1
|
Sisoudiya SD, Houle AA, Fernando T, Wilson TR, Schutzman JL, Lee J, Schrock A, Sokol ES, Sivakumar S, Shi Z, Pathria G. Ancestry-associated co-alteration landscape of KRAS and EGFR-altered non-squamous NSCLC. NPJ Precis Oncol 2024; 8:153. [PMID: 39033203 PMCID: PMC11271287 DOI: 10.1038/s41698-024-00644-4] [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: 01/12/2024] [Accepted: 07/09/2024] [Indexed: 07/23/2024] Open
Abstract
Racial/ethnic disparities mar NSCLC care and treatment outcomes. While socioeconomic factors and access to healthcare are important drivers of NSCLC disparities, a deeper understanding of genetic ancestry-associated genomic landscapes can better inform the biology and the treatment actionability for these tumors. We present a comprehensive ancestry-based prevalence and co-alteration landscape of genomic alterations and immunotherapy-associated biomarkers in patients with KRAS and EGFR-altered non-squamous (non-Sq) NSCLC. KRAS was the most frequently altered oncogene in European (EUR) and African (AFR), while EGFR alterations predominated in East Asian (EAS), South Asian (SAS), and Admixed American (AMR) groups, consistent with prior studies. As expected, STK11 and KEAP1 alterations co-occurred with KRAS alterations while showing mutual exclusivity with EGFR alterations. EAS and AMR KRAS-altered non-Sq NSCLC showed lower rates of co-occurring STK11 and KEAP1 alterations relative to other ancestry groups. Ancestry-specific co-alterations included the co-occurrence of KRAS and GNAS alterations in AMR, KRAS, and ARID1A alterations in SAS, and the mutual exclusivity of KRAS and NF1 alterations in the EUR and AFR ancestries. Contrastingly, EGFR-altered tumors exhibited a more conserved co-alteration landscape across ancestries. AFR exhibited the highest tumor mutational burden, with potential therapeutic implications for these tumors.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | - Zhen Shi
- Genentech Inc., South San Francisco, CA, USA.
| | - Gaurav Pathria
- Genentech Inc., South San Francisco, CA, USA.
- TOLREMO Therapeutics, Basel, Switzerland.
| |
Collapse
|
2
|
Miller M, Jorm L, Partyka C, Burns B, Habig K, Oh C, Immens S, Ballard N, Gallego B. Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia. Injury 2024; 55:111570. [PMID: 38664086 DOI: 10.1016/j.injury.2024.111570] [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: 01/18/2024] [Revised: 04/11/2024] [Accepted: 04/14/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records. METHODS We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %). CONCLUSION The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.
Collapse
Affiliation(s)
- Matthew Miller
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Anesthesia, St George Hospital, Kogarah, NSW 2217 Australia; Centre for Big Data Research in Health at UNSW Sydney, Kensington, NSW 2052, Australia.
| | - Louisa Jorm
- Foundation Director of the Centre for Big Data Research in Health at UNSW Sydney, Kensington 2052, Australia
| | - Chris Partyka
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Emergency Medicine, Royal North Shore Hospital, St Leonards, NSW 2065, Australia
| | - Brian Burns
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Royal North Shore Hospital, St Leonards, NSW 2065, Australia; Faculty of Medicine & Health, University of Sydney, Camperdown, NSW 2050, Australia
| | - Karel Habig
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia
| | - Carissa Oh
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Emergency Medicine, St George Hospital, Kogarah, NSW 2217 Australia
| | - Sam Immens
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia
| | - Neil Ballard
- Aeromedical Operations, New South Wales Ambulance, Rozelle, NSW 2039, Australia; Department of Paediatric Emergency Medicine, Sydney Children's Hospital, Randwick, NSW 2031, Australia; Department of Emergency Medicine, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
| | - Blanca Gallego
- Clinical analytics and machine learning unit, Centre for Big Data Research in Health at UNSW Sydney, Kensington 2052, Australia
| |
Collapse
|
3
|
Daneshvar N, Pandita D, Erickson S, Snyder Sulmasy L, DeCamp M. Artificial Intelligence in the Provision of Health Care: An American College of Physicians Policy Position Paper. Ann Intern Med 2024; 177:964-967. [PMID: 38830215 DOI: 10.7326/m24-0146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/05/2024] Open
Abstract
Internal medicine physicians are increasingly interacting with systems that implement artificial intelligence (AI) and machine learning (ML) technologies. Some physicians and health care systems are even developing their own AI models, both within and outside of electronic health record (EHR) systems. These technologies have various applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. With the growing availability of vast amounts of patient data and unprecedented levels of clinician burnout, the proliferation of these technologies is cautiously welcomed by some physicians. Others think it presents challenges to the patient-physician relationship and the professional integrity of physicians. These dispositions are understandable, given the "black box" nature of some AI models, for which specifications and development methods can be closely guarded or proprietary, along with the relative lagging or absence of appropriate regulatory scrutiny and validation. This American College of Physicians (ACP) position paper describes the College's foundational positions and recommendations regarding the use of AI- and ML-enabled tools and systems in the provision of health care. Many of the College's positions and recommendations, such as those related to patient-centeredness, privacy, and transparency, are founded on principles in the ACP Ethics Manual. They are also derived from considerations for the clinical safety and effectiveness of the tools as well as their potential consequences regarding health disparities. The College calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being.
Collapse
Affiliation(s)
| | - Deepti Pandita
- University of California Irvine Health, Laguna Niguel, California (D.P.)
| | - Shari Erickson
- American College of Physicians, Washington, DC (N.D., S.E.)
| | | | - Matthew DeCamp
- University of Colorado Anschutz Medical Campus, Aurora, Colorado (M.D.)
| |
Collapse
|
4
|
Nwachukwu C, Makhnoon S, Person M, Muthukrishnan M, Kazmi S, Anderson LD, Kaur G, Kapinos KA, Williams EL, Fatunde O, Sadeghi N, Robles F, Basey A, Hulsey T, Pruitt SL, Gerber DE. Transferring care to enhance access to early-phase cancer clinical trials: Protocol to evaluate a novel program. Contemp Clin Trials Commun 2024; 39:101292. [PMID: 38623454 PMCID: PMC11016932 DOI: 10.1016/j.conctc.2024.101292] [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: 10/15/2023] [Revised: 02/14/2024] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
Involving diverse populations in early-phase (phase I and II) cancer clinical trials is critical to informed therapeutic development. However, given the growing costs and complexities of early-phase trials, trial activation and enrollment barriers may be greatest for these studies at healthcare facilities that provide care to the most diverse patient groups, including those in historically underserved communities (e.g., safety-net healthcare systems). To promote diverse and equitable access to early-phase cancer clinical trials, we are implementing a novel program for the transfer of care to enhance access to early-phase cancer clinical trials. We will then perform a mixed-methods study to determine perceptions and impact of the program. Specifically, we will screen, recruit, and enroll diverse patients from an urban, integrated safety-net healthcare system to open and active early-phase clinical trials being conducted in a university-based cancer center. To evaluate this novel program, we will: (1) determine program impact and efficiency; and (2) determine stakeholder experience with and perceptions of the program. To achieve these goals, we will conduct preliminary cost analyses of the program. We will also conduct surveys and interviews with patients and caregivers to elucidate program impact, challenges, and areas for improvement. We hypothesize that broadening access to early-phase cancer trials conducted at experienced centers may improve equity and diversity. In turn, such efforts may enhance the efficiency and generalizability of cancer clinical research.
Collapse
Affiliation(s)
- Chika Nwachukwu
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sukh Makhnoon
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA
| | - Marieshia Person
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Meera Muthukrishnan
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA
| | - Syed Kazmi
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Division of Hematology-Oncology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Larry D. Anderson
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Division of Hematology-Oncology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Gurbakhash Kaur
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Division of Hematology-Oncology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Kandice A. Kapinos
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA
- RAND Corporation, Arlington, VA, USA
| | - Erin L. Williams
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Oluwatomilade Fatunde
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Navid Sadeghi
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Division of Hematology-Oncology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
- Parkland Health, Dallas, TX, USA
| | - Fabian Robles
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Parkland Health, Dallas, TX, USA
| | - Alice Basey
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Patient Advocate Program, Office of Community Outreach, Engagement, and Equity, Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Thomas Hulsey
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Patient Advocate Program, Office of Community Outreach, Engagement, and Equity, Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sandi L. Pruitt
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA
| | - David E. Gerber
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA
- Division of Hematology-Oncology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| |
Collapse
|
5
|
Zhou J, Wang X, Li Y, Yang Y, Shi J. Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism. BMC Med Inform Decis Mak 2024; 24:141. [PMID: 38802861 PMCID: PMC11131248 DOI: 10.1186/s12911-024-02543-x] [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: 01/23/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) technology in PTE prognosis risk assessment while ensuring the security of clinical data. METHODS A retrospective dataset consisted of PTE patients from 12 hospitals were collected, and 19 physical indicators of patients were included to train the FL-based prognosis assessment model to predict the 30-day death event. Firstly, multiple machine learning methods based on FL were compared to choose the superior model. And then performance of models trained on the independent (IID) and non-independent identical distributed(Non-IID) datasets was calculated and they were tested further on Real-world data. Besides, the optimal model was compared with pulmonary embolism severity index (PESI), simplified PESI (sPESI), Peking Union Medical College Hospital (PUMCH). RESULTS The area under the receiver operating characteristic curve (AUC) of logistic regression(0.842) outperformed convolutional neural network (0.819) and multi layer perceptron (0.784). Under IID, AUC of model trained using FL(Fed) on the training, validation and test sets was 0.852 ± 0.002, 0.867 ± 0.012 and 0.829 ± 0.004. Under Real-world, AUC of Fed was 0.855 ± 0.005, 0.882 ± 0.003 and 0.835 ± 0.005. Under IID and Real-world, AUC of Fed surpassed centralization model(NonFed) (0.847 ± 0.001, 0.841 ± 0.001 and 0.811 ± 0.001). Under Non-IID, although AUC of Fed (0.846 ± 0.047) outperformed NonFed (0.841 ± 0.001) on validation set, it (0.821 ± 0.016 and 0.799 ± 0.031) slightly lagged behind NonFed (0.847 ± 0.001 and 0.811 ± 0.001) on the training and test sets. In practice, AUC of Fed (0.853, 0.884 and 0.842) outshone PESI (0.812, 0.789 and 0.791), sPESI (0.817, 0.770 and 0.786) and PUMCH(0.848, 0.814 and 0.832) on the training, validation and test sets. Additionally, Fed (0.842) exhibited higher AUC values across test sets compared to those trained directly on the clients (0.758, 0.801, 0.783, 0.741, 0.788). CONCLUSIONS In this study, the FL based machine learning model demonstrated commendable efficacy on PTE prognostic risk prediction, rendering it well-suited for deployment in hospitals.
