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Franco D'Souza R, Mathew M, Amanullah S, Edward Thornton J, Mishra V, E M, Louis Palatty P, Surapaneni KM. Navigating merits and limits on the current perspectives and ethical challenges in the utilization of artificial intelligence in psychiatry - An exploratory mixed methods study. Asian J Psychiatr 2024; 97:104067. [PMID: 38718518 DOI: 10.1016/j.ajp.2024.104067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND The integration of Artificial Intelligence (AI) in psychiatry presents opportunities for enhancing patient care but raises significant ethical concerns and challenges in clinical application. Addressing these challenges necessitates an informed and ethically aware psychiatric workforce capable of integrating AI into practice responsibly. METHODS A mixed-methods study was conducted to assess the outcomes of the "CONNECT with AI" - (Collaborative Opportunity to Navigate and Negotiate Ethical Challenges and Trials with Artificial Intelligence) workshop, aimed at exploring AI's ethical implications and applications in psychiatry. This workshop featured presentations, discussions, and scenario analyses focusing on AI's role in mental health care. Pre- and post-workshop questionnaires and focus group discussions evaluated participants' perspectives, and ethical understanding regarding AI in psychiatry. RESULTS Participants exhibited a cautious optimism towards AI, recognizing its potential to augment mental health care while expressing concerns over ethical usage, patient-doctor relationships, and AI's practical application in patient care. The workshop significantly improved participants' ethical understanding, highlighting a substantial knowledge gap and the need for further education in AI among psychiatrists. CONCLUSION The study underscores the necessity of continuous education and ethical guideline development for psychiatrists in the era of AI, emphasizing collaborative efforts in AI system design to ensure they meet clinical needs ethically and effectively. Future initiatives should aim to broaden psychiatrists' exposure to AI, fostering a deeper understanding and integration of AI technologies in psychiatric practice.
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Affiliation(s)
- Russell Franco D'Souza
- Department of Education, UNESCO Chair in Bioethics, Melbourne, Australia; Department of Organizational Psychological Medicine, International Institute of Organisational Psychological Medicine, 71 Cleeland Street, Dandenong Victoria, Melbourne 3175, Australia
| | - Mary Mathew
- Department of Pathology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Tiger Circle Road, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Shabbir Amanullah
- Division of Geriatric Psychiatry, Queen's University, Providence Care Hospital, 752 King Street West, Postal Bag 603 Kingston, ON K7L7X3, Canada
| | - Joseph Edward Thornton
- Department of Psychiatry, University of Florida College of Medicine, Gainesville, FL, USA
| | - Vedprakash Mishra
- School of Higher Education & Research, Datta Meghe Institute of Higher Education and Research (Deemed to be University), Nagpur, Maharashtra, India
| | - Mohandas E
- Department of Psychiatry, Sun Medical and Research Centre, Thrissur, Kerala 680 001, India
| | - Princy Louis Palatty
- Department of Pharmacology, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Elamakkara P.O., Kochi, Kerala 682 041, India
| | - Krishna Mohan Surapaneni
- Department of Biochemistry, Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai, Tamil Nadu 600 123, India; Department of Medical Education, Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai, Tamil Nadu 600 123, India.
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Wang Y, Yang Y, Liang C, Zhang H. Exploring the Roles of Key Mediators IKBKE and HSPA1A in Alzheimer's Disease and Hepatocellular Carcinoma through Bioinformatics Analysis. Int J Mol Sci 2024; 25:6934. [PMID: 39000042 PMCID: PMC11241202 DOI: 10.3390/ijms25136934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/18/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024] Open
Abstract
Recent studies have hinted at a potential link between Alzheimer's Disease (AD) and cancer. Thus, our study focused on finding genes common to AD and Liver Hepatocellular Carcinoma (LIHC), assessing their promise as diagnostic indicators and guiding future treatment approaches for both conditions. Our research utilized a broad methodology, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), gene enrichment analysis, Receiver Operating Characteristic (ROC) curves, and Kaplan-Meier plots, supplemented with immunohistochemistry data from the Human Protein Atlas (HPA) and machine learning techniques, to identify critical genes and significant pathways shared between AD and LIHC. Through differential gene expression analysis, WGCNA, and machine learning methods, we identified nine key genes associated with AD, which served as entry points for LIHC analysis. Subsequent analyses revealed IKBKE and HSPA1A as shared pivotal genes in patients with AD and LIHC, suggesting these genes as potential targets for intervention in both conditions. Our study indicates that IKBKE and HSPA1A could influence the onset and progression of AD and LIHC by modulating the infiltration levels of immune cells. This lays a foundation for future research into targeted therapies based on their shared mechanisms.
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Affiliation(s)
- Yiying Wang
- Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, Ministry of Education, The Key Laboratory of New Drug Pharmacology and Toxicology, Collaborative Innovation Center of Hebei Province for Mechanism, Diagnosis and Treatment of Neuropsychiatric Diseases, Hebei Medical University, Shijiazhuang 050017, China
| | - Yakun Yang
- Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, Ministry of Education, The Key Laboratory of New Drug Pharmacology and Toxicology, Collaborative Innovation Center of Hebei Province for Mechanism, Diagnosis and Treatment of Neuropsychiatric Diseases, Hebei Medical University, Shijiazhuang 050017, China
| | - Ce Liang
- Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, Ministry of Education, The Key Laboratory of New Drug Pharmacology and Toxicology, Collaborative Innovation Center of Hebei Province for Mechanism, Diagnosis and Treatment of Neuropsychiatric Diseases, Hebei Medical University, Shijiazhuang 050017, China
| | - Hailin Zhang
- Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, Ministry of Education, The Key Laboratory of New Drug Pharmacology and Toxicology, Collaborative Innovation Center of Hebei Province for Mechanism, Diagnosis and Treatment of Neuropsychiatric Diseases, Hebei Medical University, Shijiazhuang 050017, China
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Iqbal S, Qureshi AN, Alhussein M, Aurangzeb K, Choudhry IA, Anwar MS. Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification. Front Comput Neurosci 2024; 18:1423051. [PMID: 38978524 PMCID: PMC11228303 DOI: 10.3389/fncom.2024.1423051] [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: 04/25/2024] [Accepted: 06/06/2024] [Indexed: 07/10/2024] Open
Abstract
The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan
| | - Adnan N. Qureshi
- Faculty of Arts, Society, and Professional Studies, Newman University, Birmingham, United Kingdom
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Imran Arshad Choudhry
- Department of Computer Science, Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan
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4
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Stolfi F, Abreu H, Sinella R, Nembrini S, Centonze S, Landra V, Brasso C, Cappellano G, Rocca P, Chiocchetti A. Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine. Front Psychiatry 2024; 15:1422939. [PMID: 38938457 PMCID: PMC11210496 DOI: 10.3389/fpsyt.2024.1422939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 05/28/2024] [Indexed: 06/29/2024] Open
Abstract
Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.
