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Ghoshal A, Prince R, Downar J, Lapenskie J, Subramaniam S, Wegier P, Le L, Hannon B. Exploring the Utility of the Modified Hospitalized-Patient One-Year Mortality Risk Score to Trigger Referrals to Palliative Care for Inpatients With Cancer. Cancer Med 2024; 13:e70292. [PMID: 39382260 PMCID: PMC11462593 DOI: 10.1002/cam4.70292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 09/10/2024] [Accepted: 09/22/2024] [Indexed: 10/10/2024] Open
Abstract
BACKGROUND Estimating prognosis can be a barrier to timely palliative care involvement. The modified Hospitalized-patient One-year Mortality Risk (mHOMR) score uses hospital admission data to calculate the risk of death within 12 months and may be a useful tool to trigger a referral to palliative care. METHODS The mHOMR tool was retrospectively applied to consecutive acute admissions to a quaternary cancer center in Toronto, Canada from March 1 to May 31, 2018. The study aimed to investigate the association between dichotomized mHOMR scores (the cohort median score of 0.27 and the developer-recommended score of 0.21) and the risk of death, and whether these could be used to identify patients who may benefit from timely palliative care involvement. RESULTS Of 269 inpatients, 87 were elective admissions and excluded from further analyses. At the median mHOMR score of 0.27, 91/182 patients (50%) were categorized as high-risk of death within 12 months (mHOMR+), 53 (58%) were referred to palliative care. At the lower cut-off of 0.21, 103 patients were mHOMR+, of whom 57 (55.3%) were referred to palliative care. The higher mHOMR was significantly associated with mortality (29.7% mHOMR- vs. 39.8% mHOMR+ at 12 months, log-rank p < 0.05). The association between the developer-recommended mHOMR cut-off (≥ 0.21) and mortality was not significant (p = 0.15). CONCLUSIONS A higher mHOMR score was significantly associated with the risk of mortality in patients with advanced cancer. However, the developer-recommended mHOMR cut-off of 0.21 failed to identify a statistically significant difference between patients with advanced cancer at low versus high scores. While mHOMR may be a useful tool to augment clinical judgment and identify inpatients with advanced cancer at high risk of death, who in turn may benefit from referral to palliative care, the optimal mHOMR cutoff may warrant adjustment for this population.
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Affiliation(s)
- A. Ghoshal
- Princess Margaret Cancer CentreTorontoOntarioCanada
- Department of Medicine, Division of Palliative MedicineUniversity of TorontoTorontoOntarioCanada
| | - R. Prince
- Epworth Cancer Services Clinical InstituteGeelongVictoriaAustralia
| | - J. Downar
- Department of Medicine, Division of Palliative CareUniversity of OttawaOttawaOntarioCanada
- Ottawa Hospital Research Institute and Bruyère Research InstituteOttawaOntarioCanada
| | - J. Lapenskie
- Ottawa Hospital Research Institute and Bruyère Research InstituteOttawaOntarioCanada
| | | | - P. Wegier
- Humber River HealthNorth YorkOntarioCanada
| | - L. W. Le
- Department of BiostatisticsPrincess Margaret Cancer CentreTorontoOntarioCanada
| | - B. Hannon
- Princess Margaret Cancer CentreTorontoOntarioCanada
- Department of Medicine, Division of Palliative MedicineUniversity of TorontoTorontoOntarioCanada
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2
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Ho A, Brake J, Palmer A, Binkley CE. A Holistic, Multi-Level, and Integrative Ethical Approach to Developing Machine Learning-Driven Decision Aids. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2024; 24:110-113. [PMID: 39226003 DOI: 10.1080/15265161.2024.2377104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Affiliation(s)
- Anita Ho
- University of British Columbia
- University of California, San Francisco
- CommonSpirit Health
| | | | - Amitabha Palmer
- University of Texas
- MD Anderson Cancer Center
- Institute for Data Science in Oncology
| | - Charles E Binkley
- Hackensack Meridian Health
- Hackensack Meridian School of Medicine
- Markkula Center for Applied Ethics
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3
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Salvador Comino MR, Youssef P, Heinzelmann A, Bernhardt F, Seifert C, Tewes M. Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer. JCO Clin Cancer Inform 2024; 8:e2400041. [PMID: 39197123 DOI: 10.1200/cci.24.00041] [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: 02/22/2024] [Revised: 06/25/2024] [Accepted: 07/15/2024] [Indexed: 08/30/2024] Open
Abstract
PURPOSE Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most from palliative care support. We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality. MATERIALS AND METHODS Between April 1, 2020, and March 31, 2021, a total of 265 patients with advanced cancer completed a patient-reported outcome tool. We documented objective and subjective variables collected from electronic health records, self-reported subjective variables, and all clinical variables combined. We used logistic regression (LR), 20-fold cross-validation, decision trees, and random forests to predict 1-year mortality. We analyzed the receiver operating characteristic (ROC) curve-AUC, the precision-recall curve-AUC (PR-AUC)-and the feature importance of the ML models. RESULTS The performance of clinical nonpatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predictive than that of subjective patient-reported variables (LR reaches 0.55 [ROC-AUC] and 0.52 [F1 score]). CONCLUSION The results show that objective variables used in this study are much more predictive than subjective patient-reported variables, which measure subjective burden. These findings indicate that subjective burden cannot be reliably used to predict survival. Further research is needed to clarify the role of self-reported patient burden and mortality prediction using ML.
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Affiliation(s)
- Maria Rosa Salvador Comino
- Department of Palliative Medicine, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Paul Youssef
- Institute for Artificial Intelligence in Medicine (IKIM), University of Duisburg-Essen, Essen, Germany
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| | - Anna Heinzelmann
- Department of Palliative Medicine, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Florian Bernhardt
- Department of Palliative Care, West German Cancer Center, University Hospital Muenster, University of Muenster, Muenster, Germany
| | - Christin Seifert
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| | - Mitra Tewes
- Department of Palliative Medicine, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
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4
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Zhang S, Song J. A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model. Sci Rep 2024; 14:17118. [PMID: 39054346 PMCID: PMC11272932 DOI: 10.1038/s41598-024-67429-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
In recent years, artificial intelligence has made remarkable strides, improving various aspects of our daily lives. One notable application is in intelligent chatbots that use deep learning models. These systems have shown tremendous promise in the medical sector, enhancing healthcare quality, treatment efficiency, and cost-effectiveness. However, their role in aiding disease diagnosis, particularly chronic conditions, remains underexplored. Addressing this issue, this study employs large language models from the GPT series, in conjunction with deep learning techniques, to design and develop a diagnostic system targeted at chronic diseases. Specifically, performed transfer learning and fine-tuning on the GPT-2 model, enabling it to assist in accurately diagnosing 24 common chronic diseases. To provide a user-friendly interface and seamless interactive experience, we further developed a dialog-based interface, naming it Chat Ella. This system can make precise predictions for chronic diseases based on the symptoms described by users. Experimental results indicate that our model achieved an accuracy rate of 97.50% on the validation set, and an area under the curve (AUC) value reaching 99.91%. Moreover, conducted user satisfaction tests, which revealed that 68.7% of participants approved of Chat Ella, while 45.3% of participants found the system made daily medical consultations more convenient. It can rapidly and accurately assess a patient's condition based on the symptoms described and provide timely feedback, making it of significant value in the design of medical auxiliary products for household use.
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Affiliation(s)
- Sainan Zhang
- Graduate School of Communication Design, Hanyang University, ERICA Campus, Ansan, 15588, Republic of Korea.
| | - Jisung Song
- Graduate School of Communication Design, Hanyang University, ERICA Campus, Ansan, 15588, Republic of Korea
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Trabassi D, Castiglia SF, Bini F, Marinozzi F, Ajoudani A, Lorenzini M, Chini G, Varrecchia T, Ranavolo A, De Icco R, Casali C, Serrao M. Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia. SENSORS (BASEL, SWITZERLAND) 2024; 24:3613. [PMID: 38894404 PMCID: PMC11175240 DOI: 10.3390/s24113613] [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: 05/09/2024] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.
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Affiliation(s)
- Dante Trabassi
- Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; (D.T.); (C.C.); (M.S.)
| | - Stefano Filippo Castiglia
- Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; (D.T.); (C.C.); (M.S.)
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy;
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy; (F.B.); (F.M.)
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy; (F.B.); (F.M.)
| | - Arash Ajoudani
- Department of Advanced Robotics, Italian Institute of Technology, 16163 Genoa, Italy; (A.A.); (M.L.)
| | - Marta Lorenzini
- Department of Advanced Robotics, Italian Institute of Technology, 16163 Genoa, Italy; (A.A.); (M.L.)
| | - Giorgia Chini
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy; (G.C.); (T.V.); (A.R.)
| | - Tiwana Varrecchia
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy; (G.C.); (T.V.); (A.R.)
| | - Alberto Ranavolo
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy; (G.C.); (T.V.); (A.R.)
| | - Roberto De Icco
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy;
- Headache Science & Neurorehabilitation Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Carlo Casali
- Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; (D.T.); (C.C.); (M.S.)
| | - Mariano Serrao
- Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; (D.T.); (C.C.); (M.S.)
