sze Ling Chui
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, Hong Kong, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administration Region, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong Special Administration Region, Hong Kong Science Park, Hong Kong, China
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, 164, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
| | - Seng Chan You
- Department of Biomedicine Systems Informatics, Yonsei University College of Medicine, Seoul, Korea.
- Institute for Innovation in Digital Healthcare, Yonsei University, 50-1 Yonsei-ro, Seodaemungu, Seoul, 03722, Republic of Korea.
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Shi Y, Sprooten E, Mulders P, Vrijsen J, Bralten J, Demontis D, Børglum AD, Walters GB, Stefansson K, van Eijndhoven P, Tendolkar I, Franke B, Mota NR. Multi-polygenic scores in psychiatry: From disorder specific to transdiagnostic perspectives. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32951. [PMID: 37334623 PMCID: PMC10803201 DOI: 10.1002/ajmg.b.32951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 03/31/2023] [Accepted: 06/05/2023] [Indexed: 06/20/2023]
Abstract
The dense co-occurrence of psychiatric disorders questions the categorical classification tradition and motivates efforts to establish dimensional constructs with neurobiological foundations that transcend diagnostic boundaries. In this study, we examined the genetic liability for eight major psychiatric disorder phenotypes under both a disorder-specific and a transdiagnostic framework. The study sample (n = 513) was deeply phenotyped, consisting of 452 patients from tertiary care with mood disorders, anxiety disorders (ANX), attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders, and/or substance use disorders (SUD) and 61 unaffected comparison individuals. We computed subject-specific polygenic risk score (PRS) profiles and assessed their associations with psychiatric diagnoses, comorbidity status, as well as cross-disorder behavioral dimensions derived from a rich battery of psychopathology assessments. High PRSs for depression were unselectively associated with the diagnosis of SUD, ADHD, ANX, and mood disorders (p < 1e-4). In the dimensional approach, four distinct functional domains were uncovered, namely the negative valence, social, cognitive, and regulatory systems, closely matching the major functional domains proposed by the Research Domain Criteria (RDoC) framework. Critically, the genetic predisposition for depression was selectively reflected in the functional aspect of negative valence systems (R2 = 0.041, p = 5e-4) but not others. This study adds evidence to the ongoing discussion about the misalignment between current psychiatric nosology and the underlying psychiatric genetic etiology and underscores the effectiveness of the dimensional approach in both the functional characterization of psychiatric patients and the delineation of the genetic liability for psychiatric disorders.
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Affiliation(s)
- Yingjie Shi
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Emma Sprooten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter Mulders
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Janna Vrijsen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
- Pro Persona Mental Health Care, Depression Expertise Centre, Nijmegen, The Netherlands
| | - Janita Bralten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Ditte Demontis
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Copenhagen, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders D. Børglum
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Copenhagen, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - G. Bragi Walters
- deCODE Genetics, Reykjavík, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Kari Stefansson
- deCODE Genetics, Reykjavík, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Philip van Eijndhoven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Indira Tendolkar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nina Roth Mota
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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Sim JA, Huang X, Horan MR, Stewart CM, Robison LL, Hudson MM, Baker JN, Huang IC. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Artif Intell Med 2023; 146:102701. [PMID: 38042599 PMCID: PMC10693655 DOI: 10.1016/j.artmed.2023.102701] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/30/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
OBJECTIVE Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care. METHODS We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs. RESULTS Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP. CONCLUSIONS This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.
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Affiliation(s)
- Jin-Ah Sim
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States; School of AI Convergence, Hallym University, Chuncheon, Republic of Korea
| | - Xiaolei Huang
- Department of Computer Science, University of Memphis, Memphis, TN, United States
| | - Madeline R Horan
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Christopher M Stewart
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, United States
| | - Leslie L Robison
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Melissa M Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States; Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Justin N Baker
- Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - I-Chan Huang
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States.
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Ashar YK, Lumley MA, Perlis RH, Liston C, Gunning FM, Wager TD. Reattribution to Mind-Brain Processes and Recovery From Chronic Back Pain: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2023; 6:e2333846. [PMID: 37768666 PMCID: PMC10539987 DOI: 10.1001/jamanetworkopen.2023.33846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/08/2023] [Indexed: 09/29/2023] Open
Abstract
Importance In primary chronic back pain (CBP), the belief that pain indicates tissue damage is both inaccurate and unhelpful. Reattributing pain to mind or brain processes may support recovery. Objectives To test whether the reattribution of pain to mind or brain processes was associated with pain relief in pain reprocessing therapy (PRT) and to validate natural language-based tools for measuring patients' symptom attributions. Design, Setting, and Participants This secondary analysis of clinical trial data analyzed natural language data from patients with primary CBP randomized to PRT, placebo injection control, or usual care control groups and treated in a US university research setting. Eligible participants were adults aged 21 to 70 years with CBP recruited from the community. Enrollment extended from 2017 to 2018, with the current analyses conducted from 2020 to 2022. Interventions PRT included cognitive, behavioral, and somatic techniques to support reattributing pain to nondangerous, reversible mind or brain causes. Subcutaneous placebo injection and usual care were hypothesized not to affect pain attributions. Main Outcomes and Measures At pretreatment and posttreatment, participants listed their top 3 perceived causes of pain in their own words (eg, football injury, bad posture, stress); pain intensity was measured as last-week average pain (0 to 10 rating, with 0 indicating no pain and 10 indicating greatest pain). The number of attributions categorized by masked coders as reflecting mind or brain processes were summed to yield mind-brain attribution scores (range, 0-3). An automated scoring algorithm was developed and benchmarked against human coder-derived scores. A data-driven natural language processing (NLP) algorithm identified the dimensional structure of pain attributions. Results We enrolled 151 adults (81 female [54%], 134 White [89%], mean [SD] age, 41.1 [15.6] years) reporting moderate severity CBP (mean [SD] intensity, 4.10 [1.26]; mean [SD] duration, 10.0 [8.9] years). At pretreatment, 41 attributions (10%) were categorized as mind- or brain-related across intervention conditions. PRT led to significant increases in mind- or brain-related attributions, with 71 posttreatment attributions (51%) in the PRT condition categorized as mind- or brain-related, as compared with 22 (8%) in control conditions (mind-brain attribution scores: PRT vs placebo, g = 1.95 [95% CI, 1.45-2.47]; PRT vs usual care, g = 2.06 [95% CI, 1.57-2.60]). Consistent with hypothesized PRT mechanisms, increases in mind-brain attribution score were associated with reductions in pain intensity at posttreatment (standardized β = -0.25; t127 = -2.06; P = .04) and mediated the effects of PRT vs control on 1-year follow-up pain intensity (β = -0.35 [95% CI, -0.07 to -0.63]; P = .05). The automated word-counting algorithm and human coder-derived scores achieved moderate and substantial agreement at pretreatment and posttreatment (Cohen κ = 0.42 and 0.68, respectively). The data-driven NLP algorithm identified a principal dimension of mind and brain vs biomechanical attributions, converging with hypothesis-driven analyses. Conclusions and Relevance In this secondary analysis of a randomized trial, PRT increased attribution of primary CBP to mind- or brain-related causes. Increased mind-brain attribution was associated with reductions in pain intensity.
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Affiliation(s)
- Yoni K. Ashar
- Division of Internal Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora
| | - Mark A. Lumley
- Department of Psychology, Wayne State University, Detroit, Michigan
| | - Roy H. Perlis
- Center for Quantitative Health, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medical College, New York, New York
| | - Faith M. Gunning
- Department of Psychiatry, Weill Cornell Medical College, New York, New York
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire
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Vogelgsang J, Dan S, Lally AP, Chatigny M, Vempati S, Abston J, Durning PT, Oakley DH, McCoy TH, Klengel T, Berretta S. Dimensional clinical phenotyping using post-mortem brain donor medical records: post-mortem RDoC profiling is associated with Alzheimer's disease neuropathology. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12464. [PMID: 37745891 PMCID: PMC10517223 DOI: 10.1002/dad2.12464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/30/2023] [Accepted: 07/07/2023] [Indexed: 09/26/2023]
Abstract
Introduction Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post-mortem work, as assessments of phenotypic concepts need to rely on existing records. Methods We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures. Results Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes. Discussion This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from post-mortem EHR. The associations may accelerate post-mortem brain research beyond classical case-control designs.