Collapse
Affiliation(s)
- Jun Zhou
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xin Wang
- Department of Ultrasound, Peking Union Medical College Hospital, Beijing, China
| | - Yiyao Li
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China
| | - Yuqing Yang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Juhong Shi
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China.
| |
Collapse
|
6
|
Wassell M, Vitiello A, Butler-Henderson K, Verspoor K, Pollard H. Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders. JOURNAL OF OCCUPATIONAL REHABILITATION 2024:10.1007/s10926-024-10196-w. [PMID: 38739344 DOI: 10.1007/s10926-024-10196-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/07/2024] [Indexed: 05/14/2024]
Abstract
PURPOSE Electronic Health Records (EHRs) can contain vast amounts of clinical information that could be reused in modelling outcomes of work-related musculoskeletal disorders (WMSDs). Determining the generalizability of an EHR dataset is an important step in determining the appropriateness of its reuse. The study aims to describe the EHR dataset used by occupational musculoskeletal therapists and determine whether the EHR dataset is generalizable to the Australian workers' population and injury characteristics seen in workers' compensation claims. METHODS Variables were considered if they were associated with outcomes of WMSDs and variables data were available. Completeness and external validity assessment analysed frequency distributions, percentage of records and confidence intervals. RESULTS There were 48,434 patient care plans across 10 industries from 2014 to 2021. The EHR collects information related to clinical interventions, health and psychosocial factors, job demands, work accommodations as well as workplace culture, which have all been shown to be valuable variables in determining outcomes to WMSDs. Distributions of age, duration of employment, gender and region of birth were mostly similar to the Australian workforce. Upper limb WMSDs were higher in the EHR compared to workers' compensation claims and diagnoses were similar. CONCLUSION The study shows the EHR has strong potential to be used for further research into WMSDs as it has a similar population to the Australian workforce, manufacturing industry and workers' compensation claims. It contains many variables that may be relevant in modelling outcomes to WMSDs that are not typically available in existing datasets.
Collapse
Affiliation(s)
- M Wassell
- School of Computing Technologies, RMIT University, Melbourne, Australia.
| | - A Vitiello
- School of Health, Medical and Applied Sciences, Central Queensland University, Queensland, Australia
| | - K Butler-Henderson
- STEM|Health and Biomedical Sciences, RMIT University, Melbourne, Australia
| | - K Verspoor
- School of Computing Technologies, RMIT University, Melbourne, Australia
| | - H Pollard
- Faculty of Health Sciences, Durban University of Technology, Durban, South Africa
| |
Collapse
|
7
|
Lee S, Kim JH, Ha HI, Lim MC, Cho H. Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records. JCO Clin Cancer Inform 2024; 8:e2300150. [PMID: 38442323 PMCID: PMC10927333 DOI: 10.1200/cci.23.00150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/18/2023] [Accepted: 01/10/2024] [Indexed: 03/07/2024] Open
Abstract
PURPOSE As the onset of cancer recurrence is not explicitly recorded in the electronic health record (EHR), a high volume of manual chart review is required to detect the cancer recurrence. This study aims to develop an automatic rule-based algorithm for detecting ovarian cancer (OC) recurrence on the basis of minimally preprocessed EHR data. METHODS The automatic rule-based recurrence detection algorithm (Auto-Recur), using notes on image reading (positron emission tomography-computed tomography [PET-CT], CT, magnetic resonance imaging [MRI]), biomarker (CA125), and treatment information (surgery, chemotherapy, radiotherapy), was developed to detect the first OC recurrence. Auto-Recur contains three single algorithms (images, biomarkers, treatments) and hybrid algorithms (combinations of the single algorithms). The performance of Auto-Recur was assessed using sensitivity, specificity, and accuracy of the recurrence time detected. The recurrence-free survival probabilities were estimated and compared with the retrospective chart review results. RESULTS The proposed Auto-Recur considerably reduced human resources and time; it saved approximately 1,340 days when scaled to 100,000 patients compared with the conventional retrospective chart review. The hybrid algorithm on the basis of a combination of image, biomarker, and treatment information was the most efficient (sensitivity: 93.4%, specificity: 97.4%) and precisely captured recurrence time (average time error: 8.5 days). The estimated 3-year recurrence-free survival probability (44%) was close to the estimates by the retrospective chart review (45%, log-rank P value = .894). CONCLUSION Our rule-based algorithm effectively captured the first OC recurrence from large-scale EHR while closely approximating the recurrence-free survival estimates obtained by conventional retrospective chart reviews. The study findings facilitate large-scale EHR analysis, enhancing clinical research opportunities.
Collapse
Affiliation(s)
- Sanghee Lee
- Department of Cancer Control & Population Health, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Republic of Korea
- Health Insurance Research Institute, National Health Insurance Service, Wonju, Republic of Korea
| | - Ji Hyun Kim
- Center for Gynecologic Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Republic of Korea
| | - Hyeong In Ha
- Department of Obstetrics and Gynecology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea
| | - Myong Cheol Lim
- Department of Cancer Control & Population Health, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Republic of Korea
- Center for Gynecologic Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Republic of Korea
- Rare and Pediatric Cancer Branch and Immuno-oncology Branch, Division of Rare and Refractory Cancer, Research Institute, National Cancer Center, Goyang, Republic of Korea
- Center for Clinical Trials, Hospital, National Cancer Center, Goyang, Republic of Korea
| | - Hyunsoon Cho
- Department of Cancer Control & Population Health, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Republic of Korea
- Department of Cancer AI and Digital Health, National Cancer Center Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, South Korea
- Integrated Biostatistics Branch, Division of Cancer Data Science, Research Institute, National Cancer Center, Goyang, Republic of Korea
| |
Collapse
|
8
|
Rose C, Chan T. Invisibility, cloaks and daggers: Balancing clinical hazards in the age of artificial intelligence. J Eval Clin Pract 2024; 30:9-11. [PMID: 36071693 DOI: 10.1111/jep.13758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 08/21/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Christian Rose
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, California, USA
| | - Teresa Chan
- Department of Medicine, Division of Emergency Medicine, McMaster University, Hamilton, Ontario, Canada
| |
Collapse
|
9
|
Garrido MM, Legler A, Strombotne KL, Frakt AB. Differences in adverse outcomes across race and ethnicity among Veterans with similar predicted risks of an overdose or suicide-related event. PAIN MEDICINE (MALDEN, MASS.) 2024; 25:125-130. [PMID: 37738604 DOI: 10.1093/pm/pnad129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/16/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
OBJECTIVE To evaluate the degree to which differences in incidence of mortality and serious adverse events exist across patient race and ethnicity among Veterans Health Administration (VHA) patients receiving outpatient opioid prescriptions and who have similar predicted risks of adverse outcomes. Patients were assigned scores via the VHA Stratification Tool for Opioid Risk Mitigation (STORM), a model used to predict the risk of experiencing overdose- or suicide-related health care events or death. Individuals with the highest STORM risk scores are targeted for case review. DESIGN Retrospective cohort study of high-risk veterans who received an outpatient prescription opioid between 4/2018-3/2019. SETTING All VHA medical centers. PARTICIPANTS In total, 84 473 patients whose estimated risk scores were between 0.0420 and 0.0609, the risk scores associated with the top 5%-10% of risk in the STORM development sample. METHODS We examined the expected probability of mortality and serious adverse events (SAEs; overdose or suicide-related events) given a patient's risk score and race. RESULTS Given a similar risk score, Black patients were less likely than White patients to have a recorded SAE within 6 months of risk score calculation. Black, Hispanic, and Asian patients were less likely than White patients with similar risk scores to die within 6 months of risk score calculation. Some of the mortality differences were driven by age differences in the composition of racial and ethnic groups in our sample. CONCLUSIONS Our results suggest that relying on the STORM model to identify patients who may benefit from an interdisciplinary case review may identify patients with clinically meaningful differences in outcome risk across race and ethnicity.
Collapse
Affiliation(s)
- Melissa M Garrido
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA 02130, United States
- Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA 02118, United States
| | - Aaron Legler
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA 02130, United States
| | - Kiersten L Strombotne
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA 02130, United States
- Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA 02118, United States
| | - Austin B Frakt
- Partnered Evidence-based Policy Resource Center, VA Boston Healthcare System, Boston, MA 02130, United States
- Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA 02118, United States
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Cambridge, MA 02115, United States
| |
Collapse
|
10
|
Bai L, Wu Y, Li G, Zhang W, Zhang H, Su J. AI-enabled organoids: Construction, analysis, and application. Bioact Mater 2024; 31:525-548. [PMID: 37746662 PMCID: PMC10511344 DOI: 10.1016/j.bioactmat.2023.09.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/09/2023] [Accepted: 09/09/2023] [Indexed: 09/26/2023] Open
Abstract
Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.
Collapse
Affiliation(s)
- Long Bai
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
- Wenzhou Institute of Shanghai University, Wenzhou, 325000, China
| | - Yan Wu
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Guangfeng Li
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
- Department of Orthopedics, Shanghai Zhongye Hospital, Shanghai, 201941, China
| | - Wencai Zhang
- Department of Orthopedics, First Affiliated Hospital, Jinan University, Guangzhou, 510632, China
| | - Hao Zhang
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Jiacan Su
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| |
Collapse
|
11
|
Wang J, Ding S, Da C, Chen C, Wu Z, Li C, Zhou G, Tang C. Morphology-Based Prediction of Proliferation and Differentiation Potencies of Porcine Muscle Stem Cells for Cultured Meat Production. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:18613-18621. [PMID: 37963374 DOI: 10.1021/acs.jafc.3c06919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Inconsistent efficiency of cell production caused by cellular quality variations has become a significant problem in the cultured meat industry. In our study, morphological information on passages 5-9 of porcine muscle stem cells (pMuSCs) from three lots was analyzed and used as input data in prediction models. Cell proliferation and differentiation potencies were measured by cell growth rate and average stained area of the myosin heavy chain. Analysis of PCA and heatmap showed that the morphological parameters could be used to discriminate the differences of passages and lots. Various morphological parameters were analyzed, which revealed that accumulating time-course information regarding morphological heterogeneity in cell populations is crucial to predicting the potencies. Based on the 36 and 60 h morphological profiles, the best proliferation potency prediction model (R2 = 0.95, RMSE = 1.1) and differentiation potency prediction model (R2 = 0.74, RMSE = 1.2) were explored. Correlation analysis demonstrated that morphological parameters selected in models are related to the quality of porcine muscle stem cells.