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Affiliation(s)
- Fabiola Stolfi
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Hugo Abreu
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Riccardo Sinella
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Sara Nembrini
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Sara Centonze
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Virginia Landra
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Giuseppe Cappellano
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Paola Rocca
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Annalisa Chiocchetti
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
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5
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Monosov IE, Zimmermann J, Frank MJ, Mathis MW, Baker JT. Ethological computational psychiatry: Challenges and opportunities. Curr Opin Neurobiol 2024; 86:102881. [PMID: 38696972 PMCID: PMC11162904 DOI: 10.1016/j.conb.2024.102881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 05/04/2024]
Abstract
Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.
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Affiliation(s)
- Ilya E. Monosov
- Departments of Neuroscience, Biomedical Engineering, Electrical Engineering, and Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Michael J. Frank
- Carney Center for Computational Brain Science, Brown University, Providence, RI, USA
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M. Abdelhaleem Ali A, M. Alrobaian M. Strengths and weaknesses of current and future prospects of artificial intelligence-mounted technologies applied in the development of pharmaceutical products and services. Saudi Pharm J 2024; 32:102043. [PMID: 38585196 PMCID: PMC10997913 DOI: 10.1016/j.jsps.2024.102043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/18/2024] [Indexed: 04/09/2024] Open
Abstract
Starting from drug discovery, through research and development, to clinical trials and FDA approval, artificial intelligence (AI) plays a vital role in planning, developing, assessing modelling, and optimization of product attributes. In recent decades, machine-learning algorithms integrated into artificial neural networks, neuro-fuzzy logic and decision trees have been applied to tremendous domains related to drug formulation development. Optimized formulations were transformed from lab to market based on optimized properties derived from AI Technologies. Research and development in pharmaceutical industry rely upon computer-driven equipment and machine learning technology to extract data, perform simulations, modelling, and optimization to get optimum solutions. Merging AI technologies in various steps of pharmaceutical manufacture is a major challenge due to lack of in-house technologies. In silico studies based on artificial intelligence are widely applied as effective tools to screen the market needs of medications and pharmaceutical services through inspecting scientific literature and prioritizing medicines for specific illnesses or a particular patient. Specialized personnel who excel in scientific and data science with analytical knowledge are essential for transformation to smart manufacturing and offering services. However, privacy, cybersecurity, AI-dependent unemployment, and ownership rights of AI technologies require proper regulations to gain the benefits and minimize the drawbacks.
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Affiliation(s)
- Ahmed M. Abdelhaleem Ali
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P. O. Box 11099, P. Code 21944, Taif, Saudi Arabia
| | - Majed M. Alrobaian
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P. O. Box 11099, P. Code 21944, Taif, Saudi Arabia
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7
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Ho CSH, Tan TWK, Khoe HCH, Chan YL, Tay GWN, Tang TB. Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder. J Clin Med 2024; 13:1222. [PMID: 38592058 PMCID: PMC10931723 DOI: 10.3390/jcm13051222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models. Methods: In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. Serum amino acid profiling was conducted by means of chromatography-mass spectrometry. A total of 21 metabolites were analysed, with 17 from a preset amino acid panel and the remaining 4 from a preset kynurenine panel. Logistic regression was applied to differentiate MDD patients from HCs. Results: The best-performing model utilised both feature selection and hyperparameter optimisation and yielded a moderate area under the receiver operating curve (AUC) classification value of 0.76 on the testing data. The top five metabolites identified as potential biomarkers for MDD were 3-hydroxy-kynurenine, valine, kynurenine, glutamic acid, and xanthurenic acid. Conclusions: Our study highlights the potential of using an interpretable machine learning analysis model based on amino acids to aid and increase the diagnostic accuracy of MDD in clinical practice.
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Affiliation(s)
- Cyrus Su Hui Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore;
| | - Trevor Wei Kiat Tan
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore;
- Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Howard Cai Hao Khoe
- Singapore Psychiatry Residency, National Healthcare Group, Singapore 308433, Singapore;
| | - Yee Ling Chan
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Perak, Malaysia; (Y.L.C.); (T.B.T.)
| | - Gabrielle Wann Nii Tay
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore;
| | - Tong Boon Tang
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Perak, Malaysia; (Y.L.C.); (T.B.T.)
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8
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Williams LM, Carpenter WT, Carretta C, Papanastasiou E, Vaidyanathan U. Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice. CNS Spectr 2024; 29:26-39. [PMID: 37675453 DOI: 10.1017/s1092852923002420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.
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Affiliation(s)
- Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Evangelos Papanastasiou
- Boehringer Ingelheim Pharma GmbH & Co, Ingelheim am Rhein, Rhineland-Palatinate, Germany
- HMNC Holding GmbH, Wilhelm-Wagenfeld-Strasse 20, 80807Munich, Bavaria, Germany
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Aiumtrakul N, Thongprayoon C, Arayangkool C, Vo KB, Wannaphut C, Suppadungsuk S, Krisanapan P, Garcia Valencia OA, Qureshi F, Miao J, Cheungpasitporn W. Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention. J Pers Med 2024; 14:107. [PMID: 38248809 PMCID: PMC10817681 DOI: 10.3390/jpm14010107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024] Open
Abstract
Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5-8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.
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Affiliation(s)
- Noppawit Aiumtrakul
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; (N.A.); (C.A.); (K.B.V.); (C.W.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
| | - Chinnawat Arayangkool
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; (N.A.); (C.A.); (K.B.V.); (C.W.)
| | - Kristine B. Vo
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; (N.A.); (C.A.); (K.B.V.); (C.W.)
| | - Chalothorn Wannaphut
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; (N.A.); (C.A.); (K.B.V.); (C.W.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.S.); (O.A.G.V.); (F.Q.); (J.M.); (W.C.)
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Salloum-Asfar S, Zawia N, Abdulla SA. Retracing our steps: A review on autism research in children, its limitation and impending pharmacological interventions. Pharmacol Ther 2024; 253:108564. [PMID: 38008401 DOI: 10.1016/j.pharmthera.2023.108564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 11/28/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by three core impairments: impaired communication, impaired reciprocal social interaction, and restricted, repetitive, and stereotypical behavior patterns. Spectrum refers to the heterogeneity of presentation, severity of symptoms, and medical comorbidities associated with ASD. Among the most common underlying medical conditions are attention-deficit/hyperactivity disorder (ADHD), anxiety, depression, epilepsy, digestive disorders, metabolic disorders, and immune disorders. At present, in the absence of an objective and accurate diagnosis of ASD, such as a blood test, pharmacological management remains a challenge. There are no approved medications to treat the core symptoms of the disorder and behavioral interventions are typically used as first line treatment. Additionally, psychotropic drugs with different mechanisms of action have been approved to reduce associated symptoms and comorbidities, including aripiprazole, risperidone, and haloperidol for irritability and aggression, methylphenidate, atomoxetine, clonidine, and guanfacine for ADHD, and melatonin for sleep disturbances. The purpose of this review is to emphasize that it is imperative to develop objective, personalized diagnostic kits in order to tailor and individualize treatment strategies, as well as to describe the current pharmacological management options available in clinical practice and new prospects that may be helpful in managing ASD's core symptoms.