- Movement Analysis Laboratory, Policlinico Italia, 00162 Rome, Italy
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Rossnan O, Hanson A, Spaulding A, Satashia P, Bhakta S, Robinson M, Helgeson SA, Moreno-Franco P, Sanghavi D. Palliative care needs in medical intensive care: improved identification-retrospective cohort study. BMJ Support Palliat Care 2024:spcare-2022-004128. [PMID: 38777373 DOI: 10.1136/spcare-2022-004128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 05/25/2024]
Affiliation(s)
- Olivia Rossnan
- Department of Critical Care Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Abby Hanson
- Department of Critical Care Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Aaron Spaulding
- Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Parthkumar Satashia
- Department of Critical Care Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Shivang Bhakta
- Department of Critical Care Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Maisha Robinson
- Department of Palliative Care, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Scott A Helgeson
- Department of Internal Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Pablo Moreno-Franco
- Department of Critical Care Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Devang Sanghavi
- Department of Critical Care Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA
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Zhuang Q, Zhang AY, Cong RSTY, Yang GM, Neo PSH, Tan DS, Chua ML, Tan IB, Wong FY, Eng Hock Ong M, Shao Wei Lam S, Liu N. Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction. BMC Palliat Care 2024; 23:124. [PMID: 38769564 PMCID: PMC11103848 DOI: 10.1186/s12904-024-01457-9] [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: 07/26/2023] [Accepted: 05/15/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. METHODS Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: "Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior." The model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. RESULTS In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856-0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. CONCLUSION Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care.
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Affiliation(s)
- Qingyuan Zhuang
- Division of Supportive and Palliative Care, National Cancer Centre Singapore, 30 Hospital Blvd, Singapore, 168583, Singapore.
- Data Computational Science Core, National Cancer Centre Singapore, Singapore, Singapore.
| | - Alwin Yaoxian Zhang
- Division of Supportive and Palliative Care, National Cancer Centre Singapore, 30 Hospital Blvd, Singapore, 168583, Singapore
| | - Ryan Shea Tan Ying Cong
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Data Computational Science Core, National Cancer Centre Singapore, Singapore, Singapore
| | - Grace Meijuan Yang
- Division of Supportive and Palliative Care, National Cancer Centre Singapore, 30 Hospital Blvd, Singapore, 168583, Singapore
- Lien Centre of Palliative Care, Duke-NUS Medical School, Singapore, Singapore
| | - Patricia Soek Hui Neo
- Division of Supportive and Palliative Care, National Cancer Centre Singapore, 30 Hospital Blvd, Singapore, 168583, Singapore
| | - Daniel Sw Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Division of Clinical Trials and Epidemiological Sciences, National Cancer Centre Singapore, Singapore, Singapore
| | - Melvin Lk Chua
- Data Computational Science Core, National Cancer Centre Singapore, Singapore, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Iain Beehuat Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Data Computational Science Core, National Cancer Centre Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Department of Cancer Informatics, National Cancer Centre Singapore, Singapore, Singapore
| | - Marcus Eng Hock Ong
- Health Services Research Centre, SingHealth, Singapore
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Sean Shao Wei Lam
- Health Services Research Centre, SingHealth, Singapore
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
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8
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Nair D, Raveendran KU. Consumer satisfaction, palliative care and artificial intelligence (AI). BMJ Support Palliat Care 2024; 14:171-177. [PMID: 38490720 DOI: 10.1136/spcare-2023-004634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 02/23/2024] [Indexed: 03/17/2024]
Abstract
The scope of artificial intelligence (AI) in healthcare is promising, and AI has the potential to revolutionise the field of palliative care services also. Consumer satisfaction in palliative care is a critical aspect of providing high-quality end-of-life support. It encompasses various elements that contribute to a positive experience for both patients and their families. AI-based tools and technologies can help in early identification of the beneficiaries, reduce the cost, improve the quality of care and satisfaction to the patients with chronic life-limiting illnesses. However, it is essential to ensure that AI is used ethically and in a way that complements, rather than replaces, the human touch and compassionate care, which are the core components of palliative care. This article tries to analyse the scope and challenges of improving consumer satisfaction through AI-based technology in palliative care services.
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Affiliation(s)
- Devi Nair
- Health Management, Goa Institute of Management, Sanquelim, Goa, India
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9
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Wang SY, Ravindranath R, Stein JD. Prediction Models for Glaucoma in a Multicenter Electronic Health Records Consortium: The Sight Outcomes Research Collaborative. OPHTHALMOLOGY SCIENCE 2024; 4:100445. [PMID: 38317869 PMCID: PMC10838906 DOI: 10.1016/j.xops.2023.100445] [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] [Received: 09/19/2023] [Revised: 11/22/2023] [Accepted: 12/01/2023] [Indexed: 02/07/2024]
Abstract
Purpose Advances in artificial intelligence have enabled the development of predictive models for glaucoma. However, most work is single-center and uncertainty exists regarding the generalizability of such models. The purpose of this study was to build and evaluate machine learning (ML) approaches to predict glaucoma progression requiring surgery using data from a large multicenter consortium of electronic health records (EHR). Design Cohort study. Participants Thirty-six thousand five hundred forty-eight patients with glaucoma, as identified by International Classification of Diseases (ICD) codes from 6 academic eye centers participating in the Sight OUtcomes Research Collaborative (SOURCE). Methods We developed ML models to predict whether patients with glaucoma would progress to glaucoma surgery in the coming year (identified by Current Procedural Terminology codes) using the following modeling approaches: (1) penalized logistic regression (lasso, ridge, and elastic net); (2) tree-based models (random forest, gradient boosted machines, and XGBoost), and (3) deep learning models. Model input features included demographics, diagnosis codes, medications, and clinical information (intraocular pressure, visual acuity, refractive status, and central corneal thickness) available from structured EHR data. One site was reserved as an "external site" test set (N = 1550); of the patients from the remaining sites, 10% each were randomly selected to be in development and test sets, with the remaining 27 999 reserved for model training. Main Outcome Measures Evaluation metrics included area under the receiver operating characteristic curve (AUROC) on the test set and the external site. Results Six thousand nineteen (16.5%) of 36 548 patients underwent glaucoma surgery. Overall, the AUROC ranged from 0.735 to 0.771 on the random test set and from 0.706 to 0.754 on the external test site, with the XGBoost and random forest model performing best, respectively. There was greatest performance decrease from the random test set to the external test site for the penalized regression models. Conclusions Machine learning models developed using structured EHR data can reasonably predict whether glaucoma patients will need surgery, with reasonable generalizability to an external site. Additional research is needed to investigate the impact of protected class characteristics such as race or gender on model performance and fairness. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Sophia Y. Wang
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California
| | - Rohith Ravindranath
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California
| | - Joshua D. Stein
- Department of Ophthalmology & Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan
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10
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Huguet N, Chen J, Parikh RB, Marino M, Flocke SA, Likumahuwa-Ackman S, Bekelman J, DeVoe JE. Applying Machine Learning Techniques to Implementation Science. Online J Public Health Inform 2024; 16:e50201. [PMID: 38648094 DOI: 10.2196/50201] [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: 06/22/2023] [Revised: 11/15/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.
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Affiliation(s)
- Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Jinying Chen
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Data Science Core, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- iDAPT Implementation Science Center for Cancer Control, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ravi B Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Susan A Flocke
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Sonja Likumahuwa-Ackman
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Justin Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, United States
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
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11
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Kawashima A, Furukawa T, Imaizumi T, Morohashi A, Hara M, Yamada S, Hama M, Kawaguchi A, Sato K. Predictive Models for Palliative Care Needs of Advanced Cancer Patients Receiving Chemotherapy. J Pain Symptom Manage 2024; 67:306-316.e6. [PMID: 38218414 DOI: 10.1016/j.jpainsymman.2024.01.009] [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: 10/11/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/15/2024]
Abstract
CONTEXT Early palliative care is recommended within eight-week of diagnosing advanced cancer. Although guidelines suggest routine screening to identify cancer patients who could benefit from palliative care, implementing screening can be challenging due to understaffing and time constraints. OBJECTIVES To develop and evaluate machine learning models for predicting specialist palliative care needs in advanced cancer patients undergoing chemotherapy, and to investigate if predictive models could substitute screening tools. METHODS We conducted a retrospective cohort study using supervised machine learning. The study included patients aged 18 or older, diagnosed with metastatic or stage IV cancer, who underwent chemotherapy and distress screening at a designated cancer hospital in Japan from April 1, 2018, to March 31, 2023. Specialist palliative care needs were assessed based on distress screening scores and expert evaluations. Data sources were hospital's cancer registry, health claims database, and nursing admission records. The predictive model was developed using XGBoost, a machine learning algorithm. RESULTS Out of the 1878 included patients, 561 were analyzed. Among them, 114 (20.3%) exhibited needs for specialist palliative care. After under-sampling to address data imbalance, the models achieved an Area Under the Curve (AUC) of 0.89 with 95.8% sensitivity and a specificity of 71.9%. After feature selection, the model retained five variables, including the patient-reported pain score, and showcased an 0.82 AUC. CONCLUSION Our models could forecast specialist palliative care needs for advanced cancer patients on chemotherapy. Using five variables as predictors could replace screening tools and has the potential to contribute to earlier palliative care.