Collapse
Affiliation(s)
- Jiali Wang
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Key Laboratory of Meat Processing, Ministry of Agriculture, Key Lab of Meat Processing and Quality Control, Ministry of Education, Jiangsu Collaborative Innovation Center of Meat Production and Processing, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Shijie Ding
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Key Laboratory of Meat Processing, Ministry of Agriculture, Key Lab of Meat Processing and Quality Control, Ministry of Education, Jiangsu Collaborative Innovation Center of Meat Production and Processing, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Chunyan Da
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Key Laboratory of Meat Processing, Ministry of Agriculture, Key Lab of Meat Processing and Quality Control, Ministry of Education, Jiangsu Collaborative Innovation Center of Meat Production and Processing, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Chengpu Chen
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Key Laboratory of Meat Processing, Ministry of Agriculture, Key Lab of Meat Processing and Quality Control, Ministry of Education, Jiangsu Collaborative Innovation Center of Meat Production and Processing, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhongyuan Wu
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Key Laboratory of Meat Processing, Ministry of Agriculture, Key Lab of Meat Processing and Quality Control, Ministry of Education, Jiangsu Collaborative Innovation Center of Meat Production and Processing, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Chunbao Li
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Key Laboratory of Meat Processing, Ministry of Agriculture, Key Lab of Meat Processing and Quality Control, Ministry of Education, Jiangsu Collaborative Innovation Center of Meat Production and Processing, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Guanghong Zhou
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Key Laboratory of Meat Processing, Ministry of Agriculture, Key Lab of Meat Processing and Quality Control, Ministry of Education, Jiangsu Collaborative Innovation Center of Meat Production and Processing, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Changbo Tang
- State Key Laboratory of Meat Quality Control and Cultured Meat Development, Key Laboratory of Meat Processing, Ministry of Agriculture, Key Lab of Meat Processing and Quality Control, Ministry of Education, Jiangsu Collaborative Innovation Center of Meat Production and Processing, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| |
Collapse
|
12
|
Keegan G, Crown A, DiMaggio C, Joseph KA. Insufficient Reporting of Race and Ethnicity in Breast Cancer Clinical Trials. Ann Surg Oncol 2023; 30:7008-7014. [PMID: 37658271 DOI: 10.1245/s10434-023-14201-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND Reporting race and ethnicity in clinical trial publications is critical for determining the generalizability and effectiveness of new treatments. This is particularly important for breast cancer, in which Black women have been shown to have between 40 and 100% higher mortality rate yet are underrepresented in trials. Our objective was to describe changes over time in the reporting of race/ethnicity in breast trial publications. PATIENTS AND METHODS We searched ClinicalTrials.gov to identify the primary publication linked to trials with results posted from May 2010-2022. Statistical analysis included summed frequencies and a linear regression model of the proportion of articles reporting race/ethnicity and the proportion of non-White enrollees over time. RESULTS A proportion of 72 of the 98 (73.4%) studies that met inclusion criteria reported race/ethnicity. In a linear regression model of the proportion of studies reporting race/ethnicity as a function of time, there was no statistically significant change, although we detected a signal toward a decreasing trend (coefficient for quarter = -2.2, p = 0.2). Among all studies reporting race and ethnicity over the study period, the overall percentage of non-White enrollees during the study period was 21.9%, [standard error (s.e.) 1.8, 95% confidence interval (CI) 18.4, 25.5] with a signal towards a decreasing trend in Non-White enrollment [coefficient for year-quarter = -0.8 (p = 0.2)]. CONCLUSION Our data demonstrate that both race reporting and overall representation of minority groups in breast cancer clinical trials did not improve over the last 12 years and may have, in fact, decreased. Increased reporting of race and ethnicity data forces the medical community to confront disparities in access to clinical trials. This may improve efforts to recruit and retain members of minority groups in clinical trials, and over time, reduce racial disparities in oncologic outcomes.
Collapse
Affiliation(s)
- Grace Keegan
- Pritzker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Angelena Crown
- True Family Women's Cancer Center, Swedish Cancer Institute, Seattle, WA, USA
| | - Charles DiMaggio
- Department of Surgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Kathie-Ann Joseph
- Department of Surgery, NYU Grossman School of Medicine, NYULH Institute of Excellence in Health Equity, New York, NY, USA.
| |
Collapse
|
13
|
Lukyanenko R, Storey VC, Pastor O. Conceptual modelling for life sciences based on systemist foundations. BMC Bioinformatics 2023; 23:574. [PMID: 37312025 PMCID: PMC10262140 DOI: 10.1186/s12859-023-05287-z] [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: 02/17/2022] [Accepted: 04/12/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms. RESULTS This work proposes a "systemist" perspective for creating a conceptual model of a life scientist's problem. We introduce the notion of a system and then show how it can be applied to the development of an information system for handling genomic-related information. We extend our discussion to show how the proposed systemist perspective can support the modelling of precision medicine. CONCLUSION This research recognizes challenges in life sciences research of how to model problems to better represent the connections between physical and digital worlds. We propose a new notation that explicitly incorporates systemist thinking, as well as the components of systems based on recent ontological foundations. The new notation captures important semantics in the domain of life sciences. It may be used to facilitate understanding, communication and problem-solving more broadly. We also provide a precise, sound, ontologically supported characterization of the term "system," as a basic construct for conceptual modelling in life sciences.
Collapse
Affiliation(s)
- Roman Lukyanenko
- McIntire School of Commerce, University of Virginia, Charlottesville, VA, USA
| | - Veda C Storey
- J. Mack Robinson College of Business, Dept. of Computer Information Systems, Georgia State University, Atlanta, GA, USA
| | - Oscar Pastor
- PROS Research Center, VRAIN Research Institute, Universidad Politecnica de Valencia, Valencia, Spain.
| |
Collapse
|
14
|
Krisanapan P, Tangpanithandee S, Thongprayoon C, Pattharanitima P, Cheungpasitporn W. Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence. J Clin Med 2023; 12:jcm12083018. [PMID: 37109354 PMCID: PMC10143586 DOI: 10.3390/jcm12083018] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 04/20/2023] [Indexed: 04/29/2023] Open
Abstract
Chronic kidney disease (CKD) poses a significant public health challenge, affecting approximately 11% to 13% of the global population [...].
Collapse
Affiliation(s)
- Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
- Division of Nephrology, Department of Internal Medicine, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pattharawin Pattharanitima
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
15
|
Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol 2023; 91:1-15. [PMID: 36801447 DOI: 10.1016/j.semcancer.2023.02.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/30/2023] [Accepted: 02/15/2023] [Indexed: 02/21/2023]
Abstract
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
Collapse
|
16
|
Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 2023; 36:1101-1117. [PMID: 36786976 PMCID: PMC10227138 DOI: 10.1007/s40620-023-01573-4] [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: 08/06/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. METHODS We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. RESULTS From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. CONCLUSIONS Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
Collapse
|
17
|
A Framework for Automatic Clustering of EHR Messages Using a Spatial Clustering Approach. Healthcare (Basel) 2023; 11:healthcare11030390. [PMID: 36766965 PMCID: PMC9914110 DOI: 10.3390/healthcare11030390] [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: 12/06/2022] [Revised: 01/12/2023] [Accepted: 01/16/2023] [Indexed: 02/03/2023] Open
Abstract
Although Health Level Seven (HL 7) message standards (v2, v3, Clinical Document Architecture (CDA)) have been commonly adopted, there are still issues associated with them, especially the semantic interoperability issues and lack of support for smart devices (e.g., smartphones, fitness trackers, and smartwatches), etc. In addition, healthcare organizations in many countries are still using proprietary electronic health record (EHR) message formats, making it challenging to convert to other data formats-particularly the latest HL7 Fast Health Interoperability Resources (FHIR) data standard. The FHIR is based on modern web technologies such as HTTP, XML, and JSON and would be capable of overcoming the shortcomings of the previous standards and supporting modern smart devices. Therefore, the FHIR standard could help the healthcare industry to avail the latest technologies benefits and improve data interoperability. The data representation and mapping from the legacy data standards (i.e., HL7 v2 and EHR) to the FHIR is necessary for the healthcare sector. However, direct data mapping or conversion from the traditional data standards to the FHIR data standard is challenging because of the nature and formats of the data. Therefore, in this article, we propose a framework that aims to convert proprietary EHR messages into the HL7 v2 format and apply an unsupervised clustering approach using the DBSCAN (density-based spatial clustering of applications with noise) algorithm to automatically group a variety of these HL7 v2 messages regardless of their semantic origins. The proposed framework's implementation lays the groundwork to provide a generic mapping model with multi-point and multi-format data conversion input into the FHIR. Our experimental results show the proposed framework's ability to automatically cluster various HL7 v2 message formats and provide analytic insight behind them.
Collapse
|
18
|
Bowe AK, Lightbody G, Staines A, Kiely ME, McCarthy FP, Murray DM. Predicting Low Cognitive Ability at Age 5-Feature Selection Using Machine Learning Methods and Birth Cohort Data. Int J Public Health 2022; 67:1605047. [PMID: 36439276 PMCID: PMC9684182 DOI: 10.3389/ijph.2022.1605047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/24/2022] [Indexed: 02/10/2024] Open
Abstract
Objectives: In this study, we applied the random forest (RF) algorithm to birth-cohort data to train a model to predict low cognitive ability at 5 years of age and to identify the important predictive features. Methods: Data was from 1,070 participants in the Irish population-based BASELINE cohort. A RF model was trained to predict an intelligence quotient (IQ) score ≤90 at age 5 years using maternal, infant, and sociodemographic features. Feature importance was examined and internal validation performed using 10-fold cross validation repeated 5 times. Results The five most important predictive features were the total years of maternal schooling, infant Apgar score at 1 min, socioeconomic index, maternal BMI, and alcohol consumption in the first trimester. On internal validation a parsimonious RF model based on 11 features showed excellent predictive ability, correctly classifying 95% of participants. This provides a foundation suitable for external validation in an unseen cohort. Conclusion: Machine learning approaches to large existing datasets can provide accurate feature selection to improve risk prediction. Further validation of this model is required in cohorts representative of the general population.