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Affiliation(s)
- Jonathan Vogelgsang
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Shu Dan
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Anna P. Lally
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Michael Chatigny
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
- Harvard Brain Tissue Resource Center, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Sangeetha Vempati
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Joshua Abston
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Peter T. Durning
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Derek H. Oakley
- Harvard Brain Tissue Resource Center, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
- Department of Pathology, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Thomas H. McCoy
- Department of Psychiatry and Medicine, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Torsten Klengel
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
- Harvard Brain Tissue Resource Center, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
| | - Sabina Berretta
- Department of Psychiatry, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
- Harvard Brain Tissue Resource Center, McLean HospitalHarvard Medical SchoolBelmontMassachusettsUSA
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Vogelgsang JS, Dan S, Lally AP, Chatigny M, Vempati S, Abston J, Durning PT, Oakley DH, McCoy TH, Klengel T, Berretta S. Dimensional clinical phenotyping using post-mortem brain donor medical records: Association with neuropathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.04.539430. [PMID: 37205494 PMCID: PMC10187289 DOI: 10.1101/2023.05.04.539430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
INTRODUCTION Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to postmortem work, as assessment of newly developed phenotypic concepts needs to rely on existing records. METHODS We adapted well-validated methodologies to compute NIMH research domain criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether RDoC cognitive domain scores were associated with hallmark Alzheimer's disease (AD) neuropathological measures. RESULTS Our results confirm an association of EHR-derived cognitive scores with hallmark neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß=0.38, p=0.0004), parietal (ß=0.35, p=0.0008), temporal (ß=0.37, p=0. 0004) and occipital (ß=0.37, p=0.0003) lobes. DISCUSSION This proof of concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from postmortem EHR.
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Fu S, Wang L, Moon S, Zong N, He H, Pejaver V, Relevo R, Walden A, Haendel M, Chute CG, Liu H. Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review. Clin Transl Sci 2023; 16:398-411. [PMID: 36478394 PMCID: PMC10014687 DOI: 10.1111/cts.13463] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/03/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.
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Affiliation(s)
- Sunyang Fu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Liwei Wang
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Sungrim Moon
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Nansu Zong
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Huan He
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Rose Relevo
- The National Center for Data to Health, Bethesda, Maryland, USA
| | - Anita Walden
- The National Center for Data to Health, Bethesda, Maryland, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | - Hongfang Liu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
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10
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Castro VM, Rosand J, Giacino JT, McCoy TH, Perlis RH. Case-control study of neuropsychiatric symptoms in electronic health records following COVID-19 hospitalization in 2 academic health systems. Mol Psychiatry 2022; 27:3898-3903. [PMID: 35705635 PMCID: PMC9199464 DOI: 10.1038/s41380-022-01646-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 05/18/2022] [Accepted: 05/23/2022] [Indexed: 02/08/2023]
Abstract
Neuropsychiatric symptoms may persist following acute COVID-19 illness, but the extent to which these symptoms are specific to COVID-19 has not been established. We utilized electronic health records across 6 hospitals in Massachusetts to characterize cohorts of individuals discharged following admission for COVID-19 between March 2020 and May 2021, and compared them to individuals hospitalized for other indications during this period. Natural language processing was applied to narrative clinical notes to identify neuropsychiatric symptom domains up to 150 days following hospitalization, in addition to those reflected in diagnostic codes as measured in prior studies. Among 6619 individuals hospitalized for COVID-19 drawn from a total of 42,961 hospital discharges, the most commonly-documented symptom domains between 31 and 90 days after initial positive test were fatigue (13.4%), mood and anxiety symptoms (11.2%), and impaired cognition (8.0%). In regression models adjusted for sociodemographic features and hospital course, none of these were significantly more common among COVID-19 patients; indeed, mood and anxiety symptoms were less frequent (adjusted OR 0.72 95% CI 0.64-0.92). Between 91 and 150 days after positivity, most commonly-detected symptoms were fatigue (10.9%), mood and anxiety symptoms (8.2%), and sleep disruption (6.8%), with impaired cognition in 5.8%. Frequency was again similar among non-COVID-19 post-hospital patients, with mood and anxiety symptoms less common (aOR 0.63, 95% CI 0.52-0.75). Propensity-score matched analyses yielded similar results. Overall, neuropsychiatric symptoms were common up to 150 days after initial hospitalization, but occurred at generally similar rates among individuals hospitalized for other indications during the same period. Post-acute sequelae of COVID-19 may benefit from standard if less-specific treatments developed for rehabilitation after hospitalization.
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Affiliation(s)
- Victor M Castro
- Center for Quantitative Health, Massachusetts General Hospital and Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Research Information Science and Computing, Mass General Brigham, Somerville, MA, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Joseph T Giacino
- Department of Psychiatry, Spaulding Rehabilitation Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital and Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital and Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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11
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Lage I, McCoy TH, Perlis RH, Doshi-Velez F. Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records. J Affect Disord 2022; 306:254-259. [PMID: 35181388 PMCID: PMC9980713 DOI: 10.1016/j.jad.2022.02.046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/07/2022] [Accepted: 02/14/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND With the emergence of evidence-based treatments for treatment-resistant depression, strategies to identify individuals at greater risk for treatment resistance early in the course of illness could have clinical utility. We sought to develop and validate a model to predict treatment resistance in major depressive disorder using coded clinical data from the electronic health record. METHODS We identified individuals from a large health system with a diagnosis of major depressive disorder receiving an index antidepressant prescription, and used a tree-based machine learning classifier to build a risk stratification model to identify those likely to experience treatment resistance. The resulting model was validated in a second health system. RESULTS In the second health system, the extra trees model yielded an AUC of 0.652 (95% CI: 0.623-0.682); with sensitivity constrained at 0.80, specificity was 0.358 (95% CI: 0.300-0.413). Lift in the top quintile was 1.99 (95% CI: 1.76-2.22). Including additional data for the 4 weeks following treatment initiation did not meaningfully improve model performance. LIMITATIONS The extent to which these models generalize across additional health systems will require further investigation. CONCLUSION Electronic health records facilitated stratification of risk for treatment-resistant depression and demonstrated generalizability to a second health system. Efforts to improve upon such models using additional measures, and to understand their performance in real-world clinical settings, are warranted.
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Affiliation(s)
- Isaac Lage
- Harvard John A. Paulson School of Engineering and Applied Sciences, 29 Oxford Street, Cambridge, MA 02138, USA
| | - Thomas H. McCoy
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Roy H. Perlis
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA,Clinical Correspondence: Roy H. Perlis, MD MSc, Massachusetts General Hospital, 185 Cambridge Street, 6th Floor, Boston, MA 02114, USA, , Phone: (617) 726-7426, Fax: (617) 726-0830
| | - Finale Doshi-Velez
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, 1 Oxford St, Science Center, 316.04, Cambridge, MA 02138, USA.
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12
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Cuthbert BN. Research Domain Criteria (RDoC): Progress and Potential. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2022; 31:107-114. [PMID: 35692384 PMCID: PMC9187047 DOI: 10.1177/09637214211051363] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
The National Institute of Mental Health (NIMH) addressed in its 2008 Strategic Plan an emerging concern that the current diagnostic system was hampering translational research, as accumulating data suggested that disorder categories constituted heterogeneous syndromes rather than specific diseases. However, established practices in peer review placed high priority on extant disorders in evaluating grant applications for mental illness. To provide guidelines for alternative study designs, NIMH included a goal to develop new ways of studying psychopathology based on dimensions of measurable behavior and related neurobiological measures. The Research Domain Criteria (RDoC) project is the result, intended to build a literature that informs new conceptions of mental illness and future revisions to diagnostic manuals. The framework calls for the study of empirically-derived fundamental dimensions as characterized by related behavioral/psychological and neurobiological data (e.g., reward valuation, working memory). RDoC also emphasizes full-range dimensional approaches (from typical to increasingly abnormal), neurodevelopment and environmental effects, and research designs that integrate data across behavioral, biological, and self-report measures. This commentary provides an overview of the project's first decade and its potential future directions. RDoC remains grounded in experimental psychopathology perspectives, and its progress is strongly linked to psychological measurement and integrative approaches to brain-behavior relationships.
