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Chowdhury A, Thway K, Pasquali S, Callegaro D, Gronchi A, Jones RL, Huang PH. Opportunities and Challenges in Soft Tissue Sarcoma Risk Stratification in the Era of Personalised Medicine. Curr Treat Options Oncol 2024; 25:1124-1135. [PMID: 39080193 DOI: 10.1007/s11864-024-01244-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] [Accepted: 06/28/2024] [Indexed: 08/17/2024]
Abstract
OPINION STATEMENT Soft tissue sarcomas (STS) are a rare and heterogeneous group of cancers. Treatment options have changed little in the past thirty years, and the role of neoadjuvant chemotherapy is controversial. Accurate risk stratification is crucial in STS in order to facilitate clinical discussions around peri-operative treatment. Current risk stratification tools used in clinic, such as Sarculator, use clinicopathological characteristics and may be specific to anatomical site or to histology. More recently, risk stratification tools have been developed using molecular or immunological data. Combining Sarculator with other risk stratification tools may identify novel patient groups with differential clinical outcomes. There are several considerations when translating risk stratification tools into widespread clinical use, including establishing clinical utility, health economic value, being applicable to existing clinical pathways, having strong real-world performance, and being supported by investment into infrastructure. Future work may include incorporation of novel modalities and data integration techniques.
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Affiliation(s)
- Avirup Chowdhury
- Division of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road Sutton, London, SM2 5NG, UK
| | - Khin Thway
- Division of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road Sutton, London, SM2 5NG, UK
- The Royal Marsden NHS Foundation Trust, London, UK
| | - Sandro Pasquali
- Molecular Pharmacology, Department of Experimental Oncology, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
- Department of Surgery, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - Dario Callegaro
- Department of Surgery, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - Alessandro Gronchi
- Department of Surgery, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - Robin L Jones
- The Royal Marsden NHS Foundation Trust, London, UK
- Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | - Paul H Huang
- Division of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road Sutton, London, SM2 5NG, UK.
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Loftus TJ, Balch JA, Abbott KL, Hu D, Ruppert MM, Shickel B, Ozrazgat-Baslanti T, Efron PA, Tighe PJ, Hogan WR, Rashidi P, Cardel MI, Upchurch GR, Bihorac A. Community-engaged artificial intelligence research: A scoping review. PLOS DIGITAL HEALTH 2024; 3:e0000561. [PMID: 39178307 DOI: 10.1371/journal.pdig.0000561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 06/27/2024] [Indexed: 08/25/2024]
Abstract
The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.
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Affiliation(s)
- Tyler J Loftus
- University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America
- Department of Surgery, University of Florida Health, Gainesville, Florida, United States of America
| | - Jeremy A Balch
- University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America
- Department of Surgery, University of Florida Health, Gainesville, Florida, United States of America
| | - Kenneth L Abbott
- Department of Surgery, University of Florida Health, Gainesville, Florida, United States of America
| | - Die Hu
- University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America
- Department of Surgery, University of Florida Health, Gainesville, Florida, United States of America
| | - Matthew M Ruppert
- University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America
- Department of Medicine, University of Florida Health, Gainesville, Florida, United States of America
- College of Medicine, University of Central Florida, Orlando, Florida, United States of America
| | - Benjamin Shickel
- University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America
- Department of Medicine, University of Florida Health, Gainesville, Florida, United States of America
| | - Tezcan Ozrazgat-Baslanti
- University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America
- Department of Medicine, University of Florida Health, Gainesville, Florida, United States of America
| | - Philip A Efron
- Department of Surgery, University of Florida Health, Gainesville, Florida, United States of America
| | - Patrick J Tighe
- Departments of Anesthesiology, Orthopedics, and Information Systems/Operations Management, University of Florida Health, Gainesville, Florida, United States of America
| | - William R Hogan
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Parisa Rashidi
- University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America
- Departments of Biomedical Engineering, Computer and Information Science and Engineering, and Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Michelle I Cardel
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, Florida, United States of America
| | - Azra Bihorac
- University of Florida Intelligent Clinical Care Center, Gainesville, Florida, United States of America
- Department of Surgery, University of Florida Health, Gainesville, Florida, United States of America
- Department of Medicine, University of Florida Health, Gainesville, Florida, United States of America
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Hotta S, Kytö M, Koivusalo S, Heinonen S, Marttinen P. Optimizing postprandial glucose prediction through integration of diet and exercise: Leveraging transfer learning with imbalanced patient data. PLoS One 2024; 19:e0298506. [PMID: 39088422 PMCID: PMC11293722 DOI: 10.1371/journal.pone.0298506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 07/11/2024] [Indexed: 08/03/2024] Open