Collapse
Affiliation(s)
| | - Gordon Lightbody
- INFANT Research Centre, Cork, Ireland
- Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | - Anthony Staines
- School of Nursing, Psychotherapy, and Community Health, Dublin City University, Dublin, Ireland
| | - Mairead E. Kiely
- INFANT Research Centre, Cork, Ireland
- Cork Centre for Vitamin D and Nutrition Research, School of Food and Nutritional Sciences, University College Cork, Cork, Ireland
| | - Fergus P. McCarthy
- INFANT Research Centre, Cork, Ireland
- Department of Obstetrics and Gynaecology, Cork University Maternity Hospital, Cork, Ireland
| | - Deirdre M. Murray
- INFANT Research Centre, Cork, Ireland
- Department of Paediatrics, Cork University Hospital, Cork, Ireland
| |
Collapse
|
19
|
Yoo J, Yoo I, Youn I, Kim SM, Yu R, Kim K, Kim K, Lee SB. Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107079. [PMID: 36191354 DOI: 10.1016/j.cmpb.2022.107079] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 07/25/2022] [Accepted: 08/20/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Neuromuscular disorders are diseases that damage our ability to control body movements. Needle electromyography (nEMG) is often used to diagnose neuromuscular disorders, which is an electrophysiological test measuring electric signals generated from a muscle using an invasive needle. Characteristics of nEMG signals are manually analyzed by an electromyographer to diagnose the types of neuromuscular disorders, and this process is highly dependent on the subjective experience of the electromyographer. Contemporary computer-aided methods utilized deep learning image classification models to classify nEMG signals which are not optimized for classifying signals. Additionally, model explainability was not addressed which is crucial in medical applications. This study aims to improve prediction accuracy, inference time, and explain model predictions in nEMG neuromuscular disorder classification. METHODS This study introduces the nEMGNet, a one-dimensional convolutional neural network with residual connections designed to extract features from raw signals with higher accuracy and faster speed compared to image classification models from previous works. Next, the divide-and-vote (DiVote) algorithm was designed to integrate each subject's heterogeneous nEMG signal data structures and to utilize muscle subtype information for higher accuracy. Finally, feature visualization was used to identify the causality of nEMGNet diagnosis predictions, to ensure that nEMGNet made predictions on valid features, not artifacts. RESULTS The proposed method was tested using 376 nEMG signals measured from 57 subjects between June 2015 to July 2020 in Seoul National University Hospital. The results from the three-class classification task demonstrated that nEMGNet's prediction accuracy of nEMG signal segments was 62.35%, and the subject diagnosis prediction accuracy of nEMGNet and the DiVote algorithm was 83.69 %, over 5-fold cross-validation. nEMGNet outperformed all models from previous works on nEMG diagnosis classification, and heuristic analysis of feature visualization results indicate that nEMGNet learned relevant nEMG signal characteristics. CONCLUSIONS This study introduced nEMGNet and DiVote algorithm which demonstrated fast and accurate performance in predicting neuromuscular disorders based on nEMG signals. The proposed method may be applied in medicine to support real-time electrophysiologic diagnosis.
Collapse
Affiliation(s)
- Jaesung Yoo
- School of Electrical Engineering, Korea University, Seoul, Republic of Korea
| | - Ilhan Yoo
- Department of Neurology, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Republic of Korea
| | - Ina Youn
- Department of Computer Science, New York University, NY, USA
| | - Sung-Min Kim
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ri Yu
- Department of Software and Computer Engineering, Department of Artificial Intelligence, Ajou University
| | - Kwangsoo Kim
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Keewon Kim
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Seung-Bo Lee
- Department of Medical Informatics: Keimyung University School of Medicine, Daegu, Republic of Korea.
| |
Collapse
|
20
|
Guo Y, Yang J, Liu Y, Chen X, Yang GZ. Detection and assessment of Parkinson's disease based on gait analysis: A survey. Front Aging Neurosci 2022; 14:916971. [PMID: 35992585 PMCID: PMC9382193 DOI: 10.3389/fnagi.2022.916971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.
Collapse
Affiliation(s)
- Yao Guo
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jianxin Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxuan Liu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
21
|
Xiao L, Zhou H, Fox J. Towards a systematic approach for argumentation, recommendation, and explanation in clinical decision support. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:10445-10473. [PMID: 36032002 DOI: 10.3934/mbe.2022489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In clinical decision support, argumentation plays a key role while alternative reasons may be available to explain a given set of signs and symptoms, or alternative plans to treat a diagnosed disease. In literature, this key notion usually has closed boundary across approaches and lacks of openness and interoperability in Clinical Decision Support Systems (CDSSs) been built. In this paper, we propose a systematic approach for the representation of argumentation, their interpretation towards recommendation, and finally explanation in clinical decision support. A generic argumentation and recommendation scheme lays the foundation of the approach. On the basis of this, argumentation rules are represented using Resource Description Framework (RDF) for clinical guidelines, a rule engine developed for their interpretation, and recommendation rules represented using Semantic Web Rule Language (SWRL). A pair of proof knowledge graphs are made available in an integrated clinical decision environment to explain the argumentation and recommendation rationale, so that decision makers are informed of not just what are recommended but also why. A case study of triple assessment, a common procedure in the National Health Service of UK for women suspected of breast cancer, is used to demonstrate the feasibility of the approach. In conducting hypothesis testing, we evaluate the metrics of accuracy, variation, adherence, time, satisfaction, confidence, learning, and integration of the prototype CDSS developed for the case study in comparison with a conventional CDSS and also human clinicians without CDSS. The results are presented and discussed.
Collapse
Affiliation(s)
- Liang Xiao
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - Hao Zhou
- Network & Informatization Center, Wuhan Polytechnic University, Wuhan, China
| | - John Fox
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
22
|
Turner BE, Steinberg JR, Weeks BT, Rodriguez F, Cullen MR. Race/ethnicity reporting and representation in US clinical trials: a cohort study. LANCET REGIONAL HEALTH. AMERICAS 2022; 11:100252. [PMID: 35875251 PMCID: PMC9302767 DOI: 10.1016/j.lana.2022.100252] [Citation(s) in RCA: 85] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
BACKGROUND Systemic progress in improving trial representation is uncertain, and previous analyses of minority trial participation have been limited to small cohorts with limited exploration of driving factors. METHODS We analyzed detailed trial records from all US clinical trials registered in ClinicalTrials.gov from March 2000 to March 2020. Minority enrollment was compared to 2010 US Census demographic estimates using Wilcoxon test. We utilized logistic regression and generalized linear regression with a logit link to assess the association of possible drivers (including trials' funding source, size, phase, and design) with trials' disclosure of and amount of minority enrollment respectively. FINDINGS Among 20,692 US-based trials with reported results (representing ~4·76 million enrollees), only 43% (8,871/20,692) reported any race/ethnicity data. The majority of enrollees were White (median 79·7%; interquartile range [IQR] 61·9-90·0%), followed by Black (10·0%; IQR 2·5-23·5%), Hispanic/Latino (6·0%; IQR 0·43-15·4%), Asian (1·0%; IQR 0·0-4·1%), and American Indian (0·0%; IQR 0·0-0·2%). Median combined enrollment of minority race/ethnicity groups (Black, Hispanic/Latino, Asian, American Indian, Other/Multi) was below census estimates (27·6%) (p<0·001) however increased at an annual rate of 1·7%. Industry and Academic funding were negatively associated with race/ethnicity reporting (Industry adjusted odds ratio [aOR]: 0·42, 95% confidence interval [CI]: 0·38 to 0·46, p<0.0001; Academic aOR: 0·45, CI: 0·41 to 0·50, p<0.0001). Industry also had a negative association with the proportion of minority ethnicity enrollees (aOR: 0·69, CI: 0·60 to 0·79) compared to US Government-funded trials. INTERPRETATION Over the past two decades, the majority of US trials in ClinicalTrials.gov do not report race/ethnicity enrollment data, and minorities are underrepresented in trials with modest improvement over time. FUNDING Stanford Medical Scholars Research Funding, the National Heart, Lung, and Blood Institute, NIH (1K01HL144607) and the American Heart Association/Robert Wood Johnson Medical Faculty Development Program.
Collapse
Affiliation(s)
- Brandon E. Turner
- Stanford University School of Medicine, Stanford, CA, USA
- Massachusetts General Hospital, 55 Fruit Street, Lunder Building LL3, Boston, MA 02114, USA
| | | | | | - Fatima Rodriguez
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Mark R. Cullen
- Center for Population Health Sciences, Stanford University School of Medicine, Stanford, CA, USA
| |
Collapse
|
23
|
Ndjaboue R, Ngueta G, Rochefort-Brihay C, Delorme S, Guay D, Ivers N, Shah BR, Straus SE, Yu C, Comeau S, Farhat I, Racine C, Drescher O, Witteman HO. Prediction models of diabetes complications: a scoping review. J Epidemiol Community Health 2022; 76:jech-2021-217793. [PMID: 35772935 DOI: 10.1136/jech-2021-217793] [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: 08/11/2021] [Accepted: 06/08/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes. METHODS Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards. RESULTS Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance. CONCLUSION This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.
Collapse
Affiliation(s)
- Ruth Ndjaboue
- Faculty of Medicine, Université Laval, Quebec, Quebec, Canada
- School of social work, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- CIUSSS de l'Estrie, Research Centre on Aging, Sherbrooke, Quebec, Canada
| | - Gérard Ngueta
- Université de Sherbrooke Faculté des Sciences, Sherbrooke, Quebec, Canada
| | | | | | - Daniel Guay
- Diabetes Action Canada, Toronto, Ontario, Canada
| | - Noah Ivers
- Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada
- Department of Family Medicine and Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Baiju R Shah
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Sharon E Straus
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Catherine Yu
- Knowledge Translation, St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, Ontario, Canada
| | - Sandrine Comeau
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Imen Farhat
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Charles Racine
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Olivia Drescher
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Holly O Witteman
- Family and Emergency Medicine, Laval University, Quebec City, Quebec, Canada
| |
Collapse
|
24
|
Imai Y, Iida M, Kanie K, Katsuno M, Kato R. Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells. Sci Rep 2022; 12:9296. [PMID: 35710681 PMCID: PMC9203459 DOI: 10.1038/s41598-022-12250-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 04/20/2022] [Indexed: 11/20/2022] Open
Abstract
Label-free image analysis has several advantages with respect to the development of drug screening platforms. However, the evaluation of drug-responsive cells based exclusively on morphological information is challenging, especially in cases of morphologically heterogeneous cells or a small subset of drug-responsive cells. We developed a novel label-free cell sub-population analysis method called “in silico FOCUS (in silico analysis of featured-objects concentrated by anomaly discrimination from unit space)” to enable robust phenotypic screening of morphologically heterogeneous spinal and bulbar muscular atrophy (SBMA) model cells. This method with the anomaly discrimination concept can sensitively evaluate drug-responsive cells as morphologically anomalous cells through in silico cytometric analysis. As this algorithm requires only morphological information of control cells for training, no labeling or drug administration experiments are needed. The responses of SBMA model cells to dihydrotestosterone revealed that in silico FOCUS can identify the characteristics of a small sub-population with drug-responsive phenotypes to facilitate robust drug response profiling. The phenotype classification model confirmed with high accuracy the SBMA-rescuing effect of pioglitazone using morphological information alone. In silico FOCUS enables the evaluation of delicate quality transitions in cells that are difficult to profile experimentally, including primary cells or cells with no known markers.