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Affiliation(s)
- Arisa Kawashima
- Division of Integrated Health Sciences (A.K. K.S.), Department of Nursing for Advanced Practice, Nagoya University Graduate School of Medicine, Nagoya, Japan; Department of Social Science (A.K.), Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan..
| | - Taiki Furukawa
- Medical IT Center (T.F.), Nagoya University Hospital, Nagoya, Japan; Department of Respiratory Medicine (T.F.), Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takahiro Imaizumi
- Department of Advanced Medicine (T.I., A.M.), Nagoya University Hospital, Nagoya, Japan
| | - Akemi Morohashi
- Department of Advanced Medicine (T.I., A.M.), Nagoya University Hospital, Nagoya, Japan
| | - Mariko Hara
- Department of Clinical Oncology and Chemotherapy (M.H., S.Y., M.H., A.K.), Nagoya University Hospital, Nagoya, Japan
| | - Satomi Yamada
- Department of Clinical Oncology and Chemotherapy (M.H., S.Y., M.H., A.K.), Nagoya University Hospital, Nagoya, Japan
| | - Masayo Hama
- Department of Clinical Oncology and Chemotherapy (M.H., S.Y., M.H., A.K.), Nagoya University Hospital, Nagoya, Japan
| | - Aya Kawaguchi
- Department of Clinical Oncology and Chemotherapy (M.H., S.Y., M.H., A.K.), Nagoya University Hospital, Nagoya, Japan
| | - Kazuki Sato
- Division of Integrated Health Sciences (A.K. K.S.), Department of Nursing for Advanced Practice, Nagoya University Graduate School of Medicine, Nagoya, Japan
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12
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Khaliq M, Shaikh I, Soman S. Editorial on an autoencoder algorithm for the prediction of stroke patients with left ventricular thrombus (LVT). J Neurol Sci 2024; 458:122928. [PMID: 38367487 DOI: 10.1016/j.jns.2024.122928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 02/06/2024] [Indexed: 02/19/2024]
Affiliation(s)
- Muhammad Khaliq
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ibraheem Shaikh
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Salil Soman
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
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13
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Cohen NM, Lifshitz A, Jaschek R, Rinott E, Balicer R, Shlush LI, Barbash GI, Tanay A. Longitudinal machine learning uncouples healthy aging factors from chronic disease risks. NATURE AGING 2024; 4:129-144. [PMID: 38062254 DOI: 10.1038/s43587-023-00536-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/02/2023] [Indexed: 01/21/2024]
Abstract
To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.
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Affiliation(s)
- Netta Mendelson Cohen
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Aviezer Lifshitz
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Rami Jaschek
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ehud Rinott
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ran Balicer
- Clalit Research Institute, Ramat Gan, Israel
| | - Liran I Shlush
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Gabriel I Barbash
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
| | - Amos Tanay
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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14
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Seevaratnam B, Wang S, Fong R, Hui F, Callahan A, Chobot S, Gensheimer MF, Li RC, Nguyen D, Ramchandran K, Shah NH, Shieh L, Zeng JGQ, Teuteberg W. Lessons Learned from a Multi-Site, Team-Based Serious Illness Care Program Implementation at an Academic Medical Center. J Palliat Med 2024; 27:83-89. [PMID: 37935036 DOI: 10.1089/jpm.2023.0254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023] Open
Abstract
Background: Patients with serious illness benefit from conversations to share prognosis and explore goals and values. To address this, we implemented Ariadne Labs' Serious Illness Care Program (SICP) at Stanford Health Care. Objective: Improve quantity, timing, and quality of serious illness conversations. Methods: Initial implementation followed Ariadne Labs' SICP framework. We later incorporated a team-based approach that included nonphysician care team members. Outcomes included number of patients with documented conversations according to clinician role and practice location. Machine learning algorithms were used in some settings to identify eligible patients. Results: Ambulatory oncology and hospital medicine were our largest implementation sites, engaging 4707 and 642 unique patients in conversations, respectively. Clinicians across eight disciplines engaged in these conversations. Identified barriers that included leadership engagement, complex workflows, and patient identification. Conclusion: Several factors contributed to successful SICP implementation across clinical sites: innovative clinical workflows, machine learning based predictive algorithms, and nonphysician care team member engagement.
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Affiliation(s)
- Briththa Seevaratnam
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Samantha Wang
- Division of Hospital Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Rebecca Fong
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Felicia Hui
- Section of Palliative Care, Stanford University School of Medicine, Stanford, California, USA
| | - Alison Callahan
- Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA
| | | | - Michael F Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Ron C Li
- Division of Hospital Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Duy Nguyen
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Kavitha Ramchandran
- Section of Palliative Care, Stanford University School of Medicine, Stanford, California, USA
- Division of Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Nigam H Shah
- Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA
| | - Lisa Shieh
- Division of Hospital Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Jack Guo-Qing Zeng
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Winifred Teuteberg
- Section of Palliative Care, Stanford University School of Medicine, Stanford, California, USA
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15
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Serghiou S, Rough K. Deep Learning for Epidemiologists: An Introduction to Neural Networks. Am J Epidemiol 2023; 192:1904-1916. [PMID: 37139570 DOI: 10.1093/aje/kwad107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 11/30/2022] [Accepted: 04/24/2023] [Indexed: 05/05/2023] Open
Abstract
Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g., overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article's scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers.
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16
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Love CS. "Just the Facts Ma'am": Moral and Ethical Considerations for Artificial Intelligence in Medicine and its Potential to Impact Patient Autonomy and Hope. LINACRE QUARTERLY 2023; 90:375-394. [PMID: 37974568 PMCID: PMC10638968 DOI: 10.1177/00243639231162431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Applying machine-based learning and synthetic cognition, commonly referred to as artificial intelligence (AI), to medicine intimates prescient knowledge. The ability of these algorithms to potentially unlock secrets held within vast data sets makes them invaluable to healthcare. Complex computer algorithms are routinely used to enhance diagnoses in fields like oncology, cardiology, and neurology. These algorithms have found utility in making healthcare decisions that are often complicated by seemingly endless relationships between exogenous and endogenous variables. They have also found utility in the allocation of limited healthcare resources and the management of end-of-life issues. With the increase in computing power and the ability to test a virtually unlimited number of relationships, scientists and engineers have the unprecedented ability to increase the prognostic confidence that comes from complex data analysis. While these systems present exciting opportunities for the democratization and precision of healthcare, their use raises important moral and ethical considerations around Christian concepts of autonomy and hope. The purpose of this essay is to explore some of the practical limitations associated with AI in medicine and discuss some of the potential theological implications that machine-generated diagnoses may present. Specifically, this article examines how these systems may disrupt the patient and healthcare provider relationship emblematic of Christ's healing mission. Finally, this article seeks to offer insights that might help in the development of a more robust ethical framework for the application of these systems in the future.
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17
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Suppadungsuk S, Thongprayoon C, Miao J, Krisanapan P, Qureshi F, Kashani K, Cheungpasitporn W. Exploring the Potential of Chatbots in Critical Care Nephrology. MEDICINES (BASEL, SWITZERLAND) 2023; 10:58. [PMID: 37887265 PMCID: PMC10608511 DOI: 10.3390/medicines10100058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023]
Abstract
The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.
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Affiliation(s)
- Supawadee Suppadungsuk
- 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
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Nephrology and Hypertension, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Kianoush Kashani
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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18
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Teeple S, Chivers C, Linn KA, Halpern SD, Eneanya N, Draugelis M, Courtright K. Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis. BMJ Qual Saf 2023; 32:503-516. [PMID: 37001995 PMCID: PMC10898860 DOI: 10.1136/bmjqs-2022-015173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 03/08/2023] [Indexed: 04/03/2023]
Abstract
OBJECTIVE Evaluate predictive performance of an electronic health record (EHR)-based, inpatient 6-month mortality risk model developed to trigger palliative care consultation among patient groups stratified by age, race, ethnicity, insurance and socioeconomic status (SES), which may vary due to social forces (eg, racism) that shape health, healthcare and health data. DESIGN Retrospective evaluation of prediction model. SETTING Three urban hospitals within a single health system. PARTICIPANTS All patients ≥18 years admitted between 1 January and 31 December 2017, excluding observation, obstetric, rehabilitation and hospice (n=58 464 encounters, 41 327 patients). MAIN OUTCOME MEASURES General performance metrics (c-statistic, integrated calibration index (ICI), Brier Score) and additional measures relevant to health equity (accuracy, false positive rate (FPR), false negative rate (FNR)). RESULTS For black versus non-Hispanic white patients, the model's accuracy was higher (0.051, 95% CI 0.044 to 0.059), FPR lower (-0.060, 95% CI -0.067 to -0.052) and FNR higher (0.049, 95% CI 0.023 to 0.078). A similar pattern was observed among patients who were Hispanic, younger, with Medicaid/missing insurance, or living in low SES zip codes. No consistent differences emerged in c-statistic, ICI or Brier Score. Younger age had the second-largest effect size in the mortality prediction model, and there were large standardised group differences in age (eg, 0.32 for non-Hispanic white versus black patients), suggesting age may contribute to systematic differences in the predicted probabilities between groups. CONCLUSIONS An EHR-based mortality risk model was less likely to identify some marginalised patients as potentially benefiting from palliative care, with younger age pinpointed as a possible mechanism. Evaluating predictive performance is a critical preliminary step in addressing algorithmic inequities in healthcare, which must also include evaluating clinical impact, and governance and regulatory structures for oversight, monitoring and accountability.