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Affiliation(s)
- Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
| | - Nasser Zawia
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Sara A Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
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11
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Li C, Ye G, Jiang Y, Wang Z, Yu H, Yang M. Artificial Intelligence in battling infectious diseases: A transformative role. J Med Virol 2024; 96:e29355. [PMID: 38179882 DOI: 10.1002/jmv.29355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/01/2023] [Accepted: 12/17/2023] [Indexed: 01/06/2024]
Abstract
It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.
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Affiliation(s)
- Chunhui Li
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Guoguo Ye
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, The Third People's Hospital of Shenzhen, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Yinghan Jiang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Zhiming Wang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Haiyang Yu
- Hangzhou Yalla Information Technology Service Co., Ltd., Hangzhou, People's Republic of China
| | - Minghui Yang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
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12
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Monteith S, Glenn T, Geddes JR, Achtyes ED, Whybrow PC, Bauer M. Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review. PHARMACOPSYCHIATRY 2023; 56:209-213. [PMID: 37643732 DOI: 10.1055/a-2142-9325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications. Human understanding of how a clinical AI prediction tool performs in routine and exceptional situations is fundamental to successful implementation. Physicians must be involved in all aspects of the selection, implementation, and ongoing product monitoring of clinical AI prediction tools.
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Affiliation(s)
- Scott Monteith
- Department of Psychiatry, Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, USA
| | - Tasha Glenn
- ChronoRecord Association, Fullerton, CA, USA
| | - John R Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Eric D Achtyes
- Department of Psychiatry, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, USA
| | - Peter C Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
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13
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Bhujbal SS, Kad MM, Patole VC. Recent diagnostic techniques for the detection of Alzheimer's disease: a short review. Ir J Med Sci 2023; 192:2417-2426. [PMID: 36525239 DOI: 10.1007/s11845-022-03244-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022]
Abstract
Alzheimer's disease (AD) is a neurological condition that affects millions of individuals around the world and for which there are few effective therapies. Dementia is characterized by the formation of senile plaques and neurofibrillary tangles, which is followed by neurotoxicity, which results in memory loss and mortality. Pathogenesis occurs several years before the onset of disease. As the disease-modifying drugs are most effective in the early stages of Alzheimer's disease, biomarkers for early detection of disease and their development are crucial. This review discusses the diagnostic utility, benefits, and limitations of traditional techniques such as neuroimaging, cognitive testing, positron emission tomography, and biomarkers, as well as the novel techniques such as artificial intelligence, machine learning, immunotherapy, and blood test approaches for early detection, understanding, and treatment of AD.
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Affiliation(s)
- Santosh S Bhujbal
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India.
| | - Minal M Kad
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India
| | - Vinita C Patole
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India
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14
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Cembrowska-Lech D, Krzemińska A, Miller T, Nowakowska A, Adamski C, Radaczyńska M, Mikiciuk G, Mikiciuk M. An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture. BIOLOGY 2023; 12:1298. [PMID: 37887008 PMCID: PMC10603917 DOI: 10.3390/biology12101298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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Affiliation(s)
- Danuta Cembrowska-Lech
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
| | - Adrianna Krzemińska
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | - Tymoteusz Miller
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
| | - Anna Nowakowska
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
| | - Cezary Adamski
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | | | - Grzegorz Mikiciuk
- Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Małgorzata Mikiciuk
- Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
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15
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Giansanti D. Precision Medicine 2.0: How Digital Health and AI Are Changing the Game. J Pers Med 2023; 13:1057. [PMID: 37511670 PMCID: PMC10381472 DOI: 10.3390/jpm13071057] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 07/30/2023] Open
Abstract
In the era of rapid IT developments, the health domain is undergoing a considerable transformation [...].
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16
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Holzinger A, Keiblinger K, Holub P, Zatloukal K, Müller H. AI for life: Trends in artificial intelligence for biotechnology. N Biotechnol 2023; 74:16-24. [PMID: 36754147 DOI: 10.1016/j.nbt.2023.02.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/05/2023] [Accepted: 02/05/2023] [Indexed: 02/08/2023]
Abstract
Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.
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Affiliation(s)
- Andreas Holzinger
- University of Natural Resources and Life Sciences Vienna, Austria; Medical University Graz, Austria; Alberta Machine Intelligence Institute Edmonton, Canada.
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17
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Salem H, Huynh T, Topolski N, Mwangi B, Trivedi MH, Soares JC, Rush AJ, Selvaraj S. Temporal multi-step predictive modeling of remission in major depressive disorder using early stage treatment data; STAR*D based machine learning approach. J Affect Disord 2023; 324:286-293. [PMID: 36584711 PMCID: PMC9863277 DOI: 10.1016/j.jad.2022.12.076] [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: 06/07/2022] [Revised: 12/10/2022] [Accepted: 12/18/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND Artificial intelligence is currently being used to facilitate early disease detection, better understand disease progression, optimize medication/treatment dosages, and uncover promising novel treatments and potential outcomes. METHODS Utilizing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we built a machine learning model to predict depression remission rates using same clinical data as features for each of the first three antidepressant treatment steps in STAR*D. We only used early treatment data (baseline and first follow up) in each STAR*D step to temporally analyze predictive features of remission at the end of the step. RESULTS Our model showed significant prediction performance across the three treatment steps, At step 1, Model accuracy was 66 %; sensitivity-65 %, specificity-67 %, positive predictive value (PPV)-65.5 %, and negative predictive value (NPV)-66.6 %. At step 2, model accuracy was 71.3 %, sensitivity-74.3 %, specificity-69 %, PPV-64.5 %, and NPV-77.9 %. At step 3, accuracy reached 84.6 %; sensitivity-69 %, specificity-88.8 %, PPV-67 %, and NPV-91.1 %. Across all three steps, the early Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) scores were key elements in predicting the final treatment outcome. The model also identified key sociodemographic factors that predicted treatment remission at different steps. LIMITATIONS The retrospective design, lack of replication in an independent dataset, and the use of "a complete case analysis" model in our analysis. CONCLUSIONS This proof-of-concept study showed that using early treatment data, multi-step temporal prediction of depressive symptom remission results in clinically useful accuracy rates. Whether these predictive models are generalizable deserves further study.
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Affiliation(s)
- Haitham Salem
- Department of Psychiatry and Human Behavior (DPHB), Warren Alpert School of Medicine, Brown University, Providence, RI, USA
| | - Tung Huynh
- Louis Faillace Department of Psychiatry and Behavioral Science, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA
| | - Natasha Topolski
- Louis Faillace Department of Psychiatry and Behavioral Science, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA
| | - Benson Mwangi
- Louis Faillace Department of Psychiatry and Behavioral Science, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jair C Soares
- Louis Faillace Department of Psychiatry and Behavioral Science, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA
| | - A John Rush
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Professor Emeritus, Duke-National University of Singapore, Singapore, Singapore
| | - Sudhakar Selvaraj
- Louis Faillace Department of Psychiatry and Behavioral Science, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA.