Collapse
Affiliation(s)
- Yuta Imai
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
| | - Madoka Iida
- Department of Neurology, Nagoya University Graduate School of Medicine, Tokai National Higher Education and Research System, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Kei Kanie
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, Tokai National Higher Education and Research System, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.,Institute of Nano-Life-Systems, Institutes of Innovation for Future Society, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan.,Department of Clinical Research Education, Nagoya University Graduate School of Medicine, Tokai National Higher Education and Research System, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.,Institute for Glyco-Core Research (iGCORE), Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
| | - Ryuji Kato
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan. .,Institute of Nano-Life-Systems, Institutes of Innovation for Future Society, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan. .,Institute for Glyco-Core Research (iGCORE), Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan.
| |
Collapse
|
25
|
Wu C, Wu F, Lyu L, Qi T, Huang Y, Xie X. A federated graph neural network framework for privacy-preserving personalization. Nat Commun 2022; 13:3091. [PMID: 35654792 PMCID: PMC9163103 DOI: 10.1038/s41467-022-30714-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 05/13/2022] [Indexed: 01/21/2023] Open
Abstract
Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, we present a federated GNN framework named FedPerGNN for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. Experimental results on six datasets for personalization in different scenarios show that FedPerGNN achieves 4.0% ~ 9.6% lower errors than the state-of-the-art federated personalization methods under good privacy protection. FedPerGNN provides a promising direction to mining decentralized graph data in a privacy-preserving manner for responsible and intelligent personalization.
Collapse
Affiliation(s)
- Chuhan Wu
- Department of Electronic Engineering, Tsinghua University, 100084, Beijing, China
| | - Fangzhao Wu
- Microsoft Research Asia, 100080, Beijing, China.
| | - Lingjuan Lyu
- Sony AI, 1-7-1 Konan Minato-ku, Tokyo, 108-0075, Japan
| | - Tao Qi
- Department of Electronic Engineering, Tsinghua University, 100084, Beijing, China
| | - Yongfeng Huang
- Department of Electronic Engineering, Tsinghua University, 100084, Beijing, China.
| | - Xing Xie
- Microsoft Research Asia, 100080, Beijing, China
| |
Collapse
|
26
|
Nguyen TV, Dakka MA, Diakiw SM, VerMilyea MD, Perugini M, Hall JMM, Perugini D. A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data. Sci Rep 2022; 12:8888. [PMID: 35614106 PMCID: PMC9133021 DOI: 10.1038/s41598-022-12833-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/06/2022] [Indexed: 11/09/2022] Open
Abstract
Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI training. Data-centric, cross-silo federated learning represents a pathway forward for training on distributed medical datasets. Existing approaches typically require updates to a training model to be transferred to a central server, potentially breaching data privacy laws unless the updates are sufficiently disguised or abstracted to prevent reconstruction of the dataset. Here we present a completely decentralized federated learning approach, using knowledge distillation, ensuring data privacy and protection. Each node operates independently without needing to access external data. AI accuracy using this approach is found to be comparable to centralized training, and when nodes comprise poor-quality data, which is common in healthcare, AI accuracy can exceed the performance of traditional centralized training.
Collapse
Affiliation(s)
- T V Nguyen
- Presagen, Adelaide, SA, 5000, Australia. .,School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, 2522, Australia.
| | - M A Dakka
- Presagen, Adelaide, SA, 5000, Australia.,School of Mathematical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
| | | | - M D VerMilyea
- Ovation Fertility, Austin, TX, 78731, USA.,Texas Fertility Center, Austin, TX, 78731, USA
| | - M Perugini
- Presagen, Adelaide, SA, 5000, Australia.,Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5000, Australia
| | - J M M Hall
- Presagen, Adelaide, SA, 5000, Australia.,Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, SA, 5005, Australia.,School of Physical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
| | | |
Collapse
|
27
|
Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci 2022; 23:4645. [PMID: 35563034 PMCID: PMC9104788 DOI: 10.3390/ijms23094645] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
Collapse
Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - Faryal Mehwish Awan
- Department of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan;
| | - Anam Naz
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Oscar Alvarez
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | - Ana Cernea
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | | | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43205, USA
| |
Collapse
|
28
|
Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
Collapse
Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| |
Collapse
|
29
|
Zhalechian M, Van Oyen MP, Lavieri MS, De Moraes CG, Girkin CA, Fazio MA, Weinreb RN, Bowd C, Liebmann JM, Zangwill LM, Andrews CA, Stein JD. Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study. OPHTHALMOLOGY SCIENCE 2022; 2:100097. [PMID: 36246178 PMCID: PMC9560647 DOI: 10.1016/j.xops.2021.100097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 11/17/2021] [Accepted: 12/01/2021] [Indexed: 11/28/2022]
Abstract
Purpose To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indices on perimetry can be enhanced by adding global retinal nerve fiber layer (RNFL) data and whether model performance is influenced by the racial composition of the training and testing sets. Design Retrospective, longitudinal cohort study. Participants Patients with open-angle glaucoma (OAG) or glaucoma suspects enrolled in the African Descent and Glaucoma Evaluation Study or Diagnostic Innovation in Glaucoma Study. Methods We developed a Kalman filter (KF) with tonometry and perimetry data (KF-TP) and another KF with tonometry, perimetry, and global RNFL data (KF-TPO), comparing these models with one another and with 2 linear regression (LR) models for predicting mean deviation (MD) and pattern standard deviation values 36 months into the future for patients with OAG and glaucoma suspects. We also compared KF model performance when trained on individuals of European and African descent and tested on patients of the same versus the other race. Main Outcome Measures Predictive accuracy (percentage of MD values forecasted within the 95% repeatability interval) differences among the models. Results Among 362 eligible patients, the mean ± standard deviation age at baseline was 71.3 ± 10.4 years; 196 patients (54.1%) were women; 202 patients (55.8%) were of European descent, and 139 (38.4%) were of African descent. Among patients with OAG (n = 296), the predictive accuracy for 36 months in the future was higher for the KF models (73.5% for KF-TP, 71.2% for KF-TPO) than for the LR models (57.5%, 58.0%). Predictive accuracy did not differ significantly between KF-TP and KF-TPO (P = 0.20). If the races of the training and testing set patients were aligned (versus nonaligned), the mean absolute prediction error of future MD improved 0.39 dB for KF-TP and 0.48 dB for KF-TPO. Conclusions Adding global RNFL data to existing KFs minimally improved their predictive accuracy. Although KFs attained better predictive accuracy when the races of the training and testing sets were aligned, these improvements were modest. These findings will help to guide implementation of KFs in clinical practice.
Collapse
Key Words
- AD, African descent
- ADAGES, African Descent and Glaucoma Evaluation Study
- Algorithm bias
- CI, confidence interval
- D, diopter
- DIGS, Diagnostic Innovation in Glaucoma Study
- ED, European descent
- Glaucoma
- IOP, intraocular pressure
- KF, Kalman filter
- KF-TP, Kalman filter with tonometry and perimetry data
- KF-TPO, Kalman filter with tonometry, perimetry, and global retinal nerve fiber layer data
- Kalman filter
- LR1, linear regression model 1
- LR2, linear regression model 2
- MAE, mean absolute error
- MD, mean deviation
- Machine learning
- OAG, open-angle glaucoma
- OCT
- PSD, pattern standard deviation
- RMSE, root mean square error
- RNFL, retinal nerve fiber layer
- SD, standard deviation
- VF, visual field
Collapse
Affiliation(s)
- Mohammad Zhalechian
- Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan
| | - Mark P. Van Oyen
- Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan
| | - Mariel S. Lavieri
- Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan
| | - Carlos Gustavo De Moraes
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, New York
| | - Christopher A. Girkin
- Department of Ophthalmology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Massimo A. Fazio
- Department of Ophthalmology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Robert N. Weinreb
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
| | - Christopher Bowd
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
| | - Jeffrey M. Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, New York
| | - Linda M. Zangwill
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California
| | - Christopher A. Andrews
- Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, Michigan
- Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, Michigan
| | - Joshua D. Stein
- Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, Michigan
- Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, Michigan
- Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan
| |
Collapse
|
30
|
Rose C, Díaz M, Díaz T. Addressing Medicine’s Dark Matter (Preprint). Interact J Med Res 2022; 11:e37584. [PMID: 35976194 PMCID: PMC9434397 DOI: 10.2196/37584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 07/19/2022] [Accepted: 07/31/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Christian Rose
- Department of Emergency Medicine, School of Medicine, Stanford University, Palo Alto, CA, United States
| | - Mark Díaz
- Ethical AI, Google, New York, NY, United States
| | - Tomás Díaz
- Department of Emergency Medicine, Columbia University Medical Center, New York, NY, United States
| |
Collapse
|
31
|
Imai Y, Kanie K, Kato R. Morphological heterogeneity description enabled early and parallel non-invasive prediction of T-cell proliferation inhibitory potency and growth rate for facilitating donor selection of human mesenchymal stem cells. Inflamm Regen 2022; 42:8. [PMID: 35093181 PMCID: PMC8801074 DOI: 10.1186/s41232-021-00192-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 12/30/2021] [Indexed: 11/10/2022] Open
Abstract
Background Within the extensively developed therapeutic application of mesenchymal stem cells (MSCs), allogenic immunomodulatory therapy is among the promising categories. Although donor selection is a critical early process that can maximize the production yield, determining the promising candidate is challenging owing to the lack of effective biomarkers and variations of cell sources. In this study, we developed the morphology-based non-invasive prediction models for two quality attributes, the T-cell proliferation inhibitory potency and growth rate. Methods Eleven lots of mixing bone marrow-derived and adipose-derived MSCs were analyzed. Their morphological profiles and growth rates were quantified by image processing by acquiring 6 h interval time-course phase-contrast microscopic image acquisition. T-cell proliferation inhibitory potency was measured by employing flow cytometry for counting the proliferation rate of peripheral blood mononuclear cells (PBMCs) co-cultured with MSCs. Subsequently, the morphological profile comprising 32 parameters describing the time-course transition of cell population distribution was used for explanatory parameters to construct T-cell proliferation inhibitory potency classification and growth rate prediction models. For constructing prediction models, the effect of machine learning methods, parameter types, and time-course window size of morphological profiles were examined to identify those providing the best performance. Results Unsupervised morphology-based visualization enabled the identification of anomaly lots lacking T-cell proliferation inhibitory potencies. The best performing machine learning models exhibited high performances of predictions (accuracy > 0.95 for classifying risky lots, and RMSE < 1.50 for predicting growth rate) using only the first 4 days of morphological profiles. A comparison of morphological parameter types showed that the accumulated time-course information of morphological heterogeneity in cell populations is important for predicting the potencies. Conclusions To enable more consistent cell manufacturing of allogenic MSC-based therapeutic products, this study indicated that early non-invasive morphology-based prediction can facilitate the lot selection process for effective cell bank establishment. It was also found that morphological heterogeneity description is important for such potency prediction. Furthermore, performances of the morphology-based prediction models trained with data consisting of origin-different MSCs demonstrated the effectiveness of sharing morphological data between different types of MSCs, thereby complementing the data limitation issue in the morphology-based quality prediction concept. Supplementary Information The online version contains supplementary material available at 10.1186/s41232-021-00192-5.