Abstract
BACKGROUND In recent years, numerous methods have been introduced to predict glucose levels using machine-learning techniques on patients' daily behavioral and continuous glucose data. Nevertheless, a definitive consensus remains elusive regarding modeling the combined effects of diet and exercise for optimal glucose prediction. A notable challenge is the propensity for observational patient datasets from uncontrolled environments to overfit due to skewed feature distributions of target behaviors; for instance, diabetic patients seldom engage in high-intensity exercise post-meal. METHODS In this study, we introduce a unique application of Bayesian transfer learning for postprandial glucose prediction using randomized controlled trial (RCT) data. The data comprises a time series of three key variables: continuous glucose levels, exercise expenditure, and carbohydrate intake. For building the optimal model to predict postprandial glucose levels we initially gathered balanced training data from RCTs on healthy participants by randomizing behavioral conditions. Subsequently, we pretrained the model's parameter distribution using RCT data from the healthy cohort. This pretrained distribution was then adjusted, transferred, and utilized to determine the model parameters for each patient. RESULTS The efficacy of the proposed method was appraised using data from 68 gestational diabetes mellitus (GDM) patients in uncontrolled settings. The evaluation underscored the enhanced performance attained through our method. Furthermore, when modeling the joint impact of diet and exercise, the synergetic model proved more precise than its additive counterpart. CONCLUSION An innovative application of the transfer-learning utilizing randomized controlled trial data can improve the challenging modeling task of postprandial glucose prediction for GDM patients, integrating both dietary and exercise behaviors. For more accurate prediction, future research should focus on incorporating the long-term effects of exercise and other glycemic-related factors such as stress, sleep.
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Affiliation(s)
- Shinji Hotta
- Department of Computer Science, Aalto University, Espoo, Finland
- Fujitsu Limited, Kawasaki, Japan
| | - Mikko Kytö
- IT Management, Helsinki University Hospital, Helsinki, Finland
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Saila Koivusalo
- Shared Group Services, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Seppo Heinonen
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Pekka Marttinen
- Department of Computer Science, Aalto University, Espoo, Finland
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Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 PMCID: PMC11149372 DOI: 10.1186/s13073-024-01345-0] [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: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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Ruan X, Fu S, Jia H, Mathis KL, Thiels CA, Wilson PM, Storlie CB, Liu H. Revolutionizing Postoperative Ileus Monitoring: Exploring GRU-D's Real-Time Capabilities and Cross-Hospital Transferability. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.24.24306295. [PMID: 38712199 PMCID: PMC11071561 DOI: 10.1101/2024.04.24.24306295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Postoperative ileus (POI) after colorectal surgery leads to increased morbidity, costs, and hospital stays. Identifying POI risk for early intervention is important for improving surgical outcomes especially given the increasing trend towards early discharge after surgery. While existing studies have assessed POI risk with regression models, the role of deep learning's remains unexplored. Methods We assessed the performance and transferability (brutal force/instance/parameter transfer) of Gated Recurrent Unit with Decay (GRU-D), a longitudinal deep learning architecture, for real-time risk assessment of POI among 7,349 colorectal surgeries performed across three hospital sites operated by Mayo Clinic with two electronic health records (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics. Results GRU-D exhibits robust transferability across different EHR systems and hospital sites, showing enhanced performance by integrating new measurements, even amid the extreme sparsity of real-world longitudinal data. On average, for labs, vitals, and assisted living status, 72.2%, 26.9%, and 49.3% respectively lack measurements within 24 hours after surgery. Over the follow-up period with 4-hour intervals, 98.7%, 84%, and 95.8% of data points are missing, respectively. A maximum of 5% decrease in AUROC was observed in brutal-force transfer between different EHR systems with non-overlapping surgery date frames. Multi-source instance transfer witnessed the best performance, with a maximum of 2.6% improvement in AUROC over local learning. The significant benefit, however, lies in the reduction of variance (a maximum of 86% decrease). The GRU-D model's performance mainly depends on the prediction task's difficulty, especially the case prevalence rate. Whereas the impact of training data and transfer strategy is less crucial, underscoring the challenge of effectively leveraging transfer learning for rare outcomes. While atemporal Logit models show notably superior performance at certain pre-surgical points, their performance fluctuate significantly and generally underperform GRU-D in post-surgical hours. Conclusion GRU-D demonstrated robust transferability across EHR systems and hospital sites with highly sparse real-world EHR data. Further research on built-in explainability for meaningful intervention would be highly valuable for its integration into clinical practice.