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18
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Pharmacokinetic Markers of Clinical Outcomes in Severe Mental Illness: A Systematic Review. Int J Mol Sci 2023; 24:ijms24054776. [PMID: 36902205 PMCID: PMC10003720 DOI: 10.3390/ijms24054776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/20/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
The term severe mental illness (SMI) encompasses those psychiatric disorders exerting the highest clinical burden and socio-economic impact on the affected individuals and their communities. Pharmacogenomic (PGx) approaches hold great promise in personalizing treatment selection and clinical outcomes, possibly reducing the burden of SMI. Here, we sought to review the literature in the field, focusing on PGx testing and particularly on pharmacokinetic markers. We performed a systematic review on PUBMED/Medline, Web of Science, and Scopus. The last search was performed on the 17 September 2022, and further augmented with a comprehensive pearl-growing strategy. In total, 1979 records were screened, and after duplicate removal, 587 unique records were screened by at least 2 independent reviewers. Ultimately, forty-two articles were included in the qualitative analysis, eleven randomized controlled trials and thirty-one nonrandomized studies. The observed lack of standardization in PGx tests, population selection, and tested outcomes limit the overall interpretation of the available evidence. A growing body of evidence suggests that PGx testing might be cost-effective in specific settings and may modestly improve clinical outcomes. More efforts need to be directed toward improving PGx standardization, knowledge for all stakeholders, and clinical practice guidelines for screening recommendations.
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19
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Baker MR, Padmaja DL, Puviarasi R, Mann S, Panduro-Ramirez J, Tiwari M, Samori IA. Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM). COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6501975. [PMID: 35465018 PMCID: PMC9023163 DOI: 10.1155/2022/6501975] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/09/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022]
Abstract
Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists' critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.
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Affiliation(s)
- Mohammed Rashad Baker
- Department of Computer Techniques Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
| | - D. Lakshmi Padmaja
- Department of Information Technology, Anurag University, Hyderabad, Telangana State, India
| | - R. Puviarasi
- Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India
| | - Suman Mann
- Information Technology Department, Maharaja Surajmal Institute of Technology, New Delhi, India
| | | | - Mohit Tiwari
- Department of Computer Science and Engineering, Bharati Vidyapeeth's College of Engineering, A-4, Rohtak Road, Paschim Vihar, Delhi, India
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20
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Guo D, Guo Y, Xing Y. Data on the Impact of Epidemic on Nursing Staff's Mental Health in the Context of Wireless Network. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3413815. [PMID: 35432842 PMCID: PMC9010164 DOI: 10.1155/2022/3413815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 01/29/2022] [Accepted: 02/05/2022] [Indexed: 11/18/2022]
Abstract
The research was aimed to analyze the impact of epidemic pneumonia on nursing personnel's mental health under wireless network background and to improve the selection of random forest classification (RFC) algorithm parameters by the whale optimization algorithm (WOA). Besides, a total of 148 in-service nursing personnel were selected as the research objects, and 148 questionnaires were recycled effectively. The collected data were analyzed by the improved RFC algorithm. In addition, the research investigated the impacts of demographic factors on nursing personnel's mental health by the one-way variance method. The results demonstrated that the accuracy of the improved algorithm in training samples and test samples reached 83.3% and 81.6%, respectively, both of which were obviously higher than those of support vector machine (SVM) (80.1% and 79.3%, respectively) and back-propagation neural network (BPNN) (78.23% and 77.9%, respectively), and the differences showed statistical meanings (P < 0.05). The Patient Health Questionnaire-9 (PHQ-9) showed that the depression levels of 9.46% of the included personnel were above moderate. The Generalized Anxiety Disorder (GAD-7) demonstrated that the anxiety levels of 3.38% of the included personnel were above moderate. The insomnia severity index (ISI) indicated that the insomnia levels of 3.38% of the included personnel were above moderate. The average score of male personnel (3.65) was obviously lower than that of female personnel (3.71). Besides, the average scale score of married personnel (3.78) was significantly higher than that of unmarried personnel (3.65). The average scale scores of personnel with bachelor's (3.66) and master's degrees (3.62) were obviously lower than those of personnel with junior college (3.77) and technical secondary school (3.75) diplomas. The average scale score of personnel with over 5-year work experience (3.68) was significantly lower than that of personnel working for less than five years (3.72). The average scale score of personnel with experience in responding to public emergencies (3.65) was obviously lower than that of personnel without related experience (3.74). The differences all showed statistical meaning (P < 0.05). The results of this research revealed that the accuracy of the improved RFC algorithm was remarkably higher than that of the SVM and BPNN algorithms. Furthermore, many nursing personnel suffered from mental diseases at different levels with the impact of the epidemic. Gender, marital status, education level, and experience in responding to public emergencies were the main factors affecting nursing personnel's mental health.
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Affiliation(s)
- Dan Guo
- Department of Operating Room, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China
| | - Yi Guo
- Department of Haikou Administrative Center Outpatient, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China
| | - YanJi Xing
- Department of Health Medicine, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou 570208, Hainan, China
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21
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Kambeitz-Ilankovic L, Koutsouleris N, Upthegrove R. The potential of precision psychiatry: what is in reach? Br J Psychiatry 2022; 220:175-178. [PMID: 35354501 DOI: 10.1192/bjp.2022.23] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.
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Affiliation(s)
- Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Germany; and Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; and Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Rachel Upthegrove
- Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, UK; and Institute for Mental Health, University of Birmingham, UK
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22
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Abstract
Disruptive innovation is a cornerstone of various disciplines, particularly in the business world, where paradigm‐altering approaches are often lauded. As a construct, disruptive psychiatry can be considered to embody such an approach by the pursuit of innovations within the field which test boundaries and shake up the status quo. Given the importance of addressing the current mental health pandemic, and the deficit of highly effective pharmacological treatments for various conditions, innovative disruptive thinking is required. Significant promise exists via the provision of potentially more effective innovative therapeutic options, including digital interventions, psychedelic medicines, microbiome‐altering treatments, brain stimulation technology, and nanotechnology‐based delivery systems. These approaches may be further advanced by individualization of the treatments using computational technology (including quantum computing, machine learning, and artificial intelligence) and genomics.
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Affiliation(s)
- Jerome Sarris
- NICM Health Research Institute, Western Sydney University, Westmead, New South Wales, Australia.,The Florey Institute of Neuroscience and Mental Health & Department of Psychiatry, Melbourne University, Melbourne, Victoria, Australia.,Psychae Institute, Melbourne, Victoria, Australia
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23
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Auwerx C, Sadler MC, Reymond A, Kutalik Z. From Pharmacogenetics to Pharmaco-Omics:Milestones and Future Directions. HGG ADVANCES 2022; 3:100100. [PMID: 35373152 PMCID: PMC8971318 DOI: 10.1016/j.xhgg.2022.100100] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.
