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Telenti A, Auli M, Hie BL, Maher C, Saria S, Ioannidis JPA. Large language models for science and medicine. Eur J Clin Invest 2024; 54:e14183. [PMID: 38381530 DOI: 10.1111/eci.14183] [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: 12/22/2023] [Revised: 02/06/2024] [Accepted: 02/10/2024] [Indexed: 02/23/2024]
Abstract
Large language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task-specific LLMs have demonstrated potential across diverse applications. Science and medicine have many data types that are highly suitable for LLMs, such as scientific texts (publications, patents and textbooks), electronic medical records, large databases of DNA and protein sequences and chemical compounds. Carefully validated systems that can understand and reason across all these modalities may maximize benefits. Despite the inevitable limitations and caveats of any new technology and some uncertainties specific to LLMs, LLMs have the potential to be transformative in science and medicine.
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Affiliation(s)
- Amalio Telenti
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California, USA
- Vir Biotechnology, Inc., San Francisco, California, USA
| | | | - Brian L Hie
- FAIR, Meta, Menlo Park, California, USA
- Department of Chemical Engineering, Stanford University, Stanford, California, USA
| | - Cyrus Maher
- Vir Biotechnology, Inc., San Francisco, California, USA
| | - Suchi Saria
- Malone Center for Engineering and Healthcare, Johns Hopkins University, Baltimore, Maryland, USA
| | - John P A Ioannidis
- Department of Medicine, Stanford University, Stanford, California, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
- Department of Statistics, Stanford University, Stanford, California, USA
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
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Diaz MJ, Tran JT. Potential Benefits of Non-Fungible Tokens (NFTs) and Blockchain Technology in Dermatology. Dermatol Pract Concept 2024; 14:dpc.1401a61. [PMID: 38364421 PMCID: PMC10868896 DOI: 10.5826/dpc.1401a61] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2023] [Indexed: 02/18/2024] Open
Affiliation(s)
- Michael Joseph Diaz
- College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Jasmine Thuy Tran
- School of Medicine, Indiana University, Indianapolis, Indiana, United States
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Bibb A, Schmidt K, Brink L, Pisano E, Coombs L, Apgar C, Dreyer K, Wald C. Specialty Society Support for Multicenter Research in Artificial Intelligence. Acad Radiol 2023; 30:640-643. [PMID: 36813668 DOI: 10.1016/j.acra.2023.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/06/2023] [Accepted: 01/08/2023] [Indexed: 02/22/2023]
Affiliation(s)
- Allen Bibb
- Grandview Medical Center, ACR Data Science Institute, Birmingham, Alabama.
| | | | - Laura Brink
- American College of Radiology, Reston, Virginia
| | - E Pisano
- American College of Radiology, Reston, Virginia
| | | | | | - Keith Dreyer
- Massachusetts General Hospital, ACR Data Science Institute, Boston, Massachusetts
| | - Christoph Wald
- Lahey Hospital and Medical Center, ACR Commission on Informatics, Boston, Massachusetts
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Falda M, Atzori M, Corbetta M. Semantic wikis as flexible database interfaces for biomedical applications. Sci Rep 2023; 13:1095. [PMID: 36658254 PMCID: PMC9851594 DOI: 10.1038/s41598-023-27743-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 01/06/2023] [Indexed: 01/20/2023] Open
Abstract
Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm .
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Affiliation(s)
- Marco Falda
- Neuroscience Department, University of Padova, Padova, Italy.
| | - Manfredo Atzori
- Neuroscience Department, University of Padova, Padova, Italy
- Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland
- Padova Neuroscience Center (PNC), Clinica Neurologica, and Venetian Institute of Molecular Medicine, VIMM, Padova, Italy
| | - Maurizio Corbetta
- Neuroscience Department, University of Padova, Padova, Italy
- Padova Neuroscience Center (PNC), Clinica Neurologica, and Venetian Institute of Molecular Medicine, VIMM, Padova, Italy
- Department of Neurology, Radiology, Neuroscience Washington University School of Medicine, St. Louis, MO, USA
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Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine. Int J Mol Sci 2022; 24:ijms24010004. [PMID: 36613446 PMCID: PMC9819745 DOI: 10.3390/ijms24010004] [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: 10/18/2022] [Revised: 12/16/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.