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Affiliation(s)
- Xiaoyang Ruan
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston
- Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States
| | - Sunyang Fu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston
- Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States
| | - Heling Jia
- Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States
| | - Kellie L Mathis
- Department of Surgery, Mayo Clinic, Rochester, MN, United States
| | | | - Patrick M Wilson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Curtis B Storlie
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Hongfang Liu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston
- Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States
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López-Cortés XA, Manríquez-Troncoso JM, Hernández-García R, Peralta D. MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning. Front Microbiol 2024; 15:1361795. [PMID: 38694798 PMCID: PMC11062410 DOI: 10.3389/fmicb.2024.1361795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction Antimicrobial resistance (AMR) is a global health problem that requires early and effective treatments to prevent the indiscriminate use of antimicrobial drugs and the outcome of infections. Mass Spectrometry (MS), and more particularly MALDI-TOF, have been widely adopted by routine clinical microbiology laboratories to identify bacterial species and detect AMR. The analysis of AMR with deep learning is still recent, and most models depend on filters and preprocessing techniques manually applied on spectra. Methods This study propose a deep neural network, MSDeepAMR, to learn from raw mass spectra to predict AMR. MSDeepAMR model was implemented for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus under different antibiotic resistance profiles. Additionally, a transfer learning test was performed to study the benefits of adapting the previously trained models to external data. Results MSDeepAMR models showed a good classification performance to detect antibiotic resistance. The AUROC of the model was above 0.83 in most cases studied, improving the results of previous investigations by over 10%. The adapted models improved the AUROC by up to 20% when compared to a model trained only with external data. Discussion This study demonstrate the potential of the MSDeepAMR model to predict antibiotic resistance and their use on external MS data. This allow the extrapolation of the MSDeepAMR model to de used in different laboratories that need to study AMR and do not have the capacity for an extensive sample collection.
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Affiliation(s)
- Xaviera A. López-Cortés
- Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca, Chile
- Centro de Innovación en Ingeniería Aplicada (CIIA), Universidad Católica del Maule, Talca, Chile
| | | | - Ruber Hernández-García
- Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca, Chile
- Laboratory of Technological Research in Pattern Recognition (LITRP), Universidad Católica del Maule, Talca, Chile
| | - Daniel Peralta
- IDLab, Department of Information Technology, Ghent University-imec, Ghent, Belgium
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Al-Hussaini I, White B, Varmeziar A, Mehra N, Sanchez M, Lee J, DeGroote NP, Miller TP, Mitchell CS. An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia. J Clin Med 2024; 13:1788. [PMID: 38542012 PMCID: PMC10970787 DOI: 10.3390/jcm13061788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 04/18/2024] Open
Abstract
Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either "high risk" or "low risk" in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.
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Affiliation(s)
- Irfan Al-Hussaini
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Brandon White
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Armon Varmeziar
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Nidhi Mehra
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Milagro Sanchez
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Judy Lee
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, Atlanta, GA 30322, USA (T.P.M.)
| | - Nicholas P. DeGroote
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, Atlanta, GA 30322, USA (T.P.M.)
| | - Tamara P. Miller
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, Atlanta, GA 30322, USA (T.P.M.)