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Lin E, Lin CH, Lane HY. De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update. J Chem Inf Model 2022; 62:761-774. [DOI: 10.1021/acs.jcim.1c01361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, United States
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195, United States
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
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25
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Lejeune A, Robaglia BM, Walter M, Berrouiguet S, Lemey C. Use of social media data to diagnose and monitor psychotic disorders: systematic review and perspectives (Preprint). J Med Internet Res 2022; 24:e36986. [PMID: 36066938 PMCID: PMC9490531 DOI: 10.2196/36986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 05/30/2022] [Accepted: 06/07/2022] [Indexed: 01/10/2023] Open
Abstract
Background Schizophrenia is a disease associated with high burden, and improvement in care is necessary. Artificial intelligence (AI) has been used to diagnose several medical conditions as well as psychiatric disorders. However, this technology requires large amounts of data to be efficient. Social media data could be used to improve diagnostic capabilities. Objective The objective of our study is to analyze the current capabilities of AI to use social media data as a diagnostic tool for psychotic disorders. Methods A systematic review of the literature was conducted using several databases (PubMed, Embase, Cochrane, PsycInfo, and IEEE Xplore) using relevant keywords to search for articles published as of November 12, 2021. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria to identify, select, and critically assess the quality of the relevant studies while minimizing bias. We critically analyzed the methodology of the studies to detect any bias and presented the results. Results Among the 93 studies identified, 7 studies were included for analyses. The included studies presented encouraging results. Social media data could be used in several ways to care for patients with schizophrenia, including the monitoring of patients after the first episode of psychosis. We identified several limitations in the included studies, mainly lack of access to clinical diagnostic data, small sample size, and heterogeneity in study quality. We recommend using state-of-the-art natural language processing neural networks, called language models, to model social media activity. Combined with the synthetic minority oversampling technique, language models can tackle the imbalanced data set limitation, which is a necessary constraint to train unbiased classifiers. Furthermore, language models can be easily adapted to the classification task with a procedure called “fine-tuning.” Conclusions The use of social media data for the diagnosis of psychotic disorders is promising. However, most of the included studies had significant biases; we therefore could not draw conclusions about accuracy in clinical situations. Future studies need to use more accurate methodologies to obtain unbiased results.
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Affiliation(s)
- Alban Lejeune
- Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France
| | | | - Michel Walter
- Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France
- Faculté de Médecine et Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Sofian Berrouiguet
- Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France
- Laboratoire de Traitement de l'Information Médicale, Unité Mixte de Recherche 1101, Institut National de la Santé et de la Recherche Médicale, Brest, France
| | - Christophe Lemey
- Unité de Recherche Clinique Intersectorielle, Hôpital de Bohars, Centre Hospitalier Régional Universitaire de Brest, Bohars, France
- Faculté de Médecine et Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
- Lab-STICC, Unité Mixte de Recherche, Centre National de la Recherche Scientifique 6285, F-29238, École Nationale Supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, Brest, France
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Monteith S, Glenn T, Geddes J, Whybrow PC, Achtyes E, Bauer M. Expectations for Artificial Intelligence (AI) in Psychiatry. Curr Psychiatry Rep 2022; 24:709-721. [PMID: 36214931 PMCID: PMC9549456 DOI: 10.1007/s11920-022-01378-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/15/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. RECENT FINDINGS For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.
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Affiliation(s)
- Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, 49684, USA.
| | | | - John Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Peter C. Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA USA
| | - Eric Achtyes
- Michigan State University College of Human Medicine, Grand Rapids, MI 49684 USA ,Network180, Grand Rapids, MI USA
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus Medical Faculty, Technische Universität Dresden, Dresden, Germany
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Artificial Intelligence in Public Health. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_54] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Lin E, Lin CH, Lane HY. Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2021; 19:577-588. [PMID: 34690113 PMCID: PMC8553527 DOI: 10.9758/cpn.2021.19.4.577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/10/2021] [Indexed: 12/31/2022]
Abstract
A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan
- Department of Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
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Jan Z, Ai-Ansari N, Mousa O, Abd-Alrazaq A, Ahmed A, Alam T, Househ M. The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review. J Med Internet Res 2021; 23:e29749. [PMID: 34806996 PMCID: PMC8663682 DOI: 10.2196/29749] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/02/2021] [Accepted: 07/27/2021] [Indexed: 01/10/2023] Open
Abstract
Background Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD. Objective This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes. Methods The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed. Results We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%. Conclusions This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.
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Affiliation(s)
- Zainab Jan
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha, Qatar
| | - Noor Ai-Ansari
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha, Qatar
| | - Osama Mousa
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha, Qatar
| | - Alaa Abd-Alrazaq
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha, Qatar
| | - Arfan Ahmed
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha, Qatar.,Department of Psychiatry, Weill Cornell Medicine, Education City, Doha, Qatar
| | - Tanvir Alam
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha, Qatar
| | - Mowafa Househ
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha, Qatar
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Patil AN, Kasudhan KS, Naveen M, Batra GK, Chakrabarti S, Avasthi A, Grover S. Precise pharmacogenetic pharmacometabolomic (PPP) guided clozapine therapy in treatment resistant schizophrenia: Insights from one ethnicity experiment. Schizophr Res 2021; 237:26-28. [PMID: 34481201 DOI: 10.1016/j.schres.2021.08.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 08/22/2021] [Accepted: 08/22/2021] [Indexed: 02/05/2023]
Affiliation(s)
- Amol N Patil
- Department of Clinical Pharmacology, PGIMER, Chandigarh, India
| | | | - M Naveen
- Department of Clinical Pharmacology, PGIMER, Chandigarh, India
| | | | | | - Ajit Avasthi
- Department of Psychiatry, PGIMER, Chandigarh, India
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Hsu CW, Tsai SY, Wang LJ, Liang CS, Carvalho AF, Solmi M, Vieta E, Lin PY, Hu CA, Kao HY. Predicting Serum Levels of Lithium-Treated Patients: A Supervised Machine Learning Approach. Biomedicines 2021; 9:biomedicines9111558. [PMID: 34829787 PMCID: PMC8615637 DOI: 10.3390/biomedicines9111558] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 11/16/2022] Open
Abstract
Routine monitoring of lithium levels is common clinical practice. This is because the lithium prediction strategies available developed by previous studies are still limited due to insufficient prediction performance. Thus, we used machine learning approaches to predict lithium concentration in a large real-world dataset. Real-world data from multicenter electronic medical records were used in different machine learning algorithms to predict: (1) whether the serum level was 0.6–1.2 mmol/L or 0.0–0.6 mmol/L (binary prediction), and (2) its concentration value (continuous prediction). We developed models from 1505 samples through 5-fold cross-validation and used 204 independent samples to test their performance by evaluating their accuracy. Moreover, we ranked the most important clinical features in different models and reconstructed three reduced models with fewer clinical features. For binary and continuous predictions, the average accuracy of these models was 0.70–0.73 and 0.68–0.75, respectively. Seven features were listed as important features related to serum lithium levels of 0.6–1.2 mmol/L or higher lithium concentration, namely older age, lower systolic blood pressure, higher daily and last doses of lithium prescription, concomitant psychotropic drugs with valproic acid and -pine drugs, and comorbid substance-related disorders. After reducing the features in the three new predictive models, the binary or continuous models still had an average accuracy of 0.67–0.74. Machine learning processes complex clinical data and provides a potential tool for predicting lithium concentration. This may help in clinical decision-making and reduce the frequency of serum level monitoring.