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13
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Predictive structured-unstructured interactions in EHR models: A case study of suicide prediction. NPJ Digit Med 2022; 5:15. [PMID: 35087182 PMCID: PMC8795240 DOI: 10.1038/s41746-022-00558-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 12/13/2021] [Indexed: 11/20/2022] Open
Abstract
Clinical risk prediction models powered by electronic health records (EHRs) are becoming increasingly widespread in clinical practice. With suicide-related mortality rates rising in recent years, it is becoming increasingly urgent to understand, predict, and prevent suicidal behavior. Here, we compare the predictive value of structured and unstructured EHR data for predicting suicide risk. We find that Naive Bayes Classifier (NBC) and Random Forest (RF) models trained on structured EHR data perform better than those based on unstructured EHR data. An NBC model trained on both structured and unstructured data yields similar performance (AUC = 0.743) to an NBC model trained on structured data alone (0.742, p = 0.668), while an RF model trained on both data types yields significantly better results (AUC = 0.903) than an RF model trained on structured data alone (0.887, p < 0.001), likely due to the RF model’s ability to capture interactions between the two data types. To investigate these interactions, we propose and implement a general framework for identifying specific structured-unstructured feature pairs whose interactions differ between case and non-case cohorts, and thus have the potential to improve predictive performance and increase understanding of clinical risk. We find that such feature pairs tend to capture heterogeneous pairs of general concepts, rather than homogeneous pairs of specific concepts. These findings and this framework can be used to improve current and future EHR-based clinical modeling efforts.
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14
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Bayramli I, Castro V, Barak-Corren Y, Madsen EM, Nock MK, Smoller JW, Reis BY. Temporally informed random forests for suicide risk prediction. J Am Med Inform Assoc 2021; 29:62-71. [PMID: 34725687 PMCID: PMC8714280 DOI: 10.1093/jamia/ocab225] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/20/2021] [Accepted: 10/04/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions. MATERIALS AND METHODS We propose a temporally enhanced variant of the random forest (RF) model-Omni-Temporal Balanced Random Forests (OT-BRFs)-that incorporates temporal information in every tree within the forest. We develop and validate this model using longitudinal EHRs and clinician notes from the Mass General Brigham Health System recorded between 1998 and 2018, and compare its performance to a baseline Naive Bayes Classifier and 2 standard versions of balanced RFs. RESULTS Temporal variables were found to be associated with suicide risk: Elevated suicide risk was observed in individuals with a higher total number of visits as well as those with a low rate of visits over time, while lower suicide risk was observed in individuals with a longer period of EHR coverage. RF models were more accurate than Naive Bayesian classifiers at predicting suicide risk in advance (area under the receiver operating curve = 0.824 vs. 0.754, respectively). The proposed OT-BRF model performed best among all RF approaches, yielding a sensitivity of 0.339 at 95% specificity, compared to 0.290 and 0.286 for the other 2 RF models. Temporal variables were assigned high importance by the models that incorporated them. DISCUSSION We demonstrate that temporal variables have an important role to play in suicide risk detection and that requiring their inclusion in all RF trees leads to increased predictive performance. Integrating temporal information into risk prediction models helps the models interpret patient data in temporal context, improving predictive performance.
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Affiliation(s)
- Ilkin Bayramli
- Predictive Medicine Group, Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA
- Harvard University, Cambridge, Massachusetts, USA
| | - Victor Castro
- Mass General Brigham Research Information Science and Computing, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yuval Barak-Corren
- Predictive Medicine Group, Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Emily M Madsen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Psychiatry, Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Matthew K Nock
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Psychology, Harvard University, Cambridge, Massachusetts, USA
- Mental Health Research Program, Franciscan Children’s, Brighton, Massachusetts, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Psychiatry, Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Ben Y Reis
- Predictive Medicine Group, Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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15
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Clapp MA, McCoy TH. The potential of big data for obstetrics discovery. Curr Opin Endocrinol Diabetes Obes 2021; 28:553-557. [PMID: 34709211 DOI: 10.1097/med.0000000000000679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
PURPOSE OF REVIEW The purpose of this article is to introduce the concept of 'Big Data' and review its potential to advance scientific discovery in obstetrics. RECENT FINDINGS Big Data is now ubiquitous in medicine, being used in many specialties to understand the pathophysiology, risk factors, and treatment for many diseases. Big Data analyses often employ machine learning methods to understand the complex relationships that may exist within these sources. We review the basic principles of supervised and unsupervised machine learning methods, including deep learning. We highlight how these methods have been used to study genetic risk factors for preterm birth, interpreting electronic fetal heart rate tracings, and predict adverse maternal and neonatal outcomes during pregnancy and delivery. Despite its promise, there are challenges with using Big Data, including data integrity, generalizability (namely the concerns about perpetuating inequalities), and confidentiality. SUMMARY The combination of new data and enhanced methods present a synergistic opportunity to explore the complex relationships common to human illness and medical practice, including obstetrics. With prediction as a primary objective instead of the more familiar goals of hypothesis testing, these analytic methods can capture multifaceted, rare, and nuanced relationships between exposures and outcomes that exist within these large data sets.
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Affiliation(s)
- Mark A Clapp
- Department of Obstetrics and Gynecology
- Center for Quantitative Health, Massachusetts General Hospital
- Harvard Medical School, Boston, Massachusetts, USA
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital
- Harvard Medical School, Boston, Massachusetts, USA
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16
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Beam E, Potts C, Poldrack RA, Etkin A. A data-driven framework for mapping domains of human neurobiology. Nat Neurosci 2021; 24:1733-1744. [PMID: 34764476 PMCID: PMC8761068 DOI: 10.1038/s41593-021-00948-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 09/21/2021] [Indexed: 11/25/2022]
Abstract
Functional neuroimaging has been a mainstay of human neuroscience for the past 25 years. Interpretation of fMRI data has often occurred within knowledge frameworks crafted by experts, which have the potential to amplify biases that limit the replicability of findings. Here, we employ a computational approach to derive a data-driven framework for neurobiological domains that synthesizes the texts and data of nearly 20,000 human neuroimaging articles. Across multiple levels of domain specificity, the structure-function links within domains better replicate in held-out articles than those mapped from dominant frameworks in neuroscience and psychiatry. We further show that the data-driven framework partitions the literature into modular subfields, for which domains serve as generalizable prototypes of structure-function patterns in single articles. The approach to computational ontology we present here is the most comprehensive characterization of human brain circuits quantifiable with fMRI and may be extended to synthesize other scientific literatures.
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Affiliation(s)
- Elizabeth Beam
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.,Department of Psychology, Stanford University, Stanford, CA, USA.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | | | - Russell A Poldrack
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.,Department of Psychology, Stanford University, Stanford, CA, USA
| | - Amit Etkin
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. .,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA. .,Alto Neuroscience, Inc., Los Altos, CA, USA.