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Ramos Meyers G, Samouda H, Bohn T. Short Chain Fatty Acid Metabolism in Relation to Gut Microbiota and Genetic Variability. Nutrients 2022; 14:5361. [PMID: 36558520 PMCID: PMC9788597 DOI: 10.3390/nu14245361] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
It is widely accepted that the gut microbiota plays a significant role in modulating inflammatory and immune responses of their host. In recent years, the host-microbiota interface has gained relevance in understanding the development of many non-communicable chronic conditions, including cardiovascular disease, cancer, autoimmunity and neurodegeneration. Importantly, dietary fibre (DF) and associated compounds digested by the microbiota and their resulting metabolites, especially short-chain fatty acids (SCFA), were significantly associated with health beneficial effects, such as via proposed anti-inflammatory mechanisms. However, SCFA metabolic pathways are not fully understood. Major steps include production of SCFA by microbiota, uptake in the colonic epithelium, first-pass effects at the liver, followed by biodistribution and metabolism at the host's cellular level. As dietary patterns do not affect all individuals equally, the host genetic makeup may play a role in the metabolic fate of these metabolites, in addition to other factors that might influence the microbiota, such as age, birth through caesarean, medication intake, alcohol and tobacco consumption, pathogen exposure and physical activity. In this article, we review the metabolic pathways of DF, from intake to the intracellular metabolism of fibre-derived products, and identify possible sources of inter-individual variability related to genetic variation. Such variability may be indicative of the phenotypic flexibility in response to diet, and may be predictive of long-term adaptations to dietary factors, including maladaptation and tissue damage, which may develop into disease in individuals with specific predispositions, thus allowing for a better prediction of potential health effects following personalized intervention with DF.
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Affiliation(s)
- Guilherme Ramos Meyers
- Nutrition and Health Research Group, Department of Precision Health, Luxembourg Institute of Health, 1 A-B, Rue Thomas Edison, 1445 Strassen, Luxembourg
- Doctoral School in Science and Engineering, University of Luxembourg, 2, Avenue de l'Université, 4365 Esch-sur-Alzette, Luxembourg
| | - Hanen Samouda
- Nutrition and Health Research Group, Department of Precision Health, Luxembourg Institute of Health, 1 A-B, Rue Thomas Edison, 1445 Strassen, Luxembourg
| | - Torsten Bohn
- Nutrition and Health Research Group, Department of Precision Health, Luxembourg Institute of Health, 1 A-B, Rue Thomas Edison, 1445 Strassen, Luxembourg
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Wirth FN, Kussel T, Müller A, Hamacher K, Prasser F. EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation. BMC Bioinformatics 2022; 23:531. [PMID: 36494612 PMCID: PMC9733077 DOI: 10.1186/s12859-022-05044-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/08/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability. RESULTS To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor. CONCLUSIONS We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.
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Affiliation(s)
- Felix Nikolaus Wirth
- grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Medical Informatics Group, Charitéplatz 1, 10117 Berlin, Germany
| | - Tobias Kussel
- grid.6546.10000 0001 0940 1669Computational Biology and Simulation, TU Darmstadt, Darmstadt, Germany
| | - Armin Müller
- grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Medical Informatics Group, Charitéplatz 1, 10117 Berlin, Germany
| | - Kay Hamacher
- grid.6546.10000 0001 0940 1669Computational Biology and Simulation, TU Darmstadt, Darmstadt, Germany
| | - Fabian Prasser
- grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Medical Informatics Group, Charitéplatz 1, 10117 Berlin, Germany
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Li W, Tong J, Anjum MM, Mohammed N, Chen Y, Jiang X. Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources. BMC Med Inform Decis Mak 2022; 22:269. [PMID: 36244993 PMCID: PMC9569919 DOI: 10.1186/s12911-022-02014-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 10/04/2022] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVES This paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package ('lme4') as the baseline model. METHODS The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation, abbreviated as LA and GH), which supports federated decomposition of GLMM to bring computation to data. To solve the numerical errors and singularity issues, the loss-less estimation of log-sum-exponential trick and the adaptive regularization strategy was used to tackle the problems caused by federated settings. RESULTS Our proposed method can handle GLMM to accommodate hierarchical data with multiple non-independent levels of observations in a federated setting. The experiment results demonstrate comparable (LA) and superior (GH) performances with simulated and real-world data. CONCLUSION We modified and compared federated GLMMs with different approximations, which can support researchers in analyzing versatile biomedical data to accommodate mixed effects and address non-independence due to hierarchical structures (i.e., institutes, region, country, etc.).
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Affiliation(s)
- Wentao Li
- School of Biomedical Informatics, UTHealth, 7000 Fannin St, Houston, 77030 TX USA
| | - Jiayi Tong
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, 19104 PA USA
| | - Md. Monowar Anjum
- Department of Computer Science, University of Manitoba, Winnipeg, Canada
| | - Noman Mohammed
- Department of Computer Science, University of Manitoba, Winnipeg, Canada
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, 19104 PA USA
| | - Xiaoqian Jiang
- School of Biomedical Informatics, UTHealth, 7000 Fannin St, Houston, 77030 TX USA
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Kim M, Wang S, Jiang X, Harmanci A. SVAT: Secure outsourcing of variant annotation and genotype aggregation. BMC Bioinformatics 2022; 23:409. [PMID: 36182914 PMCID: PMC9526274 DOI: 10.1186/s12859-022-04959-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 09/20/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Sequencing of thousands of samples provides genetic variants with allele frequencies spanning a very large spectrum and gives invaluable insight into genetic determinants of diseases. Protecting the genetic privacy of participants is challenging as only a few rare variants can easily re-identify an individual among millions. In certain cases, there are policy barriers against sharing genetic data from indigenous populations and stigmatizing conditions. RESULTS We present SVAT, a method for secure outsourcing of variant annotation and aggregation, which are two basic steps in variant interpretation and detection of causal variants. SVAT uses homomorphic encryption to encrypt the data at the client-side. The data always stays encrypted while it is stored, in-transit, and most importantly while it is analyzed. SVAT makes use of a vectorized data representation to convert annotation and aggregation into efficient vectorized operations in a single framework. Also, SVAT utilizes a secure re-encryption approach so that multiple disparate genotype datasets can be combined for federated aggregation and secure computation of allele frequencies on the aggregated dataset. CONCLUSIONS Overall, SVAT provides a secure, flexible, and practical framework for privacy-aware outsourcing of annotation, filtering, and aggregation of genetic variants. SVAT is publicly available for download from https://github.com/harmancilab/SVAT .