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Affiliation(s)
- Stephanie Teeple
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Kristin A Linn
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Scott D Halpern
- Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nwamaka Eneanya
- Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | | | - Katherine Courtright
- Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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19
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Liu JH, Shih CY, Huang HL, Peng JK, Cheng SY, Tsai JS, Lai F. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. J Med Internet Res 2023; 25:e47366. [PMID: 37594793 PMCID: PMC10474512 DOI: 10.2196/47366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/02/2023] [Accepted: 07/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. OBJECTIVE This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. METHODS This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. RESULTS From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. CONCLUSIONS We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. TRIAL REGISTRATION ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.
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Affiliation(s)
- Jen-Hsuan Liu
- Department of Family Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Chih-Yuan Shih
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Hsien-Liang Huang
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jen-Kuei Peng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shao-Yi Cheng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jaw-Shiun Tsai
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
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20
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SenthilKumar G, Madhusudhana S, Flitcroft M, Sheriff S, Thalji S, Merrill J, Clarke CN, Maduekwe UN, Tsai S, Christians KK, Gamblin TC, Kothari AN. Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer. Sci Rep 2023; 13:11051. [PMID: 37422500 PMCID: PMC10329647 DOI: 10.1038/s41598-023-37396-3] [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: 12/30/2022] [Accepted: 06/21/2023] [Indexed: 07/10/2023] Open
Abstract
Early postoperative mortality risk prediction is crucial for clinical management of gastric cancer. This study aims to predict 90-day mortality in gastric cancer patients undergoing gastrectomy using automated machine learning (AutoML), optimize models for preoperative prediction, and identify factors influential in prediction. National Cancer Database was used to identify stage I-III gastric cancer patients undergoing gastrectomy between 2004 and 2016. 26 features were used to train predictive models using H2O.ai AutoML. Performance on validation cohort was measured. In 39,108 patients, 90-day mortality rate was 8.8%. The highest performing model was an ensemble (AUC = 0.77); older age, nodal ratio, and length of inpatient stay (LOS) following surgery were most influential for prediction. Removing the latter two parameters decreased model performance (AUC 0.71). For optimizing models for preoperative use, models were developed to first predict node ratio or LOS, and these predicted values were inputted for 90-day mortality prediction (AUC of 0.73-0.74). AutoML performed well in predicting 90-day mortality in a larger cohort of gastric cancer patients that underwent gastrectomy. These models can be implemented preoperatively to inform prognostication and patient selection for surgery. Our study supports broader evaluation and application of AutoML to guide surgical oncologic care.
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Affiliation(s)
- Gopika SenthilKumar
- Medical Scientist Training Program, Medical College of Wisconsin, Milwaukee, USA
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Sharadhi Madhusudhana
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Madelyn Flitcroft
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Salma Sheriff
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Samih Thalji
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Jennifer Merrill
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Callisia N Clarke
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Ugwuji N Maduekwe
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Susan Tsai
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Kathleen K Christians
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - T Clark Gamblin
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
| | - Anai N Kothari
- Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA.
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21
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Okeibunor JC, Jaca A, Iwu-Jaja CJ, Idemili-Aronu N, Ba H, Zantsi ZP, Ndlambe AM, Mavundza E, Muneene D, Wiysonge CS, Makubalo L. The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review. Front Public Health 2023; 11:1102185. [PMID: 37469694 PMCID: PMC10352788 DOI: 10.3389/fpubh.2023.1102185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Background Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care. Methods We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data. Results Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health. Conclusions Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.
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Affiliation(s)
| | - Anelisa Jaca
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | | | - Ngozi Idemili-Aronu
- Department of Sociology/Anthropology, University of Nigeria, Nsukka, Nigeria
| | - Housseynou Ba
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | - Zukiswa Pamela Zantsi
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Asiphe Mavis Ndlambe
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Edison Mavundza
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | | | - Charles Shey Wiysonge
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
- HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban, South Africa
| | - Lindiwe Makubalo
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
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22
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Toqan D, Malak MZ, Ayed A, Hamaideh SH, Al-Amer R. Perception of Nurses' Knowledge about Palliative Care in West Bank/ Palestine: Levels and Influencing Factors. J Palliat Care 2023; 38:336-344. [PMID: 36278305 DOI: 10.1177/08258597221133958] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Objective: Healthcare professionals particularly nurses should be professionally prepared with knowledge about the standards of palliative care and their roles in providing palliative care. Nurses' knowledge about palliative care and influencing factors has not been examined adequately in Arab countries including Palestine. Thus, this study aimed to assess the adequacy of knowledge level and influencing factors (socio-demographic) about palliative care among nurses in West Bank/ Palestine. Methods: A descriptive-correlational design was utilized. A cluster random sampling method was applied to select 12 hospitals from the three regions in West Bank. Then, four hospitals were selected from each region using a simple random method. All registered nurses working in critical care units and medical and surgical wards in the selected hospitals were recruited. The sample consists of 424 registered nurses and data were collected using Palliative Care Quiz for Nursing (PCQN). Results: The Findings revealed that nurses' level of knowledge about palliative care was low/inadequate (M = 7.75, SD = 2.96). Knowledge about palliative care was influenced by age (B = -.106; p < 0.05), gender (B = -.223; p < 0.001), and hospital ward (B = -.597; p < 0.001), in which younger nurses, females, and those who work in critical care units reported higher levels of knowledge about palliative care. Conclusions: Findings of this study emphasized the need for developing educational and training courses, seminars, and workshops on palliative care to increase nurses' knowledge in order to enhance the quality of patient care. Also, policymakers should develop national strategic plans and policies regarding palliative care and apply these plans in all hospitals in West Bank/ Palestine.
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Affiliation(s)
- Dalia Toqan
- Nursing Education, Faculty of Nursing, Arab American University of Palestine (AAUP), Jenin, Palestine
| | - Malakeh Z Malak
- Community Health Nursing, Faculty of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan
| | - Ahmad Ayed
- Pediatric Health Nursing, Faculty of Nursing, Arab American University of Palestine (AAUP), Jenin, Palestine
| | - Shaher H Hamaideh
- Community and Mental Health Nursing Department, Faculty of Nursing, The Hashemite University, Zarqa, Jordan
| | - Rasmieh Al-Amer
- Psychiatric Health Nursing, Faculty of Nursing, Isra University, Amman, Jordan
- School of Nursing and Midwifery, Western Sydney University, Sydney, NSW, Australia
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Corbin CK, Baiocchi M, Chen JH. Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2023; 2023:81-90. [PMID: 37350883 PMCID: PMC10283136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading. In this study we describe three classes of label selection and simulate five causally distinct scenarios to assess how particular selection mechanisms bias a suite of commonly reported binary machine learning model performance metrics. Simulations reveal that when selection is affected by observed features, naive estimates of model discrimination may be misleading. When selection is affected by labels, naive estimates of calibration fail to reflect reality. We borrow traditional weighting estimators from causal inference literature and find that when selection probabilities are properly specified, they recover full population estimates. We then tackle the real-world task of monitoring the performance of deployed machine learning models whose interactions with clinicians feed-back and affect the selection mechanism of the labels. We train three machine learning models to flag low-yield laboratory diagnostics, and simulate their intended consequence of reducing wasteful laboratory utilization. We find that naive estimates of AUROC on the observed population undershoot actual performance by up to 20%. Such a disparity could be large enough to lead to the wrongful termination of a successful clinical decision support tool. We propose an altered deployment procedure, one that combines injected randomization with traditional weighted estimates, and find it recovers true model performance.
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Affiliation(s)
- Conor K Corbin
- Department of Biomedical Data Science, Stanford, California, USA
- Center for Biomedical Informatics Research, Stanford, California, USA
| | - Michael Baiocchi
- Department of Epidemiology and Population Health, Stanford, California, USA
| | - Jonathan H Chen
- Center for Biomedical Informatics Research, Stanford, California, USA
- Division of Hospital Medicine, Stanford, California, USA
- Clinical Excellence Research Center, Stanford, California, USA
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24
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Boyden JY, Bogetz JF, Johnston EE, Thienprayoon R, Williams CSP, McNeil MJ, Patneaude A, Widger KA, Rosenberg AR, Ananth P. Measuring Pediatric Palliative Care Quality: Challenges and Opportunities. J Pain Symptom Manage 2023; 65:e483-e495. [PMID: 36736860 PMCID: PMC10106436 DOI: 10.1016/j.jpainsymman.2023.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
Pediatric palliative care (PPC) programs vary widely in structure, staffing, funding, and patient census, resulting in inconsistency in service provision. Improving the quality of palliative care for children living with serious illness and their families requires measuring care quality, ensuring that quality measurement is embedded into day-to-day clinical practice, and aligning quality measurement with healthcare policy priorities. Yet, numerous challenges exist in measuring PPC quality. This paper provides an overview of PPC quality measurement, including challenges, current initiatives, and future opportunities. While important strides toward addressing quality measurement challenges in PPC have been made, including ongoing quality measurement initiatives like the Cambia Metrics Project, the PPC What Matters Most study, and collaborative learning networks, more work remains. Providing high-quality PPC to all children and families will require a multi-pronged approach. In this paper, we suggest several strategies for advancing high-quality PPC, which includes 1) considering how and by whom success is defined, 2) evaluating, adapting, and developing PPC measures, including those that address care disparities within PPC for historically marginalized and excluded communities, 3) improving the infrastructure with which to routinely and prospectively measure, monitor, and report clinical and administrative quality measures, 4) increasing endorsement of PPC quality measures by prominent quality organizations to facilitate accountability and possible reimbursement, and 5) integrating PPC-specific quality measures into the administrative, funding, and policy landscape of pediatric healthcare.