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Affiliation(s)
- Chih-Wei Hsu
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (P.-Y.L.); (C.-A.H.)
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (C.-W.H.); (H.-Y.K.)
| | - Shang-Ying Tsai
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan;
- Department of Psychiatry and Psychiatric Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei 110301, Taiwan
| | - Liang-Jen Wang
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan;
| | - Chih-Sung Liang
- National Defense Medical Center, Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, Taipei 112003, Taiwan;
- National Defense Medical Center, Department of Psychiatry, Taipei 114201, Taiwan
| | - Andre F. Carvalho
- IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre, School of Medicine, Barwon Health, Deakin University, Geelong, VIC 3216, Australia;
| | - Marco Solmi
- Psychiatry Department, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
- The Ottawa Hospital, University of Ottawa, Ottawa, ON K1H 8L6, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Hospital Clinic, IDIBAPS, CIBERSAM, University of Barcelona, 08036 Barcelona, Catalonia, Spain;
| | - Pao-Yen Lin
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (P.-Y.L.); (C.-A.H.)
- Institute for Translational Research in Biomedical Sciences, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 83301, Taiwan
| | - Chien-An Hu
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (P.-Y.L.); (C.-A.H.)
| | - Hung-Yu Kao
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (C.-W.H.); (H.-Y.K.)
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Haga SB. Revisiting Secondary Information Related to Pharmacogenetic Testing. Front Genet 2021; 12:741395. [PMID: 34659361 PMCID: PMC8517135 DOI: 10.3389/fgene.2021.741395] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 09/13/2021] [Indexed: 12/22/2022] Open
Abstract
Incidental or secondary findings have been a major part of the discussion of genomic medicine research and clinical applications. For pharmacogenetic (PGx) testing, secondary findings arise due to the pleiotropic effects of pharmacogenes, often related to their endogenous functions. Unlike the guidelines that have been developed for whole exome or genome sequencing applications for management of secondary findings (though slightly different from PGx testing in that these refer to detection of variants in multiple genes, some with clinical significance and actionability), no corresponding guidelines have been developed for PGx clinical laboratories. Nonetheless, patient and provider education will remain key components of any PGx testing program to minimize adverse responses related to secondary findings.
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Affiliation(s)
- Susanne B Haga
- Center for Applied Genomic and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
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Rema J, Novais F, Telles-Correia D. Precision Psychiatry: Machine learning as a tool to find new pharmacological targets. Curr Top Med Chem 2021; 22:1261-1269. [PMID: 34607546 DOI: 10.2174/1568026621666211004095917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/20/2021] [Accepted: 08/19/2021] [Indexed: 12/18/2022]
Abstract
There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs. OBJECTIVE The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets. METHODS Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review. RESULTS The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents. CONCLUSION Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.
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Affiliation(s)
- João Rema
- Faculdade de Medicina da Universidade de Lisboa. Portugal
| | - Filipa Novais
- Faculdade de Medicina da Universidade de Lisboa. Portugal
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Ho AMC, Weinshilboum RM, Frye MA, Biernacka JM. Genetics and antiepileptic mood stabilizer treatment response in bipolar disorder: what do we know? Pharmacogenomics 2021; 22:913-925. [PMID: 34486896 DOI: 10.2217/pgs-2021-0041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Antiepileptic mood stabilizers (AED-MS) are often used to treat bipolar disorder (BD). Similar to other mood disorder medications, AED-MS treatment response varies between patients. Identification of biomarkers associated with treatment response may ultimately help with the delivery of individualized treatment and lead to improved treatment efficacy. Here, we conducted a narrative review of the current knowledge of the pharmacogenomics of AED-MS (valproic acid, lamotrigine and carbamazepine) treatment response in BD, including genetic contributions to AED-MS pharmacokinetics. Genes involved in neurotransmitter systems and drug transport have been shown to be associated with AED-MS treatment response. As more studies are conducted, and experimental and analytical methods advance, knowledge of AED-MS pharmacogenomics is expected to grow and contribute to precision medicine in BD.
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Affiliation(s)
- Ada Man-Choi Ho
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | - Richard M Weinshilboum
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Mark A Frye
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | - Joanna M Biernacka
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55905, USA.,Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
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Vélez JI. Machine Learning based Psychology: Advocating for A Data-Driven Approach. Int J Psychol Res (Medellin) 2021; 14:6-11. [PMID: 34306575 PMCID: PMC8297577 DOI: 10.21500/20112084.5365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Jorge I Vélez
- Universidad del Norte, Barranquilla, Colombia. Universidad del Norte Universidad del Norte Barranquilla Colombia
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Lin E, Lin CH, Lane HY. Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease. Int J Mol Sci 2021; 22:7911. [PMID: 34360676 PMCID: PMC8347529 DOI: 10.3390/ijms22157911] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 12/21/2022] Open
Abstract
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
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Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1. PLoS One 2021; 16:e0253023. [PMID: 34181661 PMCID: PMC8238228 DOI: 10.1371/journal.pone.0253023] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/26/2021] [Indexed: 01/01/2023] Open
Abstract
Objectives Antidepressants are first-line treatments for major depressive disorder (MDD), but 40–60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict treatment outcomes based on clinical symptoms and episode features. We sought to independently replicate recent machine learning methodology predicting antidepressant outcomes using the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, and then externally validate these methods to train models using data from the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) dataset. Methods We replicated methodology from Nie et al (2018) using common algorithms based on linear regressions and decision trees to predict treatment-resistant depression (TRD, defined as failing to respond to 2 or more antidepressants) in the STAR*D dataset. We then trained and externally validated models using the clinical features found in both datasets to predict response (≥50% reduction on the Quick Inventory for Depressive Symptomatology, Self-Rated [QIDS-SR]) and remission (endpoint QIDS-SR score ≤5) in the CAN-BIND-1 dataset. We evaluated additional models to investigate how different outcomes and features may affect prediction performance. Results Our replicated models predicted TRD in the STAR*D dataset with slightly better balanced accuracy than Nie et al (70%-73% versus 64%-71%, respectively). Prediction performance on our external methodology validation on the CAN-BIND-1 dataset varied depending on outcome; performance was worse for response (best balanced accuracy 65%) compared to remission (77%). Using the smaller set of features found in both datasets generally improved prediction performance when evaluated on the STAR*D dataset. Conclusion We successfully replicated prior work predicting antidepressant treatment outcomes using machine learning methods and clinical data. We found similar prediction performance using these methods on an external database, although prediction of remission was better than prediction of response. Future work is needed to improve prediction performance to be clinically useful.