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Affiliation(s)
- Miran Kim
- Department of Mathematics, Hanyang University, Seoul, 04763, Republic of Korea
| | - Su Wang
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Arif Harmanci
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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Rowell C, Sebro R. Who Will Get Paid for Artificial Intelligence in Medicine? Radiol Artif Intell 2022; 4:e220054. [PMID: 36204537 PMCID: PMC9530770 DOI: 10.1148/ryai.220054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 06/16/2023]
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Li J, Wang D, Qi G, Li Z, Huang J, Zhu Z, Shen C, Lin B, Dong K, Zhao B, Shu Q, Yin J, Yu G. Alliance chain-based simulation on a new clinical research data pricing model. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:836. [PMID: 36035004 PMCID: PMC9403923 DOI: 10.21037/atm-22-3671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/02/2022] [Indexed: 11/21/2022]
Abstract
Background Multicenter clinical research faces many challenges, including how to quantitatively evaluate the data contribution of each research center. However, few data pricing model meets the requirements to the scenario. Thus, a suitable mechanism to measure the data value for clinical research is required. Methods Extensive documents were acquired and analyzed, including a rare disease list from the National Health Commission, data structures of the electronic medical records (EMR) system, diagnosis-related groups (DRGs) regulations from the Health Commission of Zhejiang Province, and the Clinical Service Price List of Zhejiang Province. Nine senior experts were invited as consultants from hospital and enterprises with professional field of clinical research, data governance, and health economics. After brainstorming and expert evaluation, seven data attributes were identified as the main factors affecting the value of medical data. Different weights were assigned for each attribute based on its influence on data value. Each attribute was quantized to an index based on proposed algorithms. The data value models for chronic diseases and other diseases were distinguished given the different sensitivity of data timeliness. A simulation system using blockchain and federated learning techniques was constructed to verify the data pricing model in the scenario of clinical research. Results A comprehensive clinical data pricing model is proposed and the simulation of three research centers with 50 million real clinical data entries was conducted to verify its effectiveness. It demonstrates that the proposed model can compute medical data value quantitatively. Conclusions Quantitative evaluation of the value of medical data for multicenter clinical research based on the proposed data pricing model works well in simulation. This model will be improved by real-world applications in the near future.
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Affiliation(s)
- Jing Li
- Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.,Department of Research, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China.,AI Lab, National Clinical Research Center for Child Health, Hangzhou, China
| | - Dejian Wang
- Department of R&D, Hangzhou Healink Technology, Hangzhou, China
| | - Guoqiang Qi
- Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.,Department of Research, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China.,AI Lab, National Clinical Research Center for Child Health, Hangzhou, China
| | - Zheming Li
- Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.,Department of Research, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China.,AI Lab, National Clinical Research Center for Child Health, Hangzhou, China
| | - Jian Huang
- Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.,Department of Research, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China.,AI Lab, National Clinical Research Center for Child Health, Hangzhou, China
| | - Zhu Zhu
- Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.,Department of Research, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China.,AI Lab, National Clinical Research Center for Child Health, Hangzhou, China
| | - Chen Shen
- Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.,Department of Research, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China.,AI Lab, National Clinical Research Center for Child Health, Hangzhou, China
| | - Bo Lin
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China.,Research Center of Domestic IT Innovation, Binjiang Institute of Zhejiang University, Hangzhou, China
| | - Kexiong Dong
- Department of R&D, Hangzhou Healink Technology, Hangzhou, China
| | - Baolong Zhao
- Department of R&D, Hangzhou Healink Technology, Hangzhou, China
| | - Qiang Shu
- Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.,AI Lab, National Clinical Research Center for Child Health, Hangzhou, China
| | - Jianwei Yin
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China.,Research Center of Domestic IT Innovation, Binjiang Institute of Zhejiang University, Hangzhou, China
| | - Gang Yu
- Department of Data and Information, The Children's Hospital Zhejiang University School of Medicine, Hangzhou, China.,Department of Research, Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China.,AI Lab, National Clinical Research Center for Child Health, Hangzhou, China.,Polytechnic Institute, Zhejiang University, Hangzhou, China
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Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. Eur Neuropsychopharmacol 2022; 60:100-116. [PMID: 35671641 DOI: 10.1016/j.euroneuro.2022.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 12/23/2022]
Abstract
Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.
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