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17
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Lee DY, Park J, Noh JS, Roh HW, Ha JH, Lee EY, Son SJ, Park RW. Characteristics of Dimensional Psychopathology in Suicidal Patients With Major Psychiatric Disorders and Its Association With the Length of Hospital Stay: Algorithm Validation Study. JMIR Ment Health 2021; 8:e30827. [PMID: 34477555 PMCID: PMC8449292 DOI: 10.2196/30827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/28/2021] [Accepted: 08/02/2021] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Suicide has emerged as a serious concern for public health; however, only few studies have revealed the differences between major psychiatric disorders and suicide. Recent studies have attempted to quantify research domain criteria (RDoC) into numeric scores to systematically use them in computerized methods. The RDoC scores were used to reveal the characteristics of suicide and its association with major psychiatric disorders. OBJECTIVE We intended to investigate the differences in the dimensional psychopathology among hospitalized suicidal patients and the association between the dimensional psychopathology of psychiatric disorders and length of hospital stay. METHODS This retrospective study enrolled hospitalized suicidal patients diagnosed with major psychiatric disorders (depression, schizophrenia, and bipolar disorder) between January 2010 and December 2020 at a tertiary hospital in South Korea. The RDoC scores were calculated using the patients' admission notes. To measure the differences between psychiatric disorder cohorts, analysis of variance and the Cochran Q test were conducted and post hoc analysis for RDoC domains was performed with the independent two-sample t test. A linear regression model was used to analyze the association between the RDoC scores and sociodemographic features and comorbidity index. To estimate the association between the RDoC scores and length of hospital stay, multiple logistic regression models were applied to each psychiatric disorder group. RESULTS We retrieved 732 admissions for 571 patients (465 with depression, 73 with schizophrenia, and 33 with bipolar disorder). We found significant differences in the dimensional psychopathology according to the psychiatric disorders. The patient group with depression showed the highest negative RDoC domain scores. In the cognitive and social RDoC domains, the groups with schizophrenia and bipolar disorder scored higher than the group with depression. In the arousal RDoC domain, the depression and bipolar disorder groups scored higher than the group with schizophrenia. We identified significant associations between the RDoC scores and length of stay for the depression and bipolar disorder groups. The odds ratios (ORs) of the length of stay were increased because of the higher negative RDoC domain scores in the group with depression (OR 1.058, 95% CI 1.006-1.114) and decreased by higher arousal RDoC domain scores in the group with bipolar disorder (OR 0.537, 95% CI 0.285-0.815). CONCLUSIONS This study showed the association between the dimensional psychopathology of major psychiatric disorders related to suicide and the length of hospital stay and identified differences in the dimensional psychopathology of major psychiatric disorders. This may provide new perspectives for understanding suicidal patients.
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Affiliation(s)
- Dong Yun Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | - Jai Sung Noh
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Hyun Woong Roh
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jae Ho Ha
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Eun Young Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.,Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon, Republic of Korea
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.,Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
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18
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Tracy M, Tiliopoulos N, Sharpe L, Bach B. The clinical utility of the ICD-11 classification of personality disorders and related traits: A preliminary scoping review. Aust N Z J Psychiatry 2021; 55:849-862. [PMID: 34144646 DOI: 10.1177/00048674211025607] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVES A diagnostic system that fails to deliver clinically useful information will not be utilized and consequently will be unable to provide valuable data for health policy and clinical decision making. Therefore, it is imperative to obtain an accurate depiction of the clinical utility of the eleventh revision of the International Classification of Diseases (ICD-11) Personality Disorder (PD) model. The current mixed-methods systematic review aimed to determine the clinical utility of the ICD-11 PD classification system. METHOD An electronic screening of six databases was conducted and resulting studies were subjected to specific exclusion criteria, which elicited eight studies of interest. Study characteristics were tabulated and methodological quality was appraised. RESULTS Four studies offered strong support for the model's clinical utility, three offered some support accompanied by notable limitations and one study could only offer criticisms. CONCLUSION Future investigation of the ICD-11 PD classification system's (a) communicative value between clinicians and their patients, and between clinicians and their patient's families; (b) ease of use; and (c) feasibility in terms of practical application is required to achieve a complete understanding of its clinical utility and ultimately bring clarity to the current ambiguous findings.
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Affiliation(s)
- Mikaela Tracy
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
| | | | - Louise Sharpe
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
| | - Bo Bach
- Centre of Excellence on Personality Disorder, Psykiatrien i Region Sjalland, Slagelse, Denmark
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19
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Hirjak D, Schwarz E, Meyer-Lindenberg A. [Twelve years of research domain criteria in psychiatric research and practice: claim and reality]. DER NERVENARZT 2021; 92:857-867. [PMID: 34342676 DOI: 10.1007/s00115-021-01174-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/13/2021] [Indexed: 12/12/2022]
Abstract
The research domain criteria (RDoC) initiative of the National Institute of Mental Health (NIMH) was presented 12 years ago. The RDoC provides a matrix for the systematic, dimensional and domain-based study of mental disorders that is not based on established disease entities as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD). The primary aim of RDoC is to understand the nature of mental health and illness in terms of different extents of dysfunction in psychological/biological systems with interconnected diagnoses. This selective review article aims to provide a comprehensive overview of RDoC-based studies that have contributed to a better conceptual organization of mental disorders. Numerous promising and methodologically sophisticated studies on RDoC were identified. The number of scientific studies increased over time, indicating that dimensional research is increasingly being pursued in psychiatry. In summary, the RDoC initiative has a considerable potential to more precisely define the complexity of pathomechanisms underlying mental disorders; however, major challenges (e.g. small and heterogeneous study samples, unclear biomarker definitions and lack of replication studies) remain to be overcome in the future. Furthermore, it is plausible that a diagnostic system of the future will integrate categorical and dimensional approaches to arrive at a stratification that can underpin a precision medical approach in psychiatry.
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Affiliation(s)
- Dusan Hirjak
- Zentralinstitut für Seelische Gesundheit, Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät Mannheim, Universität Heidelberg, 68159, Mannheim, Deutschland.
| | - Emanuel Schwarz
- Zentralinstitut für Seelische Gesundheit, Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät Mannheim, Universität Heidelberg, 68159, Mannheim, Deutschland
| | - Andreas Meyer-Lindenberg
- Zentralinstitut für Seelische Gesundheit, Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät Mannheim, Universität Heidelberg, 68159, Mannheim, Deutschland
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20
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Castro VM, Rosand J, Giacino JT, McCoy TH, Perlis RH. Case-control study of neuropsychiatric symptoms following COVID-19 hospitalization in 2 academic health systems. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021. [PMID: 34282420 DOI: 10.1101/2021.07.09.21252353] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Neuropsychiatric symptoms may persist following acute COVID-19 illness, but the extent to which these symptoms are specific to COVID-19 has not been established. We utilized electronic health records across 6 hospitals in Massachusetts to characterize cohorts of individuals discharged following admission for COVID-19 between March 2020 and May 2021, and compared them to individuals hospitalized for other indications during this period. Natural language processing was applied to narrative clinical notes to identify neuropsychiatric symptom domains up to 150 days following hospitalization. Among 6,619 individuals hospitalized for COVID-19 drawn from a total of 42,961 hospital discharges, the most commonly documented symptom domains between 31 and 90 days after initial positive test were fatigue (13.4%), mood and anxiety symptoms (11.2%), and impaired cognition (8.0%). In models adjusted for sociodemographic features and hospital course, none of these were significantly more common among COVID-19 patients; indeed, mood and anxiety symptoms were less frequent (adjusted OR 0.72 95% CI 0.64-0.92). Between 91 and 150 days after positivity, most commonly-detected symptoms were fatigue (10.9%), mood and anxiety symptoms (8.2%), and sleep disruption (6.8%), with impaired cognition in 5.8%. Frequency was again similar among non-COVID-19 post-hospital patients, with mood and anxiety symptoms less common (aOR 0.63, 95% CI 0.52-0.75). Neuropsychiatric symptoms were common up to 150 days after initial hospitalization, but occurred at generally similar rates among individuals hospitalized for other indications during the same period. Post-acute sequelae of COVID-19 thus may benefit from standard if less-specific treatments developed for rehabilitation after hospitalization. Funding R01MH120227, R01MH116270 (Perlis).
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21
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Lee J, Liu C, Kim JH, Butler A, Shang N, Pang C, Natarajan K, Ryan P, Ta C, Weng C. Comparative effectiveness of medical concept embedding for feature engineering in phenotyping. JAMIA Open 2021; 4:ooab028. [PMID: 34142015 PMCID: PMC8206403 DOI: 10.1093/jamiaopen/ooab028] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/23/2021] [Accepted: 05/03/2021] [Indexed: 01/20/2023] Open
Abstract
Objective Feature engineering is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. We compared the effectiveness of MCEs learned from knowledge graphs and electronic healthcare records (EHR) data in retrieving relevant medical features for phenotyping tasks. Materials and Methods We implemented 5 embedding methods including node2vec, singular value decomposition (SVD), LINE, skip-gram, and GloVe with 2 data sources: (1) knowledge graphs obtained from the observational medical outcomes partnership (OMOP) common data model; and (2) patient-level data obtained from the OMOP compatible electronic health records (EHR) from Columbia University Irving Medical Center (CUIMC). We used phenotypes with their relevant concepts developed and validated by the electronic medical records and genomics (eMERGE) network to evaluate the performance of learned MCEs in retrieving phenotype-relevant concepts. Hits@k% in retrieving phenotype-relevant concepts based on a single and multiple seed concept(s) was used to evaluate MCEs. Results Among all MCEs, MCEs learned by using node2vec with knowledge graphs showed the best performance. Of MCEs based on knowledge graphs and EHR data, MCEs learned by using node2vec with knowledge graphs and MCEs learned by using GloVe with EHR data outperforms other MCEs, respectively. Conclusion MCE enables scalable feature engineering tasks, thereby facilitating phenotyping. Based on current phenotyping practices, MCEs learned by using knowledge graphs constructed by hierarchical relationships among medical concepts outperformed MCEs learned by using EHR data.