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Affiliation(s)
- Jackelyn Y Boyden
- Department of Family and Community Health, School of Nursing (J.Y.B.), University of Pennsylvania, Philadelphia, Pennsylvania, USA.
| | - Jori F Bogetz
- Department of Pediatrics, Division of Bioethics and Palliative Care (J.F.B.), University of Washington School of Medicine, Seattle, Washington, USA; Center for Clinical and Translational Research (J.F.B.), Seattle Children's Research Institute, Seattle, Washington, USA
| | - Emily E Johnston
- Department of Pediatrics, Division of Hematology and Oncology (E.E.J.), University of Alabama at Birmingham, Birmingham, Alabama, USA; Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham (E.E.J.), Birmingham, Alabama, USA
| | - Rachel Thienprayoon
- Department of Anesthesia, Division of Palliative Care, Cincinnati Children's Hospital Medical Center (R.T.), Cincinnati, Ohio, USA; Department of Pediatrics, Cincinnati Children's Hospital Medical Center (R.T.), Cincinnati, Ohio, USA
| | - Conrad S P Williams
- Palliative Care Program and Department of Pediatrics (C.S.P.W.), Medical University of South Carolina, Charleston, South Carolina, USA
| | - Michael J McNeil
- St. Jude Children's Research Hospital, Department of Global Pediatric Medicine (M.J.M.), Memphis, Tennessee, USA; St. Jude Children's Research Hospital, Division of Quality and Life and Palliative Care, Department of Oncology (M.J.M.), Memphis, Tennessee, USA
| | - Arika Patneaude
- Bioethics and Palliative Care, Seattle Children's Hospital (A.P.), Seattle, Washington, USA; University of Washington School of Social Work (A.P.), Seattle, Washington, USA; Treuman Katz Center for Pediatric Bioethics (A.P.), Seattle, Washington, USA
| | - Kimberley A Widger
- Lawrence S. Bloomberg Faculty of Nursing (K.A.W.), University of Toronto, Toronto, Ontario, Canada; Hospital for Sick Children (K.A.W.), Toronto, Ontario, Canada
| | - Abby R Rosenberg
- Department of Psychosocial Oncology and Palliative Care (A.R.S.), Dana-Farber Cancer Institute, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School (A.R.S.), Boston, Massachusetts, USA
| | - Prasanna Ananth
- Department of Pediatrics, Yale School of Medicine (P.A.), New Haven, Connecticut, USA; Yale Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center (P.A.), New Haven, Connecticut, USA
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25
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Cagliero D, Deuitch N, Shah N, Feudtner C, Char D. A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning. J Am Med Inform Assoc 2023; 30:819-827. [PMID: 36826400 PMCID: PMC10114055 DOI: 10.1093/jamia/ocad022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 02/02/2023] [Accepted: 02/09/2023] [Indexed: 02/25/2023] Open
Abstract
OBJECTIVE Identifying ethical concerns with ML applications to healthcare (ML-HCA) before problems arise is now a stated goal of ML design oversight groups and regulatory agencies. Lack of accepted standard methodology for ethical analysis, however, presents challenges. In this case study, we evaluate use of a stakeholder "values-collision" approach to identify consequential ethical challenges associated with an ML-HCA for advanced care planning (ACP). Identification of ethical challenges could guide revision and improvement of the ML-HCA. MATERIALS AND METHODS We conducted semistructured interviews of the designers, clinician-users, affiliated administrators, and patients, and inductive qualitative analysis of transcribed interviews using modified grounded theory. RESULTS Seventeen stakeholders were interviewed. Five "values-collisions"-where stakeholders disagreed about decisions with ethical implications-were identified: (1) end-of-life workflow and how model output is introduced; (2) which stakeholders receive predictions; (3) benefit-harm trade-offs; (4) whether the ML design team has a fiduciary relationship to patients and clinicians; and, (5) how and if to protect early deployment research from external pressures, like news scrutiny, before research is completed. DISCUSSION From these findings, the ML design team prioritized: (1) alternative workflow implementation strategies; (2) clarification that prediction was only evaluated for ACP need, not other mortality-related ends; and (3) shielding research from scrutiny until endpoint driven studies were completed. CONCLUSION In this case study, our ethical analysis of this ML-HCA for ACP was able to identify multiple sites of intrastakeholder disagreement that mark areas of ethical and value tension. These findings provided a useful initial ethical screening.
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Affiliation(s)
- Diana Cagliero
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Natalie Deuitch
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- National Institutes of Health, National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Nigam Shah
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Palo Alto, California, USA
| | - Chris Feudtner
- The Department of Medical Ethics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Departments of Pediatrics, Medical Ethics and Healthcare Policy, The Perelman School of Medicine, The University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Danton Char
- Division of Pediatric Cardiac Anesthesia, Department of Anesthesiology, Stanford University School of Medicine, Stanford, California, USA
- Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USA
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26
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Jalamangala Shivananjaiah SK, Kumari S, Majid I, Wang SY. Predicting near-term glaucoma progression: An artificial intelligence approach using clinical free-text notes and data from electronic health records. Front Med (Lausanne) 2023; 10:1157016. [PMID: 37122330 PMCID: PMC10133544 DOI: 10.3389/fmed.2023.1157016] [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: 02/02/2023] [Accepted: 02/15/2023] [Indexed: 05/02/2023] Open
Abstract
Purpose The purpose of this study was to develop a model to predict whether or not glaucoma will progress to the point of requiring surgery within the following year, using data from electronic health records (EHRs), including both structured data and free-text progress notes. Methods A cohort of adult glaucoma patients was identified from the EHR at Stanford University between 2008 and 2020, with data including free-text clinical notes, demographics, diagnosis codes, prior surgeries, and clinical information, including intraocular pressure, visual acuity, and central corneal thickness. Words from patients' notes were mapped to ophthalmology domain-specific neural word embeddings. Word embeddings and structured clinical data were combined as inputs to deep learning models to predict whether a patient would undergo glaucoma surgery in the following 12 months using the previous 4-12 months of clinical data. We also evaluated models using only structured data inputs (regression-, tree-, and deep-learning-based models) and models using only text inputs. Results Of the 3,469 glaucoma patients included in our cohort, 26% underwent surgery. The baseline penalized logistic regression model achieved an area under the receiver operating curve (AUC) of 0.873 and F1 score of 0.750, compared with the best tree-based model (random forest, AUC 0.876; F1 0.746), the deep learning structured features model (AUC 0.885; F1 0.757), the deep learning clinical free-text features model (AUC 0.767; F1 0.536), and the deep learning model with both the structured clinical features and free-text features (AUC 0.899; F1 0.745). Discussion Fusion models combining text and EHR structured data successfully and accurately predicted glaucoma progression to surgery. Future research incorporating imaging data could further optimize this predictive approach and be translated into clinical decision support tools.
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Affiliation(s)
| | | | | | - Sophia Y. Wang
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, United States
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27
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Newman MG, Porucznik CA, Date AP, Abdelrahman S, Schliep KC, VanDerslice JA, Smith KR, Hanson HA. Generating Older Adult Multimorbidity Trajectories Using Various Comorbidity Indices and Calculation Methods. Innov Aging 2023; 7:igad023. [PMID: 37179657 PMCID: PMC10168588 DOI: 10.1093/geroni/igad023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Indexed: 05/15/2023] Open
Abstract
Background and Objectives Older adult multimorbidity trajectories are helpful for understanding the current and future health patterns of aging populations. The construction of multimorbidity trajectories from comorbidity index scores will help inform public health and clinical interventions targeting those individuals that are on unhealthy trajectories. Investigators have used many different techniques when creating multimorbidity trajectories in prior literature, and no standard way has emerged. This study compares and contrasts multimorbidity trajectories constructed from various methods. Research Design and Methods We describe the difference between aging trajectories constructed with the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). We also explore the differences between acute (single-year) and chronic (cumulative) derivations of CCI and ECI scores. Social determinants of health can affect disease burden over time; thus, our models include income, race/ethnicity, and sex differences. Results We use group-based trajectory modeling (GBTM) to estimate multimorbidity trajectories for 86,909 individuals aged 66-75 in 1992 using Medicare claims data collected over the following 21 years. We identify low-chronic disease and high-chronic disease trajectories in all 8 generated trajectory models. Additionally, all 8 models satisfied prior established statistical diagnostic criteria for well-performing GBTM models. Discussion and Implications Clinicians may use these trajectories to identify patients on an unhealthy path and prompt a possible intervention that may shift the patient to a healthier trajectory.