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Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00447-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractThe number of autism spectrum disorder individuals is dramatically increasing. For them, it is difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which may be the cause of social isolation and economic loss for all their family. This SLR aims at understanding and summarizing the current research work on this topic and analyze the limitations and open challenges to address future work. We consider papers published between 2015 and the beginning of 2021. The initial selection included about 2140 papers. 11 of them respected our selection criteria. The papers have been analyzed by mainly considering: (1) the kind of action taken on the autistic individual, (2) the considered wearables, (3) the machine learning approaches, and (4) the evaluation strategies. Results revealed that the topic is very relevant, but there are many limitations in the considered studies, such as reduced number of participants, absence of datasets and experimentation in real contexts, need for considering privacy issues, and the adoption of appropriate validation approaches. The issues highlighted in this analysis may be useful for improving machine learning techniques and highlighting areas of interest in which experimenting with the use of different noninvasive sensors.
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Lin E, Kuo PH, Lin WY, Liu YL, Yang AC, Tsai SJ. Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach. J Pers Med 2021; 11:597. [PMID: 34202750 PMCID: PMC8308113 DOI: 10.3390/jpm11070597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/14/2021] [Accepted: 06/22/2021] [Indexed: 12/16/2022] Open
Abstract
In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Po-Hsiu Kuo
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan; (P.-H.K.); (W.-Y.L.)
| | - Wan-Yu Lin
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan; (P.-H.K.); (W.-Y.L.)
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County 35053, Taiwan;
| | - Albert C. Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA 02215, USA;
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- Division of Psychiatry, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
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Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021; 20:154-170. [PMID: 34002503 PMCID: PMC8129866 DOI: 10.1002/wps.20882] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.
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Affiliation(s)
- Adam M Chekroud
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Spring Health, New York City, NY, USA
| | | | - Jaime Delgadillo
- Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK
| | - Gavin Doherty
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Akash Wasil
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Marjolein Fokkema
- Department of Methods and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Zachary Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Robert DeRubeis
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Karmel Choi
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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41
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Lin E, Lin CH, Lane HY. Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection. Sci Rep 2021; 11:10179. [PMID: 33986383 PMCID: PMC8119477 DOI: 10.1038/s41598-021-89540-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/27/2021] [Indexed: 12/31/2022] Open
Abstract
Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia’ functional outcomes such as Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we leveraged a bagging ensemble machine learning method with a feature selection algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble machine learning algorithm with other state-of-the-art models such as linear regression, support vector machine, multilayer feedforward neural networks, and random forests. The analysis reported that the bagging ensemble algorithm with feature selection outperformed other predictive algorithms to forecast the QLS functional outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. Furthermore, the bagging ensemble algorithm with feature selection surpassed other predictive algorithms to forecast the GAF functional outcome of schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the bagging ensemble machine learning algorithm with feature selection might present an applicable approach to provide software tools for forecasting the functional outcomes of schizophrenia using molecular biomarkers.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA.,Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA.,Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. .,Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan. .,School of Medicine, Chang Gung University, Taoyüan, Taiwan.
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. .,Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan. .,Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan. .,Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan.
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Prescription Pattern of Antidepressants and the Potential for Personalized Medicine in the Qatari Population. J Pers Med 2021; 11:jpm11050406. [PMID: 34068080 PMCID: PMC8152751 DOI: 10.3390/jpm11050406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 04/28/2021] [Accepted: 05/02/2021] [Indexed: 01/27/2023] Open
Abstract
Studying the prescription pattern of medications will help in understanding potential unnecessary prescriptions, due to the trial-and-error method of prescribing, and the need for personalized medicine in a population. Therefore, in this study, our aim was to explore the prescribing pattern and off-label use of antidepressants in the Qatari population. We conducted a retrospective study of Qatari patients who received prescriptions for antidepressants from the major healthcare providers in Qatar, for a period of 24 months between June 2018 and May 2020. The number of patients, prescriptions, and diagnostic indications were analyzed. The chi-square test was used for identifying statistically significant association of the number of individuals prescribed with age category or gender. Of the 14,601 Qatari patients who were prescribed antidepressants, the majority were female (61%, p < 2.2 × 10-16), and were at or above 60 years of age (27%, p < 2.2 × 10-16). More numbers of selective serotonin reuptake inhibitors (SSRIs) (22,085 out of 48,031; 46%), were dispensed than other classes of antidepressants, with escitalopram (26%) at the top of the list. Preponderance of prescription of antidepressants for non-mental health diseases was observed. Population-level prescription trends, as we reported here, when combined with patient genetic variability and outcome data, will have the power to predict the potential for treatment failures and adverse effects of these medications in the population. We also recommend educating non-mental health prescribers about the adherence to evidence and guidelines to ensure patient safety while prescribing antidepressants.
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Jan Z, Ai-ansari N, Mousa O, Abd-alrazaq A, Ahmed A, Alam T, Househ M. The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review (Preprint).. [DOI: 10.2196/preprints.29749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.
OBJECTIVE
This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.
METHODS
The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.
RESULTS
We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.
CONCLUSIONS
This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.
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Ramli FF. Pharmacogenomics biomarkers for personalized methadone maintenance treatment: The mechanism and its potential use. Bosn J Basic Med Sci 2021; 21:145-154. [PMID: 32841585 PMCID: PMC7982063 DOI: 10.17305/bjbms.2020.4897] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 08/10/2020] [Indexed: 11/23/2022] Open
Abstract
Methadone has a wide pharmacokinetic interindividual variability, resulting in unpredicted treatment response. Pharmacogenomic biomarkers seem promising for personalized methadone maintenance treatment. The evidence supports the use of ABCB1 single-nucleotide polymorphism (SNP) 1236C>T with genotypes C/T or C/C (Jewish) and haplotypes AGCTT carrier, AGCGC heterozygote, or non-carrier (Caucasian), which have a predicted lower methadone dose requirement. In contrast, ABCB1 SNP 1236C>T with genotype T/T (Jewish); haplotypes AGCGC homozygote, AGCTT non-carrier (Caucasian), and ABCB1 3435C>T variant carrier; and haplotypes CGT, TTC, and TGT (Han Chinese) have a predicted higher methadone dose. For methadone plasma levels, ABCB1 diplotype non-CGC/TTT (Malay) predicted lower, and diplotype CGC/TTT (Malay), 3435C>T allelic carrier, haplotypes (CGT, TTC, TGT) (Han Chinese) predicted higher methadone levels. In terms of metabolism biomarkers, a lower methadone requirement was related to carriers of CYP2B6 genotypes *4(G/G) and *9(T/T) among Jewish patients, CYP2B6*9 genotype (T/T) and haplotypes (TA/TG); and CYP2C19(*2/*2,*2/*3, and *3/*3; Han Chinese). Higher methadone dose was observed in CYP2C19*1 allelic carriers (Han Chinese) and CYP2D6 ultrarapid metabolizer (Caucasian). Lower methadone levels were reported in CYP2B6 SNPs, haplotypes TTT, and AGATAA (Han Chinese), CYP2C19 genotype *1/*1 (Han Chinese), allelic carrier *1xN (Caucasian), and CYP3A4 genotype *1/*1 (Caucasian). Carriers of CYP2B6 genotype *6/*6 (Caucasian), CYP2B6 haplotypes ATGCAG and ATGCTG (Han Chinese), and CYP3A4 genotype *1/*1B (Caucasian) had predicted higher methadone plasma levels. Specific pharmacokinetics biomarkers have potential uses for personalized methadone treatment in specific populations.