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Affiliation(s)
- Junghwan Lee
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Jae Hyun Kim
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Alex Butler
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Ning Shang
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Chao Pang
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Patrick Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Casey Ta
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA
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Abstract
OBJECTIVE The authors sought to characterize the association between prior mood disorder diagnosis and hospital outcomes among individuals admitted with COVID-19 to six Eastern Massachusetts hospitals. METHODS A retrospective cohort was drawn from the electronic health records of two academic medical centers and four community hospitals between February 15 and May 24, 2020. Associations between history of mood disorder and in-hospital mortality and hospital discharge home were examined using regression models among any hospitalized patients with positive tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). RESULTS Among 2,988 admitted individuals, 717 (24.0%) had a prior mood disorder diagnosis. In Cox regression models adjusted for age, sex, and hospital site, presence of a mood disorder prior to admission was associated with greater in-hospital mortality risk beyond hospital day 12 (crude hazard ratio=2.156, 95% CI=1.540, 3.020; fully adjusted hazard ratio=1.540, 95% CI=1.054, 2.250). A mood disorder diagnosis was also associated with greater likelihood of discharge to a skilled nursing facility or other rehabilitation facility rather than home (crude odds ratio=2.035, 95% CI=1.661, 2.493; fully adjusted odds ratio=1.504, 95% CI=1.132, 1.999). CONCLUSIONS Hospitalized individuals with a history of mood disorder may be at risk for greater COVID-19 morbidity and mortality and are at increased risk of need for postacute care. Further studies should investigate the mechanism by which these disorders may confer elevated risk.
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Affiliation(s)
- Victor M Castro
- Center for Quantitative Health, Massachusetts General Hospital, and Department of Psychiatry, Harvard Medical School, Boston (Castro, McCoy, Perlis); Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); and Department of Psychiatry, Weill Cornell Medicine, New York (Gunning)
| | - Faith M Gunning
- Center for Quantitative Health, Massachusetts General Hospital, and Department of Psychiatry, Harvard Medical School, Boston (Castro, McCoy, Perlis); Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); and Department of Psychiatry, Weill Cornell Medicine, New York (Gunning)
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, and Department of Psychiatry, Harvard Medical School, Boston (Castro, McCoy, Perlis); Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); and Department of Psychiatry, Weill Cornell Medicine, New York (Gunning)
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, and Department of Psychiatry, Harvard Medical School, Boston (Castro, McCoy, Perlis); Research Information Science and Computing, Mass General Brigham, Somerville, Mass. (Castro); and Department of Psychiatry, Weill Cornell Medicine, New York (Gunning)
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Hart KL, Perlis RH, McCoy TH. Mapping of Transdiagnostic Neuropsychiatric Phenotypes Across Patients in Two General Hospitals. J Acad Consult Liaison Psychiatry 2021; 62:430-439. [PMID: 34210402 DOI: 10.1016/j.jaclp.2021.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Multidimensional transdiagnostic phenotyping systems are increasingly important to neuropsychiatric phenotyping, particularly in translational research settings. The relationship the National Institute of Mental Health's Research Domain Criteria multidimensional approach to psychopathology and nonpsychiatric diagnoses has not been studied at scale but is relevant to those caring for neuropsychiatric illness in medical and surgical settings. METHODS We applied the CQH Dimensional Phenotyper natural language processing tool to estimate National Institute of Mental Health's Research Domain Criteria domain-associated symptoms of individuals admitted to nonpsychiatric wards at each of 2 large academic general hospitals over an 8-year period. We compared patterns in individual domain symptom burden, as well as a new pooled unidimensional measure, by primary medical and surgical diagnosis. RESULTS Analysis included 227,243 patients from hospital 1 of whom 68,793 (30.3%) had a prior psychiatric history and 220,213 patients from hospital 2 of whom 50,818 (23.1%) had a prior psychiatric history. The distribution of Research Domain Criteria symptom burdens over primary diagnosis was similar across hospital sites and differed significantly across primary medical or surgical diagnosis. The effect of primary medical or surgical diagnosis was larger than that of prior psychiatric history on Research Domain Criteria symptom burden. CONCLUSION Research Domain Criteria-based neuropsychiatric symptom burden estimated from general hospital patients' clinical documentation is more strongly associated with the primary hospital medical or surgical diagnosis than it is with the presence of a previous psychiatric history. The bidirectional role of psychiatric and somatic illness warrants further study through the lens of transdiagnostic phenotyping.
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Affiliation(s)
- Kamber L Hart
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA.
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Hart KL, Pellegrini AM, Forester BP, Berretta S, Murphy SN, Perlis RH, McCoy TH. Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method. Gen Hosp Psychiatry 2021; 68:46-51. [PMID: 33310013 PMCID: PMC7855889 DOI: 10.1016/j.genhosppsych.2020.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 01/29/2023]
Abstract
BACKGROUND Agitation is a common feature of many neuropsychiatric disorders. OBJECTIVE Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases. METHODS We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined. RESULTS Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype. CONCLUSION Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.
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Affiliation(s)
- Kamber L. Hart
- Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | | | - Brent P. Forester
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA,McLean Hospital, 115 Mill St, Belmont, MA 02478, USA
| | - Sabina Berretta
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; McLean Hospital, 115 Mill St, Belmont, MA 02478, USA.
| | - Shawn N. Murphy
- Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Roy H. Perlis
- Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Thomas H. McCoy
- Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA,Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA,Corresponding author at: Massachusetts General Hospital, 185 Cambridge Street, 6th Floor, Boston, MA 02114, USA. (T.H. McCoy)
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Uljarević M, Frazier TW, Phillips JM, Jo B, Littlefield S, Hardan AY. Mapping the Research Domain Criteria Social Processes Constructs to the Social Responsiveness Scale. J Am Acad Child Adolesc Psychiatry 2020; 59:1252-1263.e3. [PMID: 31376500 PMCID: PMC7470629 DOI: 10.1016/j.jaac.2019.07.938] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 07/03/2019] [Accepted: 07/25/2019] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Research Domain Criteria (RDoC) operationalizes a set of basic social dimensions that can be used to deconstruct sources of variation in social impairments across affected individuals, regardless of their diagnostic status. This is a necessary step toward the development of etiologically based and individualized treatments. The main objective of this investigation was to derive estimations of the RDoC social constructs from the Social Responsiveness Scale (SRS-2). METHOD Exploratory structural equation modeling and confirmatory factor analysis were conducted using individual SRS-2 items from six distinct databases ( N = 27,953; mean age = 9.55 years, SD = 3.79; 71.7% male participants) spanning normative (33.8%) and atypical (66.2%) development. The following models were estimated: a one-factor model; a three-factor model with separate attachment and affiliation, social communication, and understanding of mental states factors; and a four-factor model where social communication was further split into production of facial and non-facial communication. RESULTS The one-factor solution showed poor fit. The three-factor solution had adequate fit (comparative fit index = 0.952, Tucker Lewis Index = 0.937, root mean square error of approximation = 0.054). However, the four-factor solution had superior fit (comparative fit index = 0.973, Tucker Lewis Index = 0.961, root mean square error of approximation = 0.042) and was robust across age, sex, and clinical status. CONCLUSION To our knowledge, this is the first study examining estimations of the RDoC social constructs from an existing measure. Reported findings show promise for capturing important RDoC social constructs using the SRS-2 and highlight crucial areas for the development of novel dimensional social processing measures.