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Affiliation(s)
- Michael G Newman
- Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
- Utah Population Database, University of Utah, Salt Lake City, Utah, USA
| | - Christina A Porucznik
- Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Ankita P Date
- Utah Population Database, University of Utah, Salt Lake City, Utah, USA
| | - Samir Abdelrahman
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA
- Computer Science Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt
| | - Karen C Schliep
- Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - James A VanDerslice
- Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Ken R Smith
- Utah Population Database, University of Utah, Salt Lake City, Utah, USA
- Department of Family and Consumer Studies, University of Utah, Salt Lake City, Utah, USA
| | - Heidi A Hanson
- Advanced Computing for Health Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
- Department of Surgery, University of Utah School of Medicine, Salt Lake City, Utah, USA
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28
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Chi S, Kim S, Reuter M, Ponzillo K, Oliver DP, Foraker R, Heard K, Liu J, Pitzer K, White P, Moore N. Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm. JAMA Netw Open 2023; 6:e238795. [PMID: 37071421 PMCID: PMC10114011 DOI: 10.1001/jamanetworkopen.2023.8795] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/28/2023] [Indexed: 04/19/2023] Open
Abstract
Importance Goal-concordant care is an ongoing challenge in hospital settings. Identification of high mortality risk within 30 days may call attention to the need to have serious illness conversations, including the documentation of patient goals of care. Objective To examine goals of care discussions (GOCDs) in a community hospital setting with patients identified as having a high risk of mortality by a machine learning mortality prediction algorithm. Design, Setting, and Participants This cohort study took place at community hospitals within 1 health care system. Participants included adult patients with a high risk of 30-day mortality who were admitted to 1 of 4 hospitals between January 2 and July 15, 2021. Patient encounters of inpatients in the intervention hospital where physicians were notified of the computed high risk mortality score were compared with patient encounters of inpatients in 3 community hospitals without the intervention (ie, matched control). Intervention Physicians of patients with a high risk of mortality within 30 days received notification and were encouraged to arrange for GOCDs. Main Outcomes and Measures The primary outcome was the percentage change of documented GOCDs prior to discharge. Propensity-score matching was completed on a preintervention and postintervention period using age, sex, race, COVID-19 status, and machine learning-predicted mortality risk scores. A difference-in-difference analysis validated the results. Results Overall, 537 patients were included in this study with 201 in the preintervention period (94 in the intervention group; 104 in the control group) and 336 patients in the postintervention period. The intervention and control groups included 168 patients per group and were well-balanced in age (mean [SD], 79.3 [9.60] vs 79.6 [9.21] years; standardized mean difference [SMD], 0.03), sex (female, 85 [51%] vs 85 [51%]; SMD, 0), race (White patients, 145 [86%] vs 144 [86%]; SMD 0.006), and Charlson comorbidities (median [range], 8.00 [2.00-15.0] vs 9.00 [2.00 to 19.0]; SMD, 0.34). Patients in the intervention group from preintervention to postintervention period were associated with being 5 times more likely to have documented GOCDs (OR, 5.11 [95% CI, 1.93 to 13.42]; P = .001) by discharge compared with matched controls, and GOCD occurred significantly earlier in the hospitalization in the intervention patients as compared with matched controls (median, 4 [95% CI, 3 to 6] days vs 16 [95% CI, 15 to not applicable] days; P < .001). Similar findings were observed for Black patient and White patient subgroups. Conclusions and Relevance In this cohort study, patients whose physicians had knowledge of high-risk predictions from machine learning mortality algorithms were associated with being 5 times more likely to have documented GOCDs than matched controls. Additional external validation is needed to determine if similar interventions would be helpful at other institutions.
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Affiliation(s)
- Stephen Chi
- Division of Pulmonary and Critical Care Medicine, Washington University in St Louis, St Louis, Missouri
| | - Seunghwan Kim
- Institute for Informatics, Washington University in St Louis, St Louis, Missouri
| | | | | | - Debra Parker Oliver
- Division of Palliative Medicine, Department of Medicine, Washington University in St Louis, St Louis, Missouri
| | - Randi Foraker
- Institute for Informatics, Washington University in St Louis, St Louis, Missouri
| | | | - Jingxia Liu
- Division of Public Health Sciences, Department of Surgery, Washington University in St Louis, St Louis, Missouri
- Division of Biostatistics, Washington University in St Louis, St Louis, Missouri
| | - Kyle Pitzer
- Division of Palliative Medicine, Department of Medicine, Washington University in St Louis, St Louis, Missouri
- Division of Biostatistics, Washington University in St Louis, St Louis, Missouri
| | - Patrick White
- Division of Palliative Medicine, Department of Medicine, Washington University in St Louis, St Louis, Missouri
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Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:3080. [PMID: 36991790 PMCID: PMC10052105 DOI: 10.3390/s23063080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 06/19/2023]
Abstract
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.
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Affiliation(s)
- Erkan Bostanci
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Engin Kocak
- Department of Analytical Chemistry, Faculty of Gülhane Pharmacy, University of Health Sciences, 06018 Ankara, Turkey
| | - Metehan Unal
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Mehmet Serdar Guzel
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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30
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Applications of Machine Learning in Palliative Care: A Systematic Review. Cancers (Basel) 2023; 15:cancers15051596. [PMID: 36900387 PMCID: PMC10001037 DOI: 10.3390/cancers15051596] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.
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Bowers A, Drake C, Makarkin AE, Monzyk R, Maity B, Telle A. Predicting Patient Mortality for Earlier Palliative Care Identification in Medicare Advantage Plans: Features of a Machine Learning Model. JMIR AI 2023; 2:e42253. [PMID: 38875557 PMCID: PMC11041411 DOI: 10.2196/42253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/21/2022] [Accepted: 12/20/2022] [Indexed: 06/16/2024]
Abstract
BACKGROUND Machine learning (ML) can offer greater precision and sensitivity in predicting Medicare patient end of life and potential need for palliative services compared to provider recommendations alone. However, earlier ML research on older community dwelling Medicare beneficiaries has provided insufficient exploration of key model feature impacts and the role of the social determinants of health. OBJECTIVE This study describes the development of a binary classification ML model predicting 1-year mortality among Medicare Advantage plan members aged ≥65 years (N=318,774) and further examines the top features of the predictive model. METHODS A light gradient-boosted trees model configuration was selected based on 5-fold cross-validation. The model was trained with 80% of cases (n=255,020) using randomized feature generation periods, with 20% (n=63,754) reserved as a holdout for validation. The final algorithm used 907 feature inputs extracted primarily from claims and administrative data capturing patient diagnoses, service utilization, demographics, and census tract-based social determinants index measures. RESULTS The total sample had an actual mortality prevalence of 3.9% in the 2018 outcome period. The final model correctly predicted 44.2% of patient expirations among the top 1% of highest risk members (AUC=0.84; 95% CI 0.83-0.85) versus 24.0% predicted by the model iteration using only age, gender, and select high-risk utilization features (AUC=0.74; 95% CI 0.73-0.74). The most important algorithm features included patient demographics, diagnoses, pharmacy utilization, mean costs, and certain social determinants of health. CONCLUSIONS The final ML model better predicts Medicare Advantage member end of life using a variety of routinely collected data and supports earlier patient identification for palliative care.
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Affiliation(s)
- Anne Bowers
- Evernorth Health, Inc, St. Louis, MO, United States
| | | | | | | | | | - Andrew Telle
- Evernorth Health, Inc, St. Louis, MO, United States
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32
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Rossnan O, Hanson A, Spaulding A, Satashia P, Bhakta S, Robinson M, Helgeson SA, Moreno-Franco P, Sanghavi D. Improved needs identification in medical intensive care and palliative medicine: retrospective cohort study. BMJ Support Palliat Care 2023:spcare-2023-004205. [PMID: 36797044 DOI: 10.1136/spcare-2023-004205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/04/2023] [Indexed: 02/18/2023]
Affiliation(s)
- Olivia Rossnan
- Department of Critical Care Medicine, Mayo Clinic in Florida, Jacksonville, Florida, USA
| | - Abby Hanson
- Department of Critical Care Medicine, Mayo Clinic in Florida, Jacksonville, Florida, USA
| | - Aaron Spaulding
- Health Sciences Research, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Shivang Bhakta
- Department of Critical Care Medicine, Mayo Clinic in Florida, Jacksonville, Florida, USA
| | - Maisha Robinson
- Department of Critical Care Medicine, Mayo Clinic in Florida, Jacksonville, Florida, USA
| | - Scott A Helgeson
- Department of Internal Medicine, Mayo Clinic's Campus in Florida, Jacksonville, Florida, USA
| | - Pablo Moreno-Franco
- Transplant Medicine, Critical Care Services, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Devang Sanghavi
- Department of Critical Care Medicine, Mayo Clinic in Florida, Jacksonville, Florida, USA
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Liu L, Wan H, Liu L, Wang J, Tang Y, Cui S, Li Y. Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer. Diagnostics (Basel) 2023; 13:diagnostics13040748. [PMID: 36832236 PMCID: PMC9954966 DOI: 10.3390/diagnostics13040748] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/13/2023] [Accepted: 01/28/2023] [Indexed: 02/18/2023] Open
Abstract
This study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a total of 185 patients with pathologically confirmed HGSOC. A total of 185 patients were randomly assigned in a 5:3:2 ratio to a training cohort (n = 92), validation cohort 1 (n = 56), and validation cohort 2 (n = 37). We built a new deep learning network from 3839 preoperative MRI images (T2-weighted images and diffusion-weighted images) to extract HGSOC prognostic indicators. Following that, a fusion model including clinical and deep learning features is developed to predict patients' individual recurrence risk and 3-year recurrence likelihood. In the two validation cohorts, the consistency index of the fusion model was higher than both the deep learning model and the clinical feature model (0.752, 0.813 vs. 0.625, 0.600 vs. 0.505, 0.501). Among the three models, the fusion model had a higher AUC than either the deep learning model or the clinical model in validation cohorts 1 or 2 (AUC = was 0.986, 0.961 vs. 0.706, 0.676/0.506, 0.506). Using the DeLong method, the difference between them was statistically significant (p < 0.05). The Kaplan-Meier analysis distinguished two patient groups with high and low recurrence risk (p = 0.0008 and 0.0035, respectively). Deep learning may be a low-cost, non-invasive method for predicting risk for advanced HGSOC recurrence. Deep learning based on multi-sequence MRI serves as a prognostic biomarker for advanced HGSOC, which provides a preoperative model for predicting recurrence in HGSOC. Additionally, using the fusion model as a new prognostic analysis means that can use MRI data can be used without the need to follow-up the prognostic biomarker.