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Affiliation(s)
- Fitri Fareez Ramli
- Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Kuala Lumpur, Malaysia
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45
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Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease. Int J Mol Sci 2021; 22:ijms22052761. [PMID: 33803217 PMCID: PMC7963160 DOI: 10.3390/ijms22052761] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/02/2021] [Accepted: 03/05/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Alzheimer’s disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) levels of amyloid-β1-42 (Aβ42), total tau protein and hyperphosphorylated tau (p-tau). However, the available methods are expensive and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis. Methods: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews for reviews and trials that investigated the machine learning and novel biomarkers in diagnosis of AD. Results: In additional to Aβ and tau-related biomarkers, biomarkers according to other mechanisms of AD pathology have been investigated. Neuronal injury biomarker includes neurofiliament light (NFL). Biomarkers about synaptic dysfunction and/or loss includes neurogranin, BACE1, synaptotagmin, SNAP-25, GAP-43, synaptophysin. Biomarkers about neuroinflammation includes sTREM2, and YKL-40. Besides, d-glutamate is one of coagonists at the NMDARs. Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy controls. Conclusions: Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing AD in outpatient clinics.
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Chang CH, Lin CH, Liu CY, Huang CS, Chen SJ, Lin WC, Yang HT, Lane HY. Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches. J Psychopharmacol 2021; 35:265-272. [PMID: 33586518 DOI: 10.1177/0269881120972331] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. AIMS This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. METHODS Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. RESULTS The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels (r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score (B = 0.003, 95% confidence interval 0.002-0.005, p < 0.001). CONCLUSIONS Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.
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Affiliation(s)
- Chun-Hung Chang
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan.,Department of Psychiatry and Brain Disease Research Centre, China Medical University Hospital, Taichung, Taiwan.,An Nan Hospital, China Medical University, Tainan, Taiwan
| | - Chieh-Hsin Lin
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan.,Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.,Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chieh-Yu Liu
- Biostatistical Consulting Lab, Department of Speech Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Chih-Sheng Huang
- Artificial Intelligence Research and Development Department, ELAN Microelectronics Corporation, Hsinchu, Taiwan
| | - Shaw-Ji Chen
- Department of Psychiatry, Mackay Memorial Hospital Taitung Branch, Taitung, Taiwan.,Department of Medicine, Mackay Medical College, New Taipei, Taiwan
| | - Wen-Cheng Lin
- Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan
| | - Hui-Ting Yang
- School of Food Safety, Taipei Medical University, Taipei, Taiwan
| | - Hsien-Yuan Lane
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan.,Department of Psychiatry and Brain Disease Research Centre, China Medical University Hospital, Taichung, Taiwan.,Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.,Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
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Češková E, Šilhán P. From Personalized Medicine to Precision Psychiatry? Neuropsychiatr Dis Treat 2021; 17:3663-3668. [PMID: 34934319 PMCID: PMC8684413 DOI: 10.2147/ndt.s337814] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/16/2021] [Indexed: 12/28/2022] Open
Abstract
Personalised medicine aims to find an individualized approach for each particular patient. Most factors used in current psychiatry, however, depend on the assessment made by the individual clinician and lack a higher degree of reliability. Precision medicine bases decisions on quantifiable indicators available thanks to the tremendous progress in science and technology facilitating the acquisition, processing and analysis of huge amounts of data. So far, psychiatry has not been benefiting enough from the advanced diagnostic technologies; nevertheless, we are witnessing the dawn of the era of precision psychiatry, starting with the gathering of sufficient amounts of data and its analysis by the means of artificial intelligence and machine learning. First results of this approach in psychiatry are available, which facilitate diagnosis assessment, course prediction, and appropriate treatment choice. These processes are often so complex and difficult to understand that they may resemble a "black box", which can slow down the acceptance of the results of this approach in clinical practice. Still, bringing precision medicine including psychiatry to standard clinical practice is a big challenge that can result in a completely new and transformative concept of health care. Such extensive changes naturally have both their supporters and opponents. This paper aims to familiarize clinically oriented physicians with precision psychiatry and to attract their attention to its recent developments. We cover the theoretical basis of precision medicine, its specifics in psychiatry, and provide examples of its use in the field of diagnostic assessment, course prediction, and appropriate treatment planning.
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Affiliation(s)
- Eva Češková
- Department of Psychiatry, University Hospital Ostrava, Ostrava, Czech Republic.,Department of Clinical Neurosciences, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic.,Department of Psychiatry, University Hospital Brno, Brno, Czech Republic.,Department of Psychiatry, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petr Šilhán
- Department of Psychiatry, University Hospital Ostrava, Ostrava, Czech Republic.,Department of Clinical Neurosciences, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
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48
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Shukla R, Henkel ND, Alganem K, Hamoud AR, Reigle J, Alnafisah RS, Eby HM, Imami AS, Creeden JF, Miruzzi SA, Meller J, Mccullumsmith RE. Signature-based approaches for informed drug repurposing: targeting CNS disorders. Neuropsychopharmacology 2021; 46:116-130. [PMID: 32604402 PMCID: PMC7688959 DOI: 10.1038/s41386-020-0752-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/30/2020] [Accepted: 06/22/2020] [Indexed: 12/15/2022]
Abstract
CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.
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Affiliation(s)
- Rammohan Shukla
- Department of Neurosciences, University of Toledo, Toledo, OH, USA.
| | | | - Khaled Alganem
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | | | - James Reigle
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Hunter M Eby
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Ali S Imami
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Justin F Creeden
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Scott A Miruzzi
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Jaroslaw Meller
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Electrical Engineering and Computing Systems, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
| | - Robert E Mccullumsmith
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
- Neurosciences Institute, ProMedica, Toledo, OH, USA
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49
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Artificial Intelligence in Public Health. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_54-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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50
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Kam H, Jeong H. Pharmacogenomic Biomarkers and Their Applications in Psychiatry. Genes (Basel) 2020; 11:genes11121445. [PMID: 33266292 PMCID: PMC7760818 DOI: 10.3390/genes11121445] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 11/27/2020] [Accepted: 11/27/2020] [Indexed: 12/13/2022] Open
Abstract
Realizing the promise of precision medicine in psychiatry is a laudable and beneficial endeavor, since it should markedly reduce morbidity and mortality and, in effect, alleviate the economic and social burden of psychiatric disorders. This review aims to summarize important issues on pharmacogenomics in psychiatry that have laid the foundation towards personalized pharmacotherapy and, in a broader sense, precision medicine. We present major pharmacogenomic biomarkers and their applications in a variety of psychiatric disorders, such as depression, attention-deficit/hyperactivity disorder (ADHD), narcolepsy, schizophrenia, and bipolar disorder. In addition, we extend the scope into epilepsy, since antiepileptic drugs are widely used to treat psychiatric disorders, although epilepsy is conventionally considered to be a neurological disorder.
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