1
|
Mumtaz H, Saqib M, Jabeen S, Muneeb M, Mughal W, Sohail H, Safdar M, Mehmood Q, Khan MA, Ismail SM. Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review. Front Med (Lausanne) 2023; 10:1227168. [PMID: 37849490 PMCID: PMC10577305 DOI: 10.3389/fmed.2023.1227168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023] Open
Abstract
The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.
Collapse
Affiliation(s)
| | | | | | - Muhammad Muneeb
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Wajiha Mughal
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Hassan Sohail
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Myra Safdar
- Armed Forces Institute of Cardiology and National Institute of Heart Diseases (AFIC-NIHD), Rawalpindi, Pakistan
| | - Qasim Mehmood
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Muhammad Ahsan Khan
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | | |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
|
4
|
Montelongo M, Lee J, Poa E, Boland R, Rufino KA, Patriquin M, Oh H. A next-generation approach to mental health outcomes: Treatment, time, and trajectories. J Psychiatr Res 2023; 158:172-179. [PMID: 36586216 DOI: 10.1016/j.jpsychires.2022.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 11/18/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022]
Abstract
Over the last several decades, inpatient psychiatric length of stay (LOS) has been greatly reduced to the detriment of patients. Latent variable mixture modeling, can be used to improve the quality of care for patients by identifying unobserved subgroups and optimize treatment variables, including LOS. This study had three objectives (1) to replicate the findings made by Oh et al. in a distinct sample, (2) to examine demographic differences related to inpatient treatment trajectories, and (3) to relate additional variables to each trajectory. We collected data on six key mental illness factors and information on felonies, misdemeanors, history of stopping psychiatric medication and psychotherapy, length of time in psychotherapy, and the number of therapists and psychiatrists from 489 patients at an inpatient psychiatric hospital. We derived latent mental illness scores after applying growth mixture modeling to these data. We identified three distinct trajectories of mental illness change: High-Risk, Rapid Improvement (HR-RI), Low-Risk, Partial Response (LR-PR), and High-Risk, Gradual Improvement (HR-GI). The HR-GI group was more likely to have patients who were female, Asian, younger, Yearly Income (YI) <$20,000, that spent more time in psychotherapy throughout their life, and had the longest LOS while inpatient. The LR-PR group had was more likely to be male, Hispanic/Latino and multiracial, older, YI >$500,000, have a history of misdemeanors, and this group had the shortest LOS (p < .05). These findings replicate and extend our previous findings in Oh et al. (2020a) and highlight the clinical utility of agnostically determining the treatment trajectories.
Collapse
Affiliation(s)
| | - Jaehoon Lee
- Department of Educational Psychology, Leadership, and Counseling, College of Education, Texas Tech University, 3002 18th Street, Lubbock, TX, 79409, USA; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, 1977 Butler Blvd., Houston, TX, 77030, USA
| | - Edward Poa
- The Menninger Clinic, 12301 S Main St, Houston, TX, 77035, USA; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, 1977 Butler Blvd., Houston, TX, 77030, USA
| | - Robert Boland
- The Menninger Clinic, 12301 S Main St, Houston, TX, 77035, USA; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, 1977 Butler Blvd., Houston, TX, 77030, USA
| | - Katrina A Rufino
- The Menninger Clinic, 12301 S Main St, Houston, TX, 77035, USA; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, 1977 Butler Blvd., Houston, TX, 77030, USA; University of Houston Downtown, 1 Main St, Houston, TX, 77002, USA
| | - Michelle Patriquin
- The Menninger Clinic, 12301 S Main St, Houston, TX, 77035, USA; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, 1977 Butler Blvd., Houston, TX, 77030, USA
| | - Hyuntaek Oh
- The Menninger Clinic, 12301 S Main St, Houston, TX, 77035, USA; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, 1977 Butler Blvd., Houston, TX, 77030, USA.
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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)
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|