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Italia M, Forastieri C, Longaretti A, Battaglioli E, Rusconi F. Rationale, Relevance, and Limits of Stress-Induced Psychopathology in Rodents as Models for Psychiatry Research: An Introductory Overview. Int J Mol Sci 2020; 21:E7455. [PMID: 33050350 PMCID: PMC7589795 DOI: 10.3390/ijms21207455] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 12/18/2022] Open
Abstract
Emotional and cognitive information processing represent higher-order brain functions. They require coordinated interaction of specialized brain areas via a complex spatial and temporal equilibrium among neuronal cell-autonomous, circuitry, and network mechanisms. The delicate balance can be corrupted by stressful experiences, increasing the risk of developing psychopathologies in vulnerable individuals. Neuropsychiatric disorders affect twenty percent of the western world population, but therapies are still not effective for some patients. Elusive knowledge of molecular pathomechanisms and scarcity of objective biomarkers in humans present complex challenges, while the adoption of rodent models helps to improve our understanding of disease correlate and aids the search for novel pharmacological targets. Stress administration represents a strategy to induce, trace, and modify molecular and behavioral endophenotypes of mood disorders in animals. However, a mouse or rat model will only display one or a few endophenotypes of a specific human psychopathology, which cannot be in any case recapitulated as a whole. To override this issue, shared criteria have been adopted to deconstruct neuropsychiatric disorders, i.e., depression, into specific behavioral aspects, and inherent neurobiological substrates, also recognizable in lower mammals. In this work, we provide a rationale for rodent models of stress administration. In particular, comparing each rodent model with a real-life human traumatic experience, we intend to suggest an introductive guide to better comprehend and interpret these paradigms.
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Barroilhet SA, Pellegrini AM, McCoy TH, Perlis RH. Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records. Psychol Med 2020; 50:2221-2229. [PMID: 31544723 PMCID: PMC9980721 DOI: 10.1017/s0033291719002320] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Investigation of personality traits and pathology in large, generalizable clinical cohorts has been hindered by inconsistent assessment and failure to consider a range of personality disorders (PDs) simultaneously. METHODS We applied natural language processing (NLP) of electronic health record notes to characterize a psychiatric inpatient cohort. A set of terms reflecting personality trait domains were derived, expanded, and then refined based on expert consensus. Latent Dirichlet allocation was used to score notes to estimate the extent to which any given note reflected PD topics. Regression models were used to examine the relationship of these estimates with sociodemographic features and length of stay. RESULTS Among 3623 patients with 4702 admissions, being male, non-white, having a low burden of medical comorbidity, being admitted through the emergency department, and having public insurance were independently associated with greater levels of disinhibition, detachment, and psychoticism. Being female, white, and having private insurance were independently associated with greater levels of negative affectivity. The presence of disinhibition, psychoticism, and negative affectivity were each significantly associated with a longer stay, while detachment was associated with a shorter stay. CONCLUSIONS Personality features can be systematically and scalably measured using NLP in the inpatient setting, and some of these features associate with length of stay. Developing treatment strategies for patients scoring high in certain personality dimensions may facilitate more efficient, targeted interventions, and may help reduce the impact of personality features on mental health service utilization.
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Affiliation(s)
- Sergio A. Barroilhet
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Tufts University School of Medicine, Boston, MA, USA
- University Psychiatric Clinic, University of Chile Clinical Hospital, Santiago, Chile
| | - Amelia M. Pellegrini
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Thomas H. McCoy
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Roy H. Perlis
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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Walters CE, Nitin R, Margulis K, Boorom O, Gustavson DE, Bush CT, Davis LK, Below JE, Cox NJ, Camarata SM, Gordon RL. Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2020; 63:3019-3035. [PMID: 32791019 PMCID: PMC7890229 DOI: 10.1044/2020_jslhr-19-00397] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 04/23/2020] [Accepted: 05/19/2020] [Indexed: 05/13/2023]
Abstract
Purpose Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample (N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders. Supplemental Material https://doi.org/10.23641/asha.12753578.
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Affiliation(s)
- Courtney E. Walters
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN
- Neuroscience Program, College of Arts and Science, Vanderbilt University, Nashville, TN
| | - Rachana Nitin
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN
| | - Katherine Margulis
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN
- Kennedy Krieger Institute, Baltimore, MD
| | - Olivia Boorom
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Daniel E. Gustavson
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
| | - Catherine T. Bush
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jennifer E. Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Nancy J. Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Stephen M. Camarata
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Reyna L. Gordon
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
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Differences among Research Domain Criteria score trajectories by Diagnostic and Statistical Manual categorical diagnosis during inpatient hospitalization. PLoS One 2020; 15:e0237698. [PMID: 32842139 PMCID: PMC7447552 DOI: 10.1371/journal.pone.0237698] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 08/02/2020] [Indexed: 02/07/2023] Open
Abstract
With brief psychiatric hospitalizations, the extent to which symptoms change is rarely characterized. We sought to understand symptomatic changes across Research Domain Criteria (RDoC) dimensions, and the extent to which such improvement might be associated with risk for readmission. We identified 3,634 individuals with 4,713 hospital admissions to the psychiatric inpatient unit of a large academic medical center between 2010 and 2015. We applied a natural language processing tool to extract estimates of the five RDoC domains to the admission note and discharge summary and calculated the change in each domain. We examined the extent to which symptom domains changed during admission, and their relationship to baseline clinical and sociodemographic features, using linear regression. Symptomatic worsening was rare in the negative valence (0.4%) and positive valence (5.1%) domains, but more common in cognition (25.8%). Most diagnoses exhibited improvement in negative valence, which was associated with significant reduction in readmission risk. Despite generally brief hospital stays, we detected reduction across multiple symptom domains, with greatest improvement in negative symptoms, and greatest probability of worsening in cognitive symptoms. This approach should facilitate investigations of other features or interventions which may influence pace of clinical improvement.
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Corcoran CM, Cecchi GA. Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:770-779. [PMID: 32771179 DOI: 10.1016/j.bpsc.2020.06.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/09/2020] [Accepted: 06/09/2020] [Indexed: 01/12/2023]
Abstract
Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents "big data" at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.
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Affiliation(s)
- Cheryl Mary Corcoran
- Icahn School of Medicine at Mount Sinai, New York; James J. Peters Veterans Administration Medical Center, Bronx.
| | - Guillermo A Cecchi
- Thomas J. Watson Research Center, IBM Corporation, Yorktown Heights, New York
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Castro VM, Perlis RH. Electronic Health Record Documentation of Psychiatric Assessments in Massachusetts Emergency Department and Outpatient Settings During the Coronavirus Disease 2019 (COVID-19) Pandemic. JAMA Netw Open 2020; 3:e2011346. [PMID: 32511718 PMCID: PMC7280952 DOI: 10.1001/jamanetworkopen.2020.11346] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
This cohort study investigates the documentation of psychiatric symptoms in narrative clinical notes as coronavirus disease 2019 (COVID-19) prevalence increased in eastern Massachusetts.
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Affiliation(s)
- Victor M. Castro
- Center for Quantitative Health, Division of Clinical Research, Massachusetts General Hospital, Boston
| | - Roy H. Perlis
- Center for Quantitative Health, Division of Clinical Research, Massachusetts General Hospital, Boston
- Associate Editor, JAMA Network Open
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Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. A Review of Challenges and Opportunities in Machine Learning for Health. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2020; 2020:191-200. [PMID: 32477638 PMCID: PMC7233077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.