Collapse
|
32
|
Abstract
With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems. Effective RWD utilization requires disparate data sources to be turned into high-quality datasets. To harness the potential of RWD for emerging use cases, providers and organizations must accelerate life cycle improvements that support this process. We build on examples obtained from the academic literature and author experience of data curation practices across a diverse range of sectors to describe a standardized RWD life cycle containing key steps in production of useful data for analysis and insights. We delineate best practices that will add value to current data pipelines. Seven themes are highlighted that ensure sustainability and scalability for RWD life cycles: data standards adherence, tailored quality assurance, data entry incentivization, deploying natural language processing, data platform solutions, RWD governance, and ensuring equity and representation in data.
Collapse
|
33
|
Chevrette MG, Gavrilidou A, Mantri S, Selem-Mojica N, Ziemert N, Barona-Gómez F. The confluence of big data and evolutionary genome mining for the discovery of natural products. Nat Prod Rep 2021; 38:2024-2040. [PMID: 34787598 DOI: 10.1039/d1np00013f] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This review covers literature between 2003-2021The development and application of genome mining tools has given rise to ever-growing genetic and chemical databases and propelled natural products research into the modern age of Big Data. Likewise, an explosion of evolutionary studies has unveiled genetic patterns of natural products biosynthesis and function that support Darwin's theory of natural selection and other theories of adaptation and diversification. In this review, we aim to highlight how Big Data and evolutionary thinking converge in the study of natural products, and how this has led to an emerging sub-discipline of evolutionary genome mining of natural products. First, we outline general principles to best utilize Big Data in natural products research, addressing key considerations needed to provide evolutionary context. We then highlight successful examples where Big Data and evolutionary analyses have been combined to provide bioinformatic resources and tools for the discovery of novel natural products and their biosynthetic enzymes. Rather than an exhaustive list of evolution-driven discoveries, we highlight examples where Big Data and evolutionary thinking have been embraced for the evolutionary genome mining of natural products. After reviewing the nascent history of this sub-discipline, we discuss the challenges and opportunities of genomic and metabolomic tools with evolutionary foundations and/or implications and provide a future outlook for this emerging and exciting field of natural product research.
Collapse
Affiliation(s)
- Marc G Chevrette
- Wisconsin Institute for Discovery, Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI, USA
| | - Athina Gavrilidou
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Interfaculty Institute for Biomedical Informatics (IBMI), University of Tübingen, Germany.,German Centre for Infection Research (DZIF), Partner Site Tübingen, Germany.
| | - Shrikant Mantri
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Interfaculty Institute for Biomedical Informatics (IBMI), University of Tübingen, Germany.,German Centre for Infection Research (DZIF), Partner Site Tübingen, Germany. .,Computational Biology Laboratory, National Agri-Food Biotechnology Institute (NABI), Mohali, Punjab, India
| | - Nelly Selem-Mojica
- Laboratorio de Evolución de la Diversidad Metabólica, Unidad de Genómica Avanzada (Langebio), Cinvestav-IPN, Irapuato, Guanajuato, Mexico.
| | - Nadine Ziemert
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Interfaculty Institute for Biomedical Informatics (IBMI), University of Tübingen, Germany.,German Centre for Infection Research (DZIF), Partner Site Tübingen, Germany.
| | - Francisco Barona-Gómez
- Laboratorio de Evolución de la Diversidad Metabólica, Unidad de Genómica Avanzada (Langebio), Cinvestav-IPN, Irapuato, Guanajuato, Mexico.
| |
Collapse
|
34
|
Abstract
A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.
Collapse
|
35
|
Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan P, Xu D, Wu D, Huang E, Kitamura FC, Lacey G, de Antônio Corradi GC, Nino G, Shin HH, Obinata H, Ren H, Crane JC, Tetreault J, Guan J, Garrett JW, Kaggie JD, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru MG, Haider MA, AbdelMaseeh M, Rieke N, Damasceno PF, E Silva PMC, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist TM, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon YJ, Quraini A, Feng A, Priest AN, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Díez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess CP, Compas C, Bhatia D, Oermann EK, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonça MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod SL, Reed S, Gräf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lavor VL, Rakvongthai Y, Lee YR, Wen Y, Gilbert FJ, Flores MG, Li Q. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med 2021; 27:1735-1743. [PMID: 34526699 PMCID: PMC9157510 DOI: 10.1038/s41591-021-01506-3] [Citation(s) in RCA: 172] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 08/13/2021] [Indexed: 02/08/2023]
Abstract
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.
Collapse
Affiliation(s)
- Ittai Dayan
- MGH Radiology and Harvard Medical School, Boston, MA, USA
| | | | - Aoxiao Zhong
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | | | | | | | | | | | - Bradford J Wood
- Radiology & Imaging Sciences/Clinical Center, National Institutes of Health, Bethesda, MD, USA
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Chien-Sung Tsai
- Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Chun-Nan Hsu
- Center for Research in Biological Systems, University of California, San Diego, CA, USA
| | - C K Lee
- NVIDIA, Santa Clara, CA, USA
| | | | | | - Dufan Wu
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | - Gustavo Nino
- Division of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, Washington, DC, USA
| | - Hao-Hsin Shin
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Hui Ren
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jason C Crane
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | | | | | - John W Garrett
- Departments of Radiology and Medical Physics, The University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Joshua D Kaggie
- Department of Radiology, NIHR Cambridge Biomedical Resource Centre, University of Cambridge, Cambridge, UK
| | - Jung Gil Park
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | - Keith Dreyer
- MGH Radiology and Harvard Medical School, Boston, MA, USA
- Center for Clinical Data Science, Massachusetts General Brigham, Boston, MA, USA
| | - Krishna Juluru
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA
- Departments of Radiology and Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Masoom A Haider
- Joint Dept. of Medical Imaging, Sinai Health System, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | | | | | - Pablo F Damasceno
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | | | - Pochuan Wang
- MeDA Lab Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Sheng Xu
- Radiology & Imaging Sciences/Clinical Center, National Institutes of Health, Bethesda, MD, USA
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Sira Sriswasdi
- Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center for Artificial Intelligence in Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Soo Young Park
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Thomas M Grist
- Departments of Radiology, Medical Physics, and Biomedical Engineering, The University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Varun Buch
- Center for Clinical Data Science, Massachusetts General Brigham, Boston, MA, USA
| | - Watsamon Jantarabenjakul
- Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Emerging Infectious Diseases Clinical Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Weichung Wang
- MeDA Lab Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Won Young Tak
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Xiang Li
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Xihong Lin
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Young Joon Kwon
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | - Andrew N Priest
- Department of Radiology, NIHR Cambridge Biomedical Resource Centre, Cambridge University Hospital, Cambridge, UK
| | - Baris Turkbey
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Benjamin Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bernardo Bizzo
- Center for Clinical Data Science, Massachusetts General Brigham, Boston, MA, USA
| | - Byung Seok Kim
- Department of Internal Medicine, Catholic University of Daegu School of Medicine, Daegu, South Korea
| | - Carlos Tor-Díez
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA
| | - Chia-Cheng Lee
- Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Jung Hsu
- Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chin Lin
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Chiu-Ling Lai
- Medical Review and Pharmaceutical Benefits Division, National Health Insurance Administration, Taipei, Taiwan
| | - Christopher P Hess
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | | | | | - Eric K Oermann
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Evan Leibovitz
- Center for Clinical Data Science, Massachusetts General Brigham, Boston, MA, USA
| | | | - Hitoshi Mori
- Self-Defense Forces Central Hospital, Tokyo, Japan
| | | | - Jae Ho Sohn
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | | | - Li-Chen Fu
- MOST/NTU All Vista Healthcare Center, Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan
| | | | - Mike Fralick
- Division of General Internal Medicine and Geriatrics (Fralick), Sinai Health System, Toronto, Ontario, Canada
| | - Min Kyu Kang
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | | | - Natalie Gangai
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peerapon Vateekul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | | | - Sarah Hickman
- Department of Radiology, NIHR Cambridge Biomedical Resource Centre, University of Cambridge, Cambridge, UK
| | - Sharmila Majumdar
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Shelley L McLeod
- Schwartz/Reisman Emergency Medicine Institute, Sinai Health, Toronto, Ontario, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sheridan Reed
- Radiology & Imaging Sciences/Clinical Center, National Institutes of Health, Bethesda, MD, USA
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stefan Gräf
- Department of Medicine and NIHR BioResource for Translational Research, NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Stephanie Harmon
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Clinical Research Directorate, Frederick National Laboratory for Cancer, National Cancer Institute, Frederick, MD, USA
| | | | - Thanyawee Puthanakit
- Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Emerging Infectious Diseases Clinical Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Tony Mazzulli
- Department of Microbiology, Sinai Health/University Health Network, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Public Health Ontario Laboratories, Toronto, Ontario, Canada
| | | | - Yothin Rakvongthai
- Chulalongkorn University Biomedical Imaging Group and Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yu Rim Lee
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | | | - Fiona J Gilbert
- Department of Radiology, NIHR Cambridge Biomedical Resource Centre, University of Cambridge, Cambridge, UK
| | | | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
36
|
Maitra A, Kamdar MR, Zulman DM, Haverfield MC, Brown-Johnson C, Schwartz R, Israni ST, Verghese A, Musen MA. Using ethnographic methods to classify the human experience in medicine: a case study of the presence ontology. J Am Med Inform Assoc 2021; 28:1900-1909. [PMID: 34151988 PMCID: PMC8363802 DOI: 10.1093/jamia/ocab091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 04/26/2021] [Accepted: 05/13/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Although social and environmental factors are central to provider-patient interactions, the data that reflect these factors can be incomplete, vague, and subjective. We sought to create a conceptual framework to describe and classify data about presence, the domain of interpersonal connection in medicine. METHODS Our top-down approach for ontology development based on the concept of "relationality" included the following: 1) a broad survey of the social sciences literature and a systematic literature review of >20 000 articles around interpersonal connection in medicine, 2) relational ethnography of clinical encounters (n = 5 pilot, 27 full), and 3) interviews about relational work with 40 medical and nonmedical professionals. We formalized the model using the Web Ontology Language in the Protégé ontology editor. We iteratively evaluated and refined the Presence Ontology through manual expert review and automated annotation of literature. RESULTS AND DISCUSSION The Presence Ontology facilitates the naming and classification of concepts that would otherwise be vague. Our model categorizes contributors to healthcare encounters and factors such as communication, emotions, tools, and environment. Ontology evaluation indicated that cognitive models (both patients' explanatory models and providers' caregiving approaches) influenced encounters and were subsequently incorporated. We show how ethnographic methods based in relationality can aid the representation of experiential concepts (eg, empathy, trust). Our ontology could support investigative methods to improve healthcare processes for both patients and healthcare providers, including annotation of videotaped encounters, development of clinical instruments to measure presence, or implementation of electronic health record-based reminders for providers. CONCLUSION The Presence Ontology provides a model for using ethnographic approaches to classify interpersonal data.