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, Emory University, Atlanta, GA 30332, USA
| | - Cassie S. Mitchell
- Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Machine Learning Center at Georgia Tech, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Ogasawara T, Mukaino M, Matsunaga K, Wada Y, Suzuki T, Aoshima Y, Furuzawa S, Kono Y, Saitoh E, Yamaguchi M, Otaka Y, Tsukada S. Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data. Front Bioeng Biotechnol 2024; 11:1285945. [PMID: 38234303 PMCID: PMC10791943 DOI: 10.3389/fbioe.2023.1285945] [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: 08/30/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices. Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies. Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman's rank correlation coefficient (R) of 0.56 and -0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics. Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.
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Affiliation(s)
- Takayuki Ogasawara
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Masahiko Mukaino
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
- Department of Rehabilitation Medicine, Hokkaido University Hospital, Sapporo, Japan
| | | | - Yoshitaka Wada
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Takuya Suzuki
- Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan
| | - Yasushi Aoshima
- Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan
| | - Shotaro Furuzawa
- Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan
| | - Yuji Kono
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
- Department of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan
| | - Eiichi Saitoh
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Masumi Yamaguchi
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
| | - Yohei Otaka
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Shingo Tsukada
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan
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Lee T, Wollstein G, Madu CT, Wronka A, Zheng L, Zambrano R, Schuman JS, Hu J. Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality. Transl Vis Sci Technol 2023; 12:2. [PMID: 38038606 PMCID: PMC10697175 DOI: 10.1167/tvst.12.12.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Purpose Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. Method We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. Results Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. Conclusions The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. Translational Relevance TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.
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Affiliation(s)
- TingFang Lee
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Departments of Population Health, NYU Langone Health, New York, NY, USA
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Center of Neural Science, NYU College of Arts and Sciences, New York, NY, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - Chisom T Madu
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Andrew Wronka
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Lei Zheng
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Ronald Zambrano
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Joel S Schuman
- Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA
| | - Jiyuan Hu
- Departments of Population Health, NYU Langone Health, New York, NY, USA
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Hsiao YC, Kuo CY, Lin FJ, Wu YW, Lin TH, Yeh HI, Chen JW, Wu CC. Machine Learning Models for ASCVD Risk Prediction in an Asian Population - How to Validate the Model is Important. ACTA CARDIOLOGICA SINICA 2023; 39:901-912. [PMID: 38022427 PMCID: PMC10646597 DOI: 10.6515/acs.202311_39(6).20230528a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 05/28/2023] [Indexed: 12/01/2023]
Abstract
Introduction Atherosclerotic cardiovascular disease (ASCVD) is prevalent worldwide including Taiwan, however widely accepted tools to assess the risk of ASCVD are lacking in Taiwan. Machine learning models are potentially useful for risk evaluation. In this study we used two cohorts to test the feasibility of machine learning with transfer learning for developing an ASCVD risk prediction model in Taiwan. Methods Two multi-center observational registry cohorts, T-SPARCLE and T-PPARCLE were used in this study. The variables selected were based on European, U.S. and Asian guidelines. Both registries recorded the ASCVD outcomes of the patients. Ten-fold validation and temporal validation methods were used to evaluate the performance of the binary classification analysis [prediction of major adverse cardiovascular (CV) events in one year]. Time-to-event analyses were also performed. Results In the binary classification analysis, eXtreme Gradient Boosting (XGBoost) and random forest had the best performance, with areas under the receiver operating characteristic curve (AUC-ROC) of 0.72 (0.68-0.76) and 0.73 (0.69-0.77), respectively, although it was not significantly better than other models. Temporal validation was also performed, and the data showed significant differences in the distribution of various features and event rate. The AUC-ROC of XGBoost dropped to 0.66 (0.59-0.73), while that of random forest dropped to 0.69 (0.62-0.76) in the temporal validation method, and the performance also became numerically worse than that of the logistic regression model. In the time-to-event analysis, most models had a concordance index of around 0.70. Conclusions Machine learning models with appropriate transfer learning may be a useful tool for the development of CV risk prediction models and may help improve patient care in the future.