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Affiliation(s)
| | | | | | | | - Irene Y Chen
- Massachusetts Institute of Technology, Cambridge, MA, USA
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Sonabend W. A, Pellegrini AM, Chan S, Brown HE, Rosenquist JN, Vuijk PJ, Doyle AE, Perlis RH, Cai T. Integrating questionnaire measures for transdiagnostic psychiatric phenotyping using word2vec. PLoS One 2020; 15:e0230663. [PMID: 32243452 PMCID: PMC7122719 DOI: 10.1371/journal.pone.0230663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 03/05/2020] [Indexed: 12/03/2022] Open
Abstract
Background Recent initiatives in psychiatry emphasize the utility of characterizing psychiatric symptoms in a multidimensional manner. However, strategies for applying standard self-report scales for multiaxial assessment have not been well-studied, particularly where the aim is to support both categorical and dimensional phenotypes. Methods We propose a method for applying natural language processing to derive dimensional measures of psychiatric symptoms from questionnaire data. We utilized nine self-report symptom measures drawn from a large cellular biobanking study that enrolled individuals with mood and psychotic disorders, as well as healthy controls. To summarize questionnaire results we used word embeddings, a technique to represent words as numeric vectors preserving semantic and syntactic meaning. A low-dimensional approximation to the embedding space was used to derive the proposed succinct summary of symptom profiles. To validate our embedding-based disease profiles, these were compared to presence or absence of axis I diagnoses derived from structured clinical interview, and to objective neurocognitive testing. Results Unsupervised and supervised classification to distinguish presence/absence of axis I disorders using survey-level embeddings remained discriminative, with area under the receiver operating characteristic curve up to 0.85, 95% confidence interval (CI) (0.74,0.91) using Gaussian mixture modeling, and cross-validated area under the receiver operating characteristic curve 0.91, 95% CI (0.88,0.94) using logistic regression. Derived symptom measures and estimated Research Domain Criteria scores also associated significantly with performance on neurocognitive tests. Conclusions Our results support the potential utility of deriving dimensional phenotypic measures in psychiatric illness through the use of word embeddings, while illustrating the challenges in identifying truly orthogonal dimensions.
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Affiliation(s)
- Aaron Sonabend W.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Amelia M. Pellegrini
- Center for Quantitative Health, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Stephanie Chan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Hannah E. Brown
- Center for Quantitative Health, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Psychiatry, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - James N. Rosenquist
- Center for Quantitative Health, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Pieter J. Vuijk
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Alysa E. Doyle
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Roy H. Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (RHP); (TC)
| | - Tianxi Cai
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (RHP); (TC)
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McCoy TH, Han L, Pellegrini AM, Tanzi RE, Berretta S, Perlis RH. Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study. Alzheimers Dement 2020; 16:531-540. [PMID: 31859230 DOI: 10.1016/j.jalz.2019.09.084] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION Preventing dementia, or modifying disease course, requires identification of presymptomatic or minimally symptomatic high-risk individuals. METHODS We used longitudinal electronic health records from two large academic medical centers and applied a validated natural language processing tool to estimate cognitive symptomatology. We used survival analysis to examine the association of cognitive symptoms with incident dementia diagnosis during up to 8 years of follow-up. RESULTS Among 267,855 hospitalized patients with 1,251,858 patient years of follow-up data, 6516 (2.4%) received a new diagnosis of dementia. In competing risk regression, an increasing cognitive symptom score was associated with earlier dementia diagnosis (HR 1.63; 1.54-1.72). Similar results were observed in the second hospital system and in subgroup analysis of younger and older patients. DISCUSSION A cognitive symptom measure identified in discharge notes facilitated stratification of risk for dementia up to 8 years before diagnosis.
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Affiliation(s)
- Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA, USA
| | - Larry Han
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Amelia M Pellegrini
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, MassGeneral Institute for Neurodegenerative Disease, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sabina Berretta
- Translational Neuroscience Lab., Basic Neuroscience Division, McLean Hospital, Belmont, MA, USA
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA, USA
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Xu Z, Chou J, Zhang XS, Luo Y, Isakova T, Adekkanattu P, Ancker JS, Jiang G, Kiefer RC, Pacheco JA, Rasmussen LV, Pathak J, Wang F. Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks. J Biomed Inform 2020; 102:103361. [PMID: 31911172 DOI: 10.1016/j.jbi.2019.103361] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 11/18/2019] [Accepted: 12/16/2019] [Indexed: 01/08/2023]
Abstract
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions. This study used a memory network-based deep learning approach to discover AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis. We leveraged a real world critical care EHR corpus including 37,486 ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype I is with an average age of 63.03±17.25 years, and is characterized by mild loss of kidney excretory function (Serum Creatinine (SCr) 1.55±0.34 mg/dL, estimated Glomerular Filtration Rate Test (eGFR) 107.65±54.98 mL/min/1.73 m2). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81±10.43 years, and was characterized by severe loss of kidney excretory function (SCr 1.96±0.49 mg/dL, eGFR 82.19±55.92 mL/min/1.73 m2). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07±11.32 years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr 1.69±0.32 mg/dL, eGFR 93.97±56.53 mL/min/1.73 m2). Both SCr and eGFR are significantly different across the three sub-phenotypes with statistical testing plus postdoc analysis, and the conclusion still holds after age adjustment.
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Affiliation(s)
| | | | | | - Yuan Luo
- Northwestern University, Chicago, IL, USA
| | | | | | | | | | | | | | | | | | - Fei Wang
- Weill Cornell Medicine, New York, NY, USA.
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Barroilhet SA, Bieling AE, McCoy TH, Perlis RH. Association between DSM-5 and ICD-11 personality dimensional traits in a general medical cohort and readmission and mortality. Gen Hosp Psychiatry 2020; 64:63-67. [PMID: 32247933 PMCID: PMC9945433 DOI: 10.1016/j.genhosppsych.2020.01.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/22/2020] [Accepted: 01/23/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND Personality has long been studied as a factor associated with health outcomes. Investigations of large, generalizable clinical cohorts are limited by variations in personality diagnostic methodologies and difficulties with long-term follow-up. METHODS Electronic health records of a cohort of patients admitted to a general hospital were characterized using a previously developed natural language processing tool for extracting DSM-5 and ICD-11 personality domains. We used Cox regression and Fine-Gray competing risk survival to analyze the relationships between these personality estimates, sociodemographic features, and risk of readmission and mortality. RESULTS Among 12,274 patients, 2379 deaths occurred in the course of 61,761 patient-years at risk, with 19,985 admissions during follow-up. Detachment was the most common personality feature. Presence of disinhibition was independently associated with a higher mortality risk, while anankastic traits were associated with a lower mortality risk. Increased likelihood of readmission was predicted by detachment, while decreased likelihood of readmission was associated with disinhibition and psychoticism traits. CONCLUSIONS Personality features can be identified from electronic health records and are associated with readmission and mortality risk. Developing treatment strategies that target patients with higher personality symptom burden in specific dimensions could enable more efficient and focused interventions.
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Affiliation(s)
- Sergio A. Barroilhet
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA,Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, USA,University Psychiatric Clinic, University of Chile Clinical Hospital, Santiago, Chile
| | - Alexandra E. Bieling
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Thomas H. McCoy
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA,Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Roy H. Perlis
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA,Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, USA
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Corcoran CM, Benavides C, Cecchi G. Natural Language Processing: Opportunities and Challenges for Patients, Providers, and Hospital Systems. Psychiatr Ann 2019. [DOI: 10.3928/00485713-20190411-01] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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McCoy TH, Pellegrini AM, Perlis RH. Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death. Depress Anxiety 2019; 36:392-399. [PMID: 30710497 PMCID: PMC6488379 DOI: 10.1002/da.22882] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 12/10/2018] [Accepted: 01/12/2019] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering clinical phenomenology improves prediction of longer term outcomes remains understudied. Hospital discharge provides an opportunity to stratify risk using readily available clinical records and details. METHODS We applied a validated natural language processing tool to generate estimated Research Domain Criteria (RDoC) scores for a cohort of 444,317 individuals drawn from 815,457 hospital discharges between 2005 and 2013. We used survival analysis to examine the association of this risk with suicide and accidental death, adjusted for sociodemographic features. RESULTS In adjusted models, symptoms in each of the five domains contributed to incremental risk (log rank P < 0.001), with greatest increase observed with positive valence. The contribution of each domain to risk was time dependent. CONCLUSIONS RDoC symptom scores parsed from clinical documentation are associated with suicide and illustrates that multiple domains contribute to risk in a time-varying fashion.