Collapse
Affiliation(s)
- Amrapali Maitra
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Presence Center, Stanford University School of Medicine, Stanford, California, USA
| | - Maulik R Kamdar
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA
| | - Donna M Zulman
- Division of Primary Care and Population Health, Stanford University, Stanford, California, USA
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, California, USA
| | - Marie C Haverfield
- Department of Communication Studies, San Jose State University, San Jose, California, USA
| | - Cati Brown-Johnson
- Division of Primary Care and Population Health, Stanford University, Stanford, California, USA
| | - Rachel Schwartz
- WellMD Center, Stanford University School of Medicine, Stanford, California, USA
| | | | - Abraham Verghese
- Presence Center, Stanford University School of Medicine, Stanford, California, USA
| | - Mark A Musen
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA
| |
Collapse
|
37
|
Jaladanki SK, Vaid A, Sawant AS, Xu J, Shah K, Dellepiane S, Paranjpe I, Chan L, Kovatch P, Charney AW, Wang F, Glicksberg BS, Singh K, Nadkarni GN. Development of a federated learning approach to predict acute kidney injury in adult hospitalized patients with COVID-19 in New York City. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.07.25.21261105. [PMID: 34341802 PMCID: PMC8328073 DOI: 10.1101/2021.07.25.21261105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Federated learning is a technique for training predictive models without sharing patient-level data, thus maintaining data security while allowing inter-institutional collaboration. We used federated learning to predict acute kidney injury within three and seven days of admission, using demographics, comorbidities, vital signs, and laboratory values, in 4029 adults hospitalized with COVID-19 at five sociodemographically diverse New York City hospitals, between March-October 2020. Prediction performance of federated models was generally higher than single-hospital models and was comparable to pooled-data models. In the first use-case in kidney disease, federated learning improved prediction of a common complication of COVID-19, while preserving data privacy.
Collapse
Affiliation(s)
- Suraj K Jaladanki
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
| | - Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
| | - Ashwin S Sawant
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- The Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jie Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Kush Shah
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
| | - Sergio Dellepiane
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- The Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
| | - Lili Chan
- The Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Patricia Kovatch
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alexander W Charney
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Karandeep Singh
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- The Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| |
Collapse
|
38
|
Tong HL, Quiroz JC, Kocaballi AB, Fat SCM, Dao KP, Gehringer H, Chow CK, Laranjo L. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Prev Med 2021; 148:106532. [PMID: 33774008 DOI: 10.1016/j.ypmed.2021.106532] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/07/2021] [Accepted: 03/21/2021] [Indexed: 11/25/2022]
Abstract
Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.
Collapse
Affiliation(s)
- Huong Ly Tong
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
| | - Juan C Quiroz
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia
| | - A Baki Kocaballi
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; School of Computer Science, University of Technology Sydney, Sydney, Australia
| | | | | | - Holly Gehringer
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Clara K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Liliana Laranjo
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| |
Collapse
|
39
|
Lee CS, Brandt JD, Lee AY. Big Data and Artificial Intelligence in Ophthalmology: Where Are We Now? OPHTHALMOLOGY SCIENCE 2021; 1:100036. [PMID: 36249294 PMCID: PMC9560652 DOI: 10.1016/j.xops.2021.100036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Cecilia S. Lee
- Correspondence: Cecilia S. Lee, MD, MS, University of Washington, Box 359607, 325 Ninth Avenue, Seattle, WA 98104.
| | | | | |
Collapse
|
40
|
Elkhader J, Elemento O. Artificial intelligence in oncology: From bench to clinic. Semin Cancer Biol 2021; 84:113-128. [PMID: 33915289 DOI: 10.1016/j.semcancer.2021.04.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/22/2021] [Accepted: 04/15/2021] [Indexed: 02/07/2023]
Abstract
In the past few years, Artificial Intelligence (AI) techniques have been applied to almost every facet of oncology, from basic research to drug development and clinical care. In the clinical arena where AI has perhaps received the most attention, AI is showing promise in enhancing and automating image-based diagnostic approaches in fields such as radiology and pathology. Robust AI applications, which retain high performance and reproducibility over multiple datasets, extend from predicting indications for drug development to improving clinical decision support using electronic health record data. In this article, we review some of these advances. We also introduce common concepts and fundamentals of AI and its various uses, along with its caveats, to provide an overview of the opportunities and challenges in the field of oncology. Leveraging AI techniques productively to provide better care throughout a patient's medical journey can fuel the predictive promise of precision medicine.
Collapse
Affiliation(s)
- Jamal Elkhader
- HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA; Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, 10065, USA; Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA
| | - Olivier Elemento
- HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA; Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, 10065, USA; Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA.
| |
Collapse
|
41
|
Martinez-Martin N, Luo Z, Kaushal A, Adeli E, Haque A, Kelly SS, Wieten S, Cho MK, Magnus D, Fei-Fei L, Schulman K, Milstein A. Ethical issues in using ambient intelligence in health-care settings. Lancet Digit Health 2021; 3:e115-e123. [PMID: 33358138 PMCID: PMC8310737 DOI: 10.1016/s2589-7500(20)30275-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 10/26/2020] [Accepted: 11/11/2020] [Indexed: 12/16/2022]
Abstract
Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole.
Collapse
Affiliation(s)
| | - Zelun Luo
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Amit Kaushal
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ehsan Adeli
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Albert Haque
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Sara S Kelly
- Clinical Excellence Research Center, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Sarah Wieten
- Center for Biomedical Ethics, Stanford University, Stanford, CA, USA
| | - Mildred K Cho
- Center for Biomedical Ethics, Stanford University, Stanford, CA, USA
| | - David Magnus
- Center for Biomedical Ethics, Stanford University, Stanford, CA, USA
| | - Li Fei-Fei
- Stanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA
| | - Kevin Schulman
- Clinical Excellence Research Center, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Arnold Milstein
- Clinical Excellence Research Center, Department of Medicine, Stanford University, Stanford, CA, USA
| |
Collapse
|
42
|
Flores M, Dayan I, Roth H, Zhong A, Harouni A, Gentili A, Abidin A, Liu A, Costa A, Wood B, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan C, Xu D, Wu D, Huang E, Kitamura F, Lacey G, César de Antônio Corradi G, Shin HH, Obinata H, Ren H, Crane J, Tetreault J, Guan J, Garrett J, Park JG, Dreyer K, Juluru K, Kersten K, Bezerra Cavalcanti Rockenbach MA, Linguraru M, Haider M, AbdelMaseeh M, Rieke N, Damasceno P, Cruz E Silva PM, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist T, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon F, Gilbert F, Kaggie J, Li Q, Quraini A, Feng A, Priest A, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess C, Compas C, Bhatia D, Oermann E, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Keshava Murthy KN, Fu LC, Furtado de Mendonça MR, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod S, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lima Lavor V, Rakvongthai Y, Lee YR, Wen Y. Federated Learning used for predicting outcomes in SARS-COV-2 patients. RESEARCH SQUARE 2021:rs.3.rs-126892. [PMID: 33442676 PMCID: PMC7805458 DOI: 10.21203/rs.3.rs-126892/v1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.