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Affiliation(s)
- Yu-Chung Hsiao
- Department of Internal Medicine, National Taiwan University Hospital
| | - Chen-Yuan Kuo
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University
| | - Fang-Ju Lin
- Graduate Institute of Clinical Pharmacy & School of Pharmacy, College of Medicine, National Taiwan University
- Department of Pharmacy, National Taiwan University Hospital, Taipei
| | - Yen-Wen Wu
- Division of Cardiology, Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City
- School of Medicine, National Yang Ming Chiao Tung University, School of Medicine, Taipei
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan
| | - Tsung-Hsien Lin
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung
| | - Hung-I Yeh
- MacKay Memorial Hospital, MacKay Medical College
| | - Jaw-Wen Chen
- Department of Medical Research and Education, Taipei Veterans General Hospital
| | - Chau-Chung Wu
- Department of Internal Medicine, National Taiwan University Hospital
- Graduate Institute of Medical Education & Bioethics, College of Medicine, National Taiwan University, Taipei, Taiwan
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11
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Wibaek R, Andersen GS, Dahm CC, Witte DR, Hulman A. Large Language Models for Epidemiological Research via Automated Machine Learning: Case Study Using Data From the British National Child Development Study. JMIR Med Inform 2023; 11:e43638. [PMID: 37787655 PMCID: PMC10547934 DOI: 10.2196/43638] [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: 10/21/2022] [Revised: 06/29/2023] [Accepted: 07/22/2023] [Indexed: 10/04/2023] Open
Abstract
Background Large language models have had a huge impact on natural language processing (NLP) in recent years. However, their application in epidemiological research is still limited to the analysis of electronic health records and social media data. objectives To demonstrate the potential of NLP beyond these domains, we aimed to develop prediction models based on texts collected from an epidemiological cohort and compare their performance to classical regression methods. Methods We used data from the British National Child Development Study, where 10,567 children aged 11 years wrote essays about how they imagined themselves as 25-year-olds. Overall, 15% of the data set was set aside as a test set for performance evaluation. Pretrained language models were fine-tuned using AutoTrain (Hugging Face) to predict current reading comprehension score (range: 0-35) and future BMI and physical activity (active vs inactive) at the age of 33 years. We then compared their predictive performance (accuracy or discrimination) with linear and logistic regression models, including demographic and lifestyle factors of the parents and children from birth to the age of 11 years as predictors. Results NLP clearly outperformed linear regression when predicting reading comprehension scores (root mean square error: 3.89, 95% CI 3.74-4.05 for NLP vs 4.14, 95% CI 3.98-4.30 and 5.41, 95% CI 5.23-5.58 for regression models with and without general ability score as a predictor, respectively). Predictive performance for physical activity was similarly poor for the 2 methods (area under the receiver operating characteristic curve: 0.55, 95% CI 0.52-0.60 for both) but was slightly better than random assignment, whereas linear regression clearly outperformed the NLP approach when predicting BMI (root mean square error: 4.38, 95% CI 4.02-4.74 for NLP vs 3.85, 95% CI 3.54-4.16 for regression). The NLP approach did not perform better than simply assigning the mean BMI from the training set as a predictor. Conclusions Our study demonstrated the potential of using large language models on text collected from epidemiological studies. The performance of the approach appeared to depend on how directly the topic of the text was related to the outcome. Open-ended questions specifically designed to capture certain health concepts and lived experiences in combination with NLP methods should receive more attention in future epidemiological studies.
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Affiliation(s)
| | | | | | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Adam Hulman
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
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12
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Gao Y, Sharma T, Cui Y. Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective. Annu Rev Biomed Data Sci 2023; 6:153-171. [PMID: 37104653 PMCID: PMC10529864 DOI: 10.1146/annurev-biodatasci-020722-020704] [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] [Indexed: 04/29/2023]
Abstract
Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
| | - Teena Sharma
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
| | - Yan Cui
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
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13
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Wang M, Sushil M, Miao BY, Butte AJ. Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data. J Am Med Inform Assoc 2023; 30:1323-1332. [PMID: 37187158 PMCID: PMC10280344 DOI: 10.1093/jamia/ocad085] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/03/2023] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
OBJECTIVES As the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligence (AI) are becoming increasingly applied to enable efficient data-driven learning and, ultimately, to advance healthcare. Our objective is to provide readers with an understanding of evolving computational methods and help in deciding on methods to pursue. TARGET AUDIENCE The sheer diversity of existing methods presents a challenge for health scientists who are beginning to apply computational methods to their research. Therefore, this tutorial is aimed at scientists working with EHR data who are early entrants into the field of applying AI methodologies. SCOPE This manuscript describes the diverse and growing AI research approaches in healthcare data science and categorizes them into 2 distinct paradigms, the bottom-up and top-down paradigms to provide health scientists venturing into artificial intelligent research with an understanding of the evolving computational methods and help in deciding on methods to pursue through the lens of real-world healthcare data.