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Affiliation(s)
- Lili Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
- Department of Radiology, Chongqing General Hospital, Chongqing 401120, China
| | - Haoming Wan
- College of Computer and Information Science, Chongqing Normal University, Chongqing 400016, China
| | - Li Liu
- Department of Radiology, The People’s Hospital of Yubei District of Chongqing, Chongqing 401120, China
| | - Jie Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yibo Tang
- College of Computer and Information Science, Chongqing Normal University, Chongqing 400016, China
| | - Shaoguo Cui
- College of Computer and Information Science, Chongqing Normal University, Chongqing 400016, China
- Correspondence: (S.C.); (Y.L.)
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China
- Correspondence: (S.C.); (Y.L.)
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Mesafint Belete D, D. Huchaiah M. A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS Dataset. Infect Dis (Lond) 2023. [DOI: 10.5772/intechopen.104224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
Abstract
At present, HIV/AIDS has steadily been listed in the top position as a major cause of death. However, HIV is largely preventable and can be avoided by making strategies to increase HIV early prediction. So, there is a need for a predictive tool that can help the domain experts with early prediction of the disease and hence can recommend strategies to stop the prognosis of the diseases. Using deep learning models, we investigated whether demographic and health survey dataset might be utilized to predict HIV test status. The contribution of this work is to improve the accuracy of a model for predicting an individual’s HIV test status. We employed deep learning models to predict HIV status using Ethiopian demography and health survey (EDHS) datasets. Furthermore, we discovered that predictive models based on these dataset may be used to forecast individuals’ HIV test status, which might assist domain experts prioritize strategies and policies to safeguard the pandemic. The outcome of the study confirms that a DL model provides the best results with the most promising extracted features. The accuracy of the all DL models can further be enhanced by including the big dataset for predicting the prognosis of the disease.
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Abstract
Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are "black boxes." The initial response in the literature was a demand for "explainable AI." However, recently, several authors have suggested that making AI more explainable or "interpretable" is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a "lethal prejudice." In this paper, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to-and perhaps even harm-patients.
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Affiliation(s)
- Joshua Hatherley
- School of Philosophical, Historical, and International Studies, Monash University, Clayton, Victoria, Australia
| | - Robert Sparrow
- School of Philosophical, Historical, and International Studies, Monash University, Clayton, Victoria, Australia
| | - Mark Howard
- School of Philosophical, Historical, and International Studies, Monash University, Clayton, Victoria, Australia
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Trends and features of autism spectrum disorder research using artificial intelligence techniques: a bibliometric approach. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-03977-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Hatherley J, Sparrow R, Howard M. The Virtues of Interpretable Medical Artificial Intelligence. Camb Q Healthc Ethics 2022:1-10. [PMID: 36524245 DOI: 10.1017/s0963180122000305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are "black boxes." The initial response in the literature was a demand for "explainable AI." However, recently, several authors have suggested that making AI more explainable or "interpretable" is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a "lethal prejudice." In this article, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to-and perhaps even harm-patients.
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Affiliation(s)
- Joshua Hatherley
- School of Philosophical, Historical, and International Studies, Monash University, Clayton, Victoria3168, Australia
| | - Robert Sparrow
- School of Philosophical, Historical, and International Studies, Monash University, Clayton, Victoria3168, Australia
| | - Mark Howard
- School of Philosophical, Historical, and International Studies, Monash University, Clayton, Victoria3168, Australia
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Ramirez-Valdez EA, Leong C, Wu F, Ball S, Maistrello G, Martin G, Fritz Z. Towards cataloguing and characterising advance care planning and end-of-life care resources. BMC Palliat Care 2022; 21:211. [PMID: 36447187 PMCID: PMC9706845 DOI: 10.1186/s12904-022-01102-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 11/11/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Resources for healthcare professionals, patients and those important to them relating to planning and coordinating treatment and care at the end of life are abundant, and can be difficult to navigate. However, they have not been systematically collated or catalogued in terms of their purpose, scope or intended audience. AIM To collate, categorise and characterise advance care planning and end-of-life treatment and care (EoLT + C) resources directed towards healthcare professionals, patients and their families. METHODS Rapid review and thematic synthesis of resources available in the United Kingdom. Google searches and reviews of websites belonging to selected organisations that develop and publish materials relating to EoLT + C, and advance care planning were used. Materials were included if they were intended for those over 18 living in the UK and pertained to five domains of EoLT + C: identifying those approaching end of life; accessing EoLT + C services; conducting important conversations about EoLT + C and preferences; advance care planning, including recording of preferences and plans; and ensuring that plans and preferences are accessed and used by health and social care services. RESULTS 246 resources directed at healthcare professionals, patients and their families were identified, collated, catalogued and made internationally available for clinicians, researchers, patients and the public. 61 were classified as interactive, providing decision support in EoLT + C that went beyond simply providing information. Of these, there was notable content overlap among tools for identifying patients in their last year of life. There was variation in the development of tools across all domains of end-of-life care by geography and patient group. Few interactive resources integrated seamlessly with a digital interface or healthcare provider workflows. Incentives for the adoption of best-practice appeared rare. CONCLUSIONS We present a repeatable and scalable approach to the cataloguing and characterisation of palliative care resources. The identified resources will be of benefit not only to those in the UK but to those in other countries, developing or evaluating their own resources for aiding professionals and patients to plan and deliver excellent treatment and care at the end of life.
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Affiliation(s)
- Edric Aram Ramirez-Valdez
- School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, Cambridge, CB2 0SP, UK.
| | - Clare Leong
- The Healthcare Improvement Studies Institute, THIS Institute, University of Cambridge, Cambridge Biomedical Campus, Clifford Allbutt Building, Cambridge, CB2 0AH, UK
| | - Frances Wu
- The Healthcare Improvement Studies Institute, THIS Institute, University of Cambridge, Cambridge Biomedical Campus, Clifford Allbutt Building, Cambridge, CB2 0AH, UK
| | | | | | - Graham Martin
- The Healthcare Improvement Studies Institute, THIS Institute, University of Cambridge, Cambridge Biomedical Campus, Clifford Allbutt Building, Cambridge, CB2 0AH, UK
| | - Zoë Fritz
- The Healthcare Improvement Studies Institute, THIS Institute, University of Cambridge, Cambridge Biomedical Campus, Clifford Allbutt Building, Cambridge, CB2 0AH, UK
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Post B, Badea C, Faisal A, Brett SJ. Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus. AI AND ETHICS 2022; 3:1-14. [PMID: 36338525 PMCID: PMC9628590 DOI: 10.1007/s43681-022-00230-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/12/2022] [Indexed: 11/05/2022]
Abstract
An appropriate ethical framework around the use of Artificial Intelligence (AI) in healthcare has become a key desirable with the increasingly widespread deployment of this technology. Advances in AI hold the promise of improving the precision of outcome prediction at the level of the individual. However, the addition of these technologies to patient-clinician interactions, as with any complex human interaction, has potential pitfalls. While physicians have always had to carefully consider the ethical background and implications of their actions, detailed deliberations around fast-moving technological progress may not have kept up. We use a common but key challenge in healthcare interactions, the disclosure of bad news (likely imminent death), to illustrate how the philosophical framework of the 'Felicific Calculus' developed in the eighteenth century by Jeremy Bentham, may have a timely quasi-quantitative application in the age of AI. We show how this ethical algorithm can be used to assess, across seven mutually exclusive and exhaustive domains, whether an AI-supported action can be morally justified.
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Affiliation(s)
- Benjamin Post
- Department of Bioengineering, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
- UKRI Centre in AI for Healthcare, Imperial College London, London, UK
| | - Cosmin Badea
- Department of Computing, Imperial College London, London, UK
| | - Aldo Faisal
- Department of Bioengineering, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
- UKRI Centre in AI for Healthcare, Imperial College London, London, UK
- Institute of Artificial and Human Intelligence, University of Bayreuth, Bayreuth, Germany
| | - Stephen J. Brett
- UKRI Centre in AI for Healthcare, Imperial College London, London, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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Hahn W, Schütte K, Schultz K, Wolkenhauer O, Sedlmayr M, Schuler U, Eichler M, Bej S, Wolfien M. Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care. J Pers Med 2022; 12:1278. [PMID: 36013227 PMCID: PMC9409663 DOI: 10.3390/jpm12081278] [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: 07/04/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 11/23/2022] Open
Abstract
AI model development for synthetic data generation to improve Machine Learning (ML) methodologies is an integral part of research in Computer Science and is currently being transferred to related medical fields, such as Systems Medicine and Medical Informatics. In general, the idea of personalized decision-making support based on patient data has driven the motivation of researchers in the medical domain for more than a decade, but the overall sparsity and scarcity of data are still major limitations. This is in contrast to currently applied technology that allows us to generate and analyze patient data in diverse forms, such as tabular data on health records, medical images, genomics data, or even audio and video. One solution arising to overcome these data limitations in relation to medical records is the synthetic generation of tabular data based on real world data. Consequently, ML-assisted decision-support can be interpreted more conveniently, using more relevant patient data at hand. At a methodological level, several state-of-the-art ML algorithms generate and derive decisions from such data. However, there remain key issues that hinder a broad practical implementation in real-life clinical settings. In this review, we will give for the first time insights towards current perspectives and potential impacts of using synthetic data generation in palliative care screening because it is a challenging prime example of highly individualized, sparsely available patient information. Taken together, the reader will obtain initial starting points and suitable solutions relevant for generating and using synthetic data for ML-based screenings in palliative care and beyond.