Collapse
Affiliation(s)
| | | | | | - Aoxiao Zhong
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | | | | | | | | | | | - Bradford Wood
- Radiology & Imaging Sciences / Clinical Center, National Institutes of Health
| | - Chien-Sung Tsai
- Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chih-Hung Wang
- Tri-Service General Hospital, National Defense Medical Center
| | - Chun-Nan Hsu
- Center for Research in Biological Systems, University of California, San Diego
| | | | | | | | - Dufan Wu
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | | | | | | | | | | | | | - Hui Ren
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Jason Crane
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | | | | | - John Garrett
- The University of Wisconsin-Madison School of Medicine and Public Health
| | | | - Keith Dreyer
- Center for Clinical Data Science, Massachusetts General Brigham, Boston, MA
| | | | | | | | - Marius Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital and School of Medicine and Health Sciences, George Washington University, Washington, DC
| | - Masoom Haider
- Joint Dept. of Medical Imaging, Sinai Health System, University of Toronto, Toronto, Canada and Lunenfeld-Tanenbaum Research Institute, Toronto, Canada
| | | | | | - Pablo Damasceno
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | | | - Pochuan Wang
- MeDA Lab and Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
| | - Sheng Xu
- Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, USA
| | | | | | - Soo Young Park
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | | | - Varun Buch
- Center for Clinical Data Science, Massachusetts General Brigham, Boston, MA
| | - Watsamon Jantarabenjakul
- Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand and Thai Red Cross Emerging Infectious Diseases Clinical Center, King Chulalongkorn Memorial Hospital, Bang
| | | | - Won Young Tak
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Xiang Li
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Xihong Lin
- Harvard T.H. Chan School of Public Health
| | | | | | - Josh Kaggie
- Department of Radiology, NIHR Cambridge Biomedical Resource Centre, University of Cambridge
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | | | | | - Andrew Priest
- Department of Radiology, NIHR Cambridge Biomedical Resource Centre, Cambridge University Hospital
| | | | | | - Bernardo Bizzo
- Center for Clinical Data Science, Massachusetts General Brigham, Boston, MA
| | - Byung Seok Kim
- Department of Internal Medicine, Catholic University of Daegu School of Medicine, Daegu, South Korea
| | - Carlos Tor-Diez
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC
| | - Chia-Cheng Lee
- Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C. and Division of Colorectal Surgery, Department of Surgery, Tri-Service General H
| | - Chia-Jung Hsu
- Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin Lin
- School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C. and School of Public Health, National Defense Medical Center, Taipei, Taiwan, R.O.C. and Graduate Institute of Life Scienc
| | - Chiu-Ling Lai
- Medical Review and Pharmaceutical Benefits Division, National Health Insurance Administration, Taipei. Taiwan
| | | | | | | | | | - Evan Leibovitz
- The Center for Clinical Data Science, Mass General Brigham
| | | | | | | | - Jae Ho Sohn
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | | | - Li-Chen Fu
- MOST/NTU All Vista Healthcare Center, Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan
| | | | - Mike Fralick
- Division of General Internal Medicine and Geriatrics (Fralick), Sinai Health System, Toronto, Canada
| | - Min Kyu Kang
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | | | | | - Peerapon Vateekul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University
| | | | - Sarah Hickman
- Department of Radiology, NIHR Cambridge Biomedical Resource Centre, University of Cambridge
| | - Sharmila Majumdar
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Shelley McLeod
- Schwartz/Reisman Emergency Medicine Institute, Sinai Health, Toronto, ON, Canada and Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | - Sheridan Reed
- Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | - Thanyawee Puthanakit
- Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Center of Excellence in Pediatric Infectious Diseases and Vaccine, Chulalongkorn University
| | - Tony Mazzulli
- Department of Microbiology, Sinai Health/University Health Network, Toronto, Canada and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto. Canada Public Health Ontar
| | | | - Yothin Rakvongthai
- Chulalongkorn University Biomedical Imaging Group and Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yu Rim Lee
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | | |
Collapse
|
43
|
Morgenstern JD, Rosella LC, Daley MJ, Goel V, Schünemann HJ, Piggott T. "AI's gonna have an impact on everything in society, so it has to have an impact on public health": a fundamental qualitative descriptive study of the implications of artificial intelligence for public health. BMC Public Health 2021; 21:40. [PMID: 33407254 PMCID: PMC7787411 DOI: 10.1186/s12889-020-10030-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/09/2020] [Indexed: 12/20/2022] Open
Abstract
Background Our objective was to determine the impacts of artificial intelligence (AI) on public health practice. Methods We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically. Results We developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI’s applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation. Conclusions Experts are cautiously optimistic about AI’s impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-020-10030-x.
Collapse
Affiliation(s)
- Jason D Morgenstern
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.,Public Health Ontario, Toronto, Ontario, Canada.,Vector Institute, Toronto, Ontario, Canada
| | - Mark J Daley
- Vector Institute, Toronto, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada.,Department of Biology, Western University, London, Ontario, Canada.,Department of Actuarial Sciences and Statistics, Western University, London, Ontario, Canada.,Brain and Mind Institute, Western University, London, Ontario, Canada
| | - Vivek Goel
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Holger J Schünemann
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.,Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Thomas Piggott
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
| |
Collapse
|
44
|
Hameed BMZ, S. Dhavileswarapu AVL, Naik N, Karimi H, Hegde P, Rai BP, Somani BK. Big Data Analytics in urology: the story so far and the road ahead. Ther Adv Urol 2021; 13:1756287221998134. [PMID: 33747134 PMCID: PMC7940776 DOI: 10.1177/1756287221998134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 02/04/2021] [Indexed: 12/25/2022] Open
Abstract
Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.
Collapse
Affiliation(s)
- B. M. Zeeshan Hameed
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, India KMC Innovation Centre, Manipal Academy of Higher Education, Manipal, India iTRUE (International Training and Research in Uro-Oncology and Endourology) Group
| | | | - Nithesh Naik
- Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group
| | - Hadis Karimi
- Department of Pharmacy, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Padmaraj Hegde
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Bhavan Prasad Rai
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group Department of Urology, Freeman Hospital, Newcastle, UK
| | - Bhaskar K. Somani
- Department of Urology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, India
- iTRUE (International Training and Research in Uro-oncology and Endourology) Group Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK
| |
Collapse
|
45
|
Artificial intelligence in oncology. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
46
|
Bozkurt S, Cahan EM, Seneviratne MG, Sun R, Lossio-Ventura JA, Ioannidis JPA, Hernandez-Boussard T. Reporting of demographic data and representativeness in machine learning models using electronic health records. J Am Med Inform Assoc 2020; 27:1878-1884. [PMID: 32935131 PMCID: PMC7727384 DOI: 10.1093/jamia/ocaa164] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 06/22/2020] [Accepted: 06/27/2020] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE The development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability in specific populations. We sought to evaluate whether studies developing ML models from electronic health record (EHR) data report sufficient demographic data on the study populations to demonstrate representativeness and reproducibility. MATERIALS AND METHODS We searched PubMed for articles applying ML models to improve clinical decision-making using EHR data. We limited our search to papers published between 2015 and 2019. RESULTS Across the 164 studies reviewed, demographic variables were inconsistently reported and/or included as model inputs. Race/ethnicity was not reported in 64%; gender and age were not reported in 24% and 21% of studies, respectively. Socioeconomic status of the population was not reported in 92% of studies. Studies that mentioned these variables often did not report if they were included as model inputs. Few models (12%) were validated using external populations. Few studies (17%) open-sourced their code. Populations in the ML studies include higher proportions of White and Black yet fewer Hispanic subjects compared to the general US population. DISCUSSION The demographic characteristics of study populations are poorly reported in the ML literature based on EHR data. Demographic representativeness in training data and model transparency is necessary to ensure that ML models are deployed in an equitable and reproducible manner. Wider adoption of reporting guidelines is warranted to improve representativeness and reproducibility.
Collapse
Affiliation(s)
- Selen Bozkurt
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Eli M Cahan
- Department of Medicine, Stanford University, Stanford, California, USA
- NYU School of Medicine, New York, New York, USA
| | | | - Ran Sun
- Department of Medicine, Stanford University, Stanford, California, USA
| | | | - John P A Ioannidis
- Department of Medicine, Stanford University, Stanford, California, USA
- Department of Epidemiology and Population Health, School of Medicine, 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, Stanford University, Stanford, California, USA
| | - Tina Hernandez-Boussard
- Department of Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
- Department of Surgery, Stanford University, Stanford, California, USA
| |
Collapse
|
47
|
Aggarwal N, Ahmed M, Basu S, Curtin JJ, Evans BJ, Matheny ME, Nundy S, Sendak MP, Shachar C, Shah RU, Thadaney-Israni S. Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic. NAM Perspect 2020; 2020:202011f. [PMID: 35291747 PMCID: PMC8916812 DOI: 10.31478/202011f] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Affiliation(s)
| | | | | | | | | | - Michael E Matheny
- Vanderbilt University Medical Center and Tennessee Valley Healthcare System VA
| | | | | | | | | | | |
Collapse
|
48
|
Tan L, Tivey D, Kopunic H, Babidge W, Langley S, Maddern G. Part 1: Artificial intelligence technology in surgery. ANZ J Surg 2020; 90:2409-2414. [PMID: 33000556 DOI: 10.1111/ans.16343] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 08/25/2020] [Accepted: 09/14/2020] [Indexed: 12/21/2022]
Abstract
Artificial intelligence (AI) is one of the disruptive technologies of the fourth Industrial Revolution that is changing our work practices. This technology is in use in highly diverse industries including health care, defence, insurance and e-commerce. This review focuses on the relevance of AI to surgery. AI will aid surgeons with diagnostic decision-making, patient selection for surgery as well as improve patient pre- and post-operative care and management. Ethical considerations of AI with respect to patient rights and data privacy are highlighted. A further challenge is how best to present to national regulators a pragmatic way to assess AI as 'software as a medical device'. This relates to the ramifications for the adoption of AI technology in clinical practice, and its subsequent public funding support and reimbursement. It is evident that AI technology has important applications in surgery in the 21st century. The establishment of a key work programme in this area will be important if surgeons are to fully utilize AI in surgery.
Collapse
Affiliation(s)
- Lorwai Tan
- Research, Audit and Academic Surgery, Royal Australasian College of Surgeons, Adelaide, South Australia, Australia
| | - David Tivey
- Research, Audit and Academic Surgery, Royal Australasian College of Surgeons, Adelaide, South Australia, Australia.,Discipline of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia
| | - Helena Kopunic
- Research, Audit and Academic Surgery, Royal Australasian College of Surgeons, Adelaide, South Australia, Australia
| | - Wendy Babidge
- Research, Audit and Academic Surgery, Royal Australasian College of Surgeons, Adelaide, South Australia, Australia.,Discipline of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia
| | - Sally Langley
- Plastic and Reconstructive Surgery Department, Christchurch Hospital, Christchurch, New Zealand
| | - Guy Maddern
- Research, Audit and Academic Surgery, Royal Australasian College of Surgeons, Adelaide, South Australia, Australia.,Discipline of Surgery, The Queen Elizabeth Hospital, The University of Adelaide, Adelaide, South Australia, Australia
| |
Collapse
|
49
|
Khan IH, Javaid M. Big Data Applications in Medical Field: A Literature Review. JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT 2020. [DOI: 10.1142/s242486222030001x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.
Collapse
Affiliation(s)
- Ibrahim Haleem Khan
- School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
| | - Mohd Javaid
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
| |
Collapse
|
50
|
Haque A, Milstein A, Fei-Fei L. Illuminating the dark spaces of healthcare with ambient intelligence. Nature 2020; 585:193-202. [PMID: 32908264 DOI: 10.1038/s41586-020-2669-y] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 07/14/2020] [Indexed: 11/09/2022]
Abstract
Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.
Collapse
Affiliation(s)
- Albert Haque
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Arnold Milstein
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Li Fei-Fei
- Department of Computer Science, Stanford University, Stanford, CA, USA. .,Stanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA.
| |
Collapse
|