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Affiliation(s)
- Michelle Wang
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, USA
| | - Madhumita Sushil
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, USA
| | - Brenda Y Miao
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA
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14
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Qiu Y, Lin F, Chen W, Xu M. Pre-training in Medical Data: A Survey. MACHINE INTELLIGENCE RESEARCH 2023. [PMCID: PMC9942039 DOI: 10.1007/s11633-022-1382-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods’ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.
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Affiliation(s)
- Yixuan Qiu
- The University of Queensland, Brisbane, 4072 Australia
| | - Feng Lin
- The University of Queensland, Brisbane, 4072 Australia
| | - Weitong Chen
- The University of Adelaide, Adelaide, 5005 Australia
| | - Miao Xu
- The University of Queensland, Brisbane, 4072 Australia
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15
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Venkatesh KP, Raza MM, Kvedar JC. Automating the overburdened clinical coding system: challenges and next steps. NPJ Digit Med 2023; 6:16. [PMID: 36737496 PMCID: PMC9898522 DOI: 10.1038/s41746-023-00768-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023] Open
Affiliation(s)
| | - Marium M. Raza
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Joseph C. Kvedar
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
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16
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Lam L, Lam A, Bacchi S, Abou-Hamden A. Neurosurgery inpatient outcome prediction for discharge planning with deep learning and transfer learning. Br J Neurosurg 2022:1-5. [PMID: 36458628 DOI: 10.1080/02688697.2022.2151565] [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: 05/13/2022] [Revised: 11/15/2022] [Accepted: 11/19/2022] [Indexed: 12/05/2022]
Abstract
INTRODUCTION Deep learning may be able to assist with the prediction of neurosurgical inpatient outcomes. The aims of this study were to investigate deep learning and transfer learning in the prediction of several inpatient outcomes including timing of discharge and discharge destination. METHOD Data were collected on consecutive neurosurgical admissions from existing databases over a 15-month period. Following pre-processing artificial neural networks were applied to admission notes and ward round notes to predict four inpatient outcomes. Models were developed on the training dataset, before being tested on a hold-out test dataset and a validation dataset. RESULTS 1341 individual admissions were included in the study. Using transfer learning and an artificial neural network an area under the receiver operator curve (AUC) of 0.81 and 0.80 on the derivation and validation datasets was able to be achieved for the prediction of discharge within the next 48 hours using daily ward round notes. This result is in comparison to an AUC of 0.71 and 0.68 using an artificial neural network without transfer learning for the same outcome. When the artificial neural network with transfer learning was applied to the other outcomes AUC of 0.72, 0.93 and 0.83 was achieved on the validation datasets for predicting discharge within the next 7 days, survival to discharge and discharge to home as a destination. CONCLUSIONS Deep learning may predict inpatient neurosurgery outcomes from free-text medical data. Recurrent predictions with ward round notes enable the use of information obtained throughout hospital admissions in these estimates.
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Affiliation(s)
- Lydia Lam
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Antoinette Lam
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Stephen Bacchi
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Amal Abou-Hamden
- Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
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17
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Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics. Antibiotics (Basel) 2022; 11:antibiotics11111611. [DOI: 10.3390/antibiotics11111611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
Antimicrobial resistance (AMR) has become one of the serious global health problems, threatening the effective treatment of a growing number of infections. Machine learning and deep learning show great potential in rapid and accurate AMR predictions. However, a large number of samples for the training of these models is essential. In particular, for novel antibiotics, limited training samples and data imbalance hinder the models’ generalization performance and overall accuracy. We propose a deep transfer learning model that can improve model performance for AMR prediction on small, imbalanced datasets. As our approach relies on transfer learning and secondary mutations, it is also applicable to novel antibiotics and emerging resistances in the future and enables quick diagnostics and personalized treatments.
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