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Affiliation(s)
- Waldemar Hahn
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany
| | - Katharina Schütte
- University Palliative Center, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany
| | - Kristian Schultz
- Department of Systems Biology and Bioinformatics, University of Rostock, Universitätsplatz 1, 18051 Rostock, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Universitätsplatz 1, 18051 Rostock, Germany
- Leibniz-Institute for Food Systems Biology, Technical University Munich, 85354 Freising, Germany
- Stellenbosch Institute of Advanced Study, Wallenberg Research Centre, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany
| | - Ulrich Schuler
- University Palliative Center, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany
| | - Martin Eichler
- National Center for Tumor Diseases Dresden (NCT/UCC), Fetscherstraße 74, 01307 Dresden, Germany
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany
- Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Bautzner Landstraße 400, 01328 Dresden, Germany
| | - Saptarshi Bej
- Department of Systems Biology and Bioinformatics, University of Rostock, Universitätsplatz 1, 18051 Rostock, Germany
- Leibniz-Institute for Food Systems Biology, Technical University Munich, 85354 Freising, Germany
| | - Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, Germany
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Zhang N, Mattsson O. Identification of palliative care needs at the end of life for dementia patients can decrease acute hospital care needs and admissions. Evid Based Nurs 2022; 25:86. [PMID: 35046067 DOI: 10.1136/ebnurs-2021-103458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Affiliation(s)
- Nicole Zhang
- The Valley Foundation School of Nursing, San Jose State University, San Jose, California, USA
| | - Odessa Mattsson
- The Valley Foundation School of Nursing, San Jose State University, San Jose, California, USA
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Predicting mortality in The Irish Longitudinal Study on Ageing (TILDA): development of a four-year index and comparison with international measures. BMC Geriatr 2022; 22:510. [PMID: 35729488 PMCID: PMC9211047 DOI: 10.1186/s12877-022-03196-z] [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: 11/17/2021] [Accepted: 06/03/2022] [Indexed: 11/28/2022] Open
Abstract
Objectives We aimed to replicate existing international (US and UK) mortality indices using Irish data. We developed and validated a four-year mortality index for adults aged 50 + in Ireland and compared performance with these international indices. We then extended this model by including additional predictors (self-report and healthcare utilization) and compared its performance to our replication model. Methods Eight thousand one hundred seventy-four participants in The Irish Longitudinal Study on Ageing were split for development (n = 4,121) and validation (n = 4,053). Six baseline predictor categories were examined (67 variables total): demographics; cardiovascular-related illness; non-cardiovascular illness; health and lifestyle variables; functional variables; self-report (wellbeing and social connectedness) and healthcare utilization. We identified variables independently associated with four-year mortality in the development cohort and attached these variables a weight according to strength of association. We summed the weights to calculate a single index score for each participant and evaluated predicted accuracy in the validation cohort. Results Our final 14-predictor (extended) model assigned risk points for: male (1pt); age (65–69: 2pts; 70–74: 4 pts; 75–79: 4pts; 80–84: 6pts; 85 + : 7pts); heart attack (1pt); cancer (3pts); smoked past age 30 (2pts); difficulty walking 100 m (2pts); difficulty using the toilet (3pts); difficulty lifting 10lbs (1pts); poor self-reported health (1pt); and hospital admission in previous year (1pt). Index discrimination was strong (ROC area = 0.78). Discussion Our index is predictive of four-year mortality in community-dwelling older Irish adults. Comparisons with the international indices show that our 12-predictor (replication) model performed well and suggests that generalisability is high. Our 14-predictor (extended) model showed modest improvements compared to the 12-predictor model. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-022-03196-z.
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Random Noise vs. State-of-the-Art Probabilistic Forecasting Methods: A Case Study on CRPS-Sum Discrimination Ability. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The recent developments in the machine-learning domain have enabled the development of complex multivariate probabilistic forecasting models. To evaluate the predictive power of these complex methods, it is pivotal to have a precise evaluation method to gauge the performance and predictability power of these complex methods. To do so, several evaluation metrics have been proposed in the past (such as the energy score, Dawid–Sebastiani score, and variogram score); however, these cannot reliably measure the performance of a probabilistic forecaster. Recently, CRPS-Sum has gained a lot of prominence as a reliable metric for multivariate probabilistic forecasting. This paper presents a systematic evaluation of CRPS-Sum to understand its discrimination ability. We show that the statistical properties of target data affect the discrimination ability of CRPS-Sum. Furthermore, we highlight that CRPS-Sum calculation overlooks the performance of the model on each dimension. These flaws can lead us to an incorrect assessment of model performance. Finally, with experiments on real-world datasets, we demonstrate that the shortcomings of CRPS-Sum provide a misleading indication of the probabilistic forecasting performance method. We illustrate that it is easily possible to have a better CRPS-Sum for a dummy model, which looks like random noise, in comparison to the state-of-the-art method.
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Zachariah FJ, Rossi LA, Roberts LM, Bosserman LD. Prospective Comparison of Medical Oncologists and a Machine Learning Model to Predict 3-Month Mortality in Patients With Metastatic Solid Tumors. JAMA Netw Open 2022; 5:e2214514. [PMID: 35639380 PMCID: PMC9157269 DOI: 10.1001/jamanetworkopen.2022.14514] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/24/2022] [Indexed: 12/29/2022] Open
Abstract
Importance To date, oncologist and model prognostic performance have been assessed independently and mostly retrospectively; however, how model prognostic performance compares with oncologist prognostic performance prospectively remains unknown. Objective To compare oncologist performance with a model in predicting 3-month mortality for patients with metastatic solid tumors in an outpatient setting. Design, Setting, and Participants This prognostic study evaluated prospective predictions for a cohort of patients with metastatic solid tumors seen in outpatient oncology clinics at a National Cancer Institute-designated cancer center and associated satellites between December 6, 2019, and August 6, 2021. Oncologists (57 physicians and 17 advanced practice clinicians) answered a 3-month surprise question (3MSQ) within clinical pathways. A model was trained with electronic health record data from January 1, 2013, to April 24, 2019, to identify patients at high risk of 3-month mortality and deployed silently in October 2019. Analysis was limited to oncologist prognostications with a model prediction within the preceding 30 days. Exposures Three-month surprise question and gradient-boosting binary classifier. Main Outcomes and Measures The primary outcome was performance comparison between oncologists and the model to predict 3-month mortality. The primary performance metric was the positive predictive value (PPV) at the sensitivity achieved by the medical oncologists with their 3MSQ answers. Results A total of 74 oncologists answered 3099 3MSQs for 2041 patients with advanced cancer (median age, 62.6 [range, 18-96] years; 1271 women [62.3%]). In this cohort with a 15% prevalence of 3-month mortality and 30% sensitivity for both oncologists and the model, the PPV of oncologists was 34.8% (95% CI, 30.1%-39.5%) and the PPV of the model was 60.0% (95% CI, 53.6%-66.3%). Area under the receiver operating characteristic curve for the model was 81.2% (95% CI, 79.1%-83.3%). The model significantly outperformed the oncologists in short-term mortality. Conclusions and Relevance In this prognostic study, the model outperformed oncologists overall and within the breast and gastrointestinal cancer cohorts in predicting 3-month mortality for patients with advanced cancer. These findings suggest that further studies may be useful to examine how model predictions could improve oncologists' prognostic confidence and patient-centered goal-concordant care at the end of life.
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Affiliation(s)
- Finly J. Zachariah
- Department of Supportive Care Medicine, City of Hope National Medical Center, Duarte, California
| | - Lorenzo A. Rossi
- Department of Applied AI and Data Science, City of Hope National Medical Center, Duarte, California
| | - Laura M. Roberts
- Department of Clinical Informatics, City of Hope National Medical Center, Duarte, California
| | - Linda D. Bosserman
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, California
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Sandham MH, Hedgecock EA, Siegert RJ, Narayanan A, Hocaoglu MB, Higginson IJ. Intelligent Palliative Care Based on Patient-Reported Outcome Measures. J Pain Symptom Manage 2022; 63:747-757. [PMID: 35026384 DOI: 10.1016/j.jpainsymman.2021.11.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/11/2021] [Accepted: 11/16/2021] [Indexed: 12/31/2022]
Abstract
CONTEXT The growth of patient reported outcome measures data in palliative care provides an opportunity for machine learning to identify patterns in patient responses signifying different phases of illness. OBJECTIVES The study will explore if machine learning and network analysis can identify phases in patient palliative status through symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS). METHODS A partly cross-sectional and partially longitudinal observational study was undertaken using the Australasian Karnofsky Performance Scale (AKPS); Integrated Palliative Care Outcome Scale (IPOS); Phase of Illness (POI). Patient palliative records (n = 1507, 65% stable, 20% unstable, 9% deteriorating, 2% terminal) from 804 adult patients enrolled in a New Zealand palliative care service were analysed using a combination of statistical, machine learning and