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Affiliation(s)
- Thomas H. McCoy
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA, USA, Department of Psychiatry, Harvard Medical School, Boston, MA, USA, Department of Medicine, Harvard Medical School, Boston, MA, USA, Correspondence:Thomas H. McCoy, Jr. MD, Massachusetts General Hospital, 185 Cambridge Street, 6 Floor, Boston, MA 02114, Ph 617-643-6310, Fax 617-726-7541,
| | | | - Roy H. Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA, USA, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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Edgcomb JB, Zima B. Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research. Psychiatr Serv 2019; 70:346-349. [PMID: 30784377 DOI: 10.1176/appi.ps.201800401] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
An unprecedented amount of clinical information is now available via electronic health records (EHRs). These massive data sets have stimulated opportunities to adapt computational approaches to track and identify target areas for quality improvement in mental health care. In this column, three key areas of EHR data science are described: EHR phenotyping, natural language processing, and predictive modeling. For each of these computational approaches, case examples are provided to illustrate their role in mental health services research. Together, adaptation of these methods underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.
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Affiliation(s)
- Juliet Beni Edgcomb
- Department of Psychiatry and Behavioral Sciences (Edgcomb, Zima) and Center for Health Services and Society (Zima), University of California, Los Angeles, Los Angeles
| | - Bonnie Zima
- Department of Psychiatry and Behavioral Sciences (Edgcomb, Zima) and Center for Health Services and Society (Zima), University of California, Los Angeles, Los Angeles
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McCoy TH, Wiste AK, Doyle AE, Pellegrini AM, Perlis RH. Association between child psychiatric emergency room outcomes and dimensions of psychopathology. Gen Hosp Psychiatry 2019; 59:1-6. [PMID: 31034963 PMCID: PMC7392621 DOI: 10.1016/j.genhosppsych.2019.04.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 04/08/2019] [Accepted: 04/15/2019] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To determine the degree to which dimensional psychopathology predicts length of stay in an emergency department (ED) and need for hospital admission among children with psychiatric complaints. METHOD Electronic health records of children age 4-17 years who presented to the ED of a large academic medical center were analyzed using a natural language processing tool to estimate Research Domain Criteria (RDoC) symptom scores. These scores' association with length of stay and probability of admission versus discharge to home were evaluated. RESULTS We identified 3061 children and adolescents who presented to the ED and were evaluated by the psychiatry service between November 2008 and March 2015. Median length of stay was 7.8 h (interquartile range 5.2-14.3 h) and 1696 (55.4%) were admitted to the hospital. Higher estimated RDoC arousal, cognitive, positive, and social domain scores were associated with increased length of stay in multiple regression models, adjusted for age, sex, race, private insurance, voluntary admission, and diagnostic categories. In similarly adjusted models, odds of hospital admission were increased by higher RDoC arousal and cognitive domain scores and decreased by higher negative domain scores. CONCLUSIONS A natural language processing tool to characterize dimensional psychopathology identified features associated with differential outcomes in children in the psychiatric ED, most notably symptoms reflecting arousal and cognitive function. Methodologically, this in silico approach to risk stratification should facilitate precision psychiatry in children within the emergency setting.
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Affiliation(s)
- Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02114, USA; Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
| | - Anna K Wiste
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02114, USA
| | - Alysa E Doyle
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02114, USA; Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Amelia M Pellegrini
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02114, USA
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Gordon DD, Patel I, Pellegrini AM, Perlis RH. Prevalence and Nature of Financial Considerations Documented in Narrative Clinical Records in Intensive Care Units. JAMA Netw Open 2018; 1:e184178. [PMID: 30646344 PMCID: PMC6324587 DOI: 10.1001/jamanetworkopen.2018.4178] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 09/03/2018] [Indexed: 11/14/2022] Open
Abstract
Importance The extent to which financial considerations alter intensive care unit (ICU) decision making is poorly understood. Objectives To characterize the prevalence and nature of financial considerations documented in narrative clinical records and their association with patient-level demographic and clinical features. Design, Setting, and Participants In silico cohort study applying natural language processing to narrative notes from the Medical Information Mart for Intensive Care (MIMIC-III) study. Data from all individuals hospitalized between June 1, 2001, and October 31, 2012, in the ICU of Beth Israel Deaconess Medical Center were analyzed from April 1 to April 30, 2018. Main Outcomes and Measure Presence of financial considerations in narrative clinical notes. Results Among 46 146 index ICU admissions, 1936 patients (4.2%) were identified with at least 1 note reflecting financial considerations during the ICU stay. Of these 1936 patients, 1135 (58.6%) were male, with a mean (SD) age of 38.8 (28.4) years and mean (SD) length of stay of 21.7 (27.1) days. Among the remaining 44 210 admissions in the cohort, 24 780 (56.1%) were male, with a mean (SD) age of 48.6 (32.1) years and mean (SD) length of stay of 9.2 (11.4) days. Among the 46 146 admissions, 142 (0.3%) included notes describing a change in the discharge plan, 142 (0.3%) describing a change in the treatment plan, and 303 (0.7%) describing a change in medication or previous nonadherence to medication associated with financial considerations. In logistic regression models adjusted for age, sex, marital status, and insurance type, longer hospital stays were significantly associated with the presence of financial notes (odds ratio, 1.01; 95% CI, 1.01-1.01). Conclusions and Relevance In this study, among patients in the ICU, clinical notes document the association of financial considerations with care decisions. Although such notes likely underestimate the frequency of such considerations, they highlight the need to develop better systematic approaches to understanding how financial constraints may alter care decisions in US health systems.
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Affiliation(s)
- Deborah D. Gordon
- Mossavar-Rahmani Center for Business and Government, Harvard Kennedy School, Cambridge, Massachusetts
| | - Ihsaan Patel
- Mossavar-Rahmani Center for Business and Government, Harvard Kennedy School, Cambridge, Massachusetts
| | - Amelia M. Pellegrini
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston
| | - Roy H. Perlis
- Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
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McCoy TH, Castro VM, Hart KL, Pellegrini AM, Yu S, Cai T, Perlis RH. Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records. Biol Psychiatry 2018; 83:1005-1011. [PMID: 29496196 PMCID: PMC5972060 DOI: 10.1016/j.biopsych.2017.12.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 11/17/2017] [Accepted: 12/07/2017] [Indexed: 10/17/2022]
Abstract
BACKGROUND Genetic studies of neuropsychiatric disease strongly suggest an overlap in liability. There are growing efforts to characterize these diseases dimensionally rather than categorically, but the extent to which such dimensional models correspond to biology is unknown. METHODS We applied a newly developed natural language processing method to extract five symptom dimensions based on the National Institute of Mental Health Research Domain Criteria definitions from narrative hospital discharge notes in a large biobank. We conducted a genome-wide association study to examine whether common variants were associated with each of these dimensions as quantitative traits. RESULTS Among 4687 individuals, loci in three of five domains exceeded a genome-wide threshold for statistical significance. These included a locus spanning the neocortical development genes RFPL3 and RFPL3S for arousal (p = 2.29 × 10-8) and one spanning the FPR3 gene for cognition (p = 3.22 × 10-8). CONCLUSIONS Natural language processing identifies dimensional phenotypes that may facilitate the discovery of common genetic variation that is relevant to psychopathology.
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Affiliation(s)
- Thomas H. McCoy
- Center for Quantitative Health and Department of Psychiatry, Simches Research Building, 6th Floor, 185 Cambridge Street, Massachusetts General Hospital and Harvard Medical School, Boston, MA,Correspondence: Thomas H. McCoy, Jr, MD, Massachusetts General Hospital, Simches Research Building, 6th Floor, Boston, MA 02114, 617-726-7426,
| | - Victor M. Castro
- Center for Quantitative Health and Department of Psychiatry, Simches Research Building, 6th Floor, 185 Cambridge Street, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Kamber L. Hart
- Center for Quantitative Health and Department of Psychiatry, Simches Research Building, 6th Floor, 185 Cambridge Street, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Amelia M. Pellegrini
- Center for Quantitative Health and Department of Psychiatry, Simches Research Building, 6th Floor, 185 Cambridge Street, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Sheng Yu
- Tsinghua University, 30 Shuangqing Rd, Haidian Qu, Beijing Shi, China, 100084
| | - Tianxi Cai
- Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115
| | - Roy H. Perlis
- Center for Quantitative Health and Department of Psychiatry, Simches Research Building, 6th Floor, 185 Cambridge Street, Massachusetts General Hospital and Harvard Medical School, Boston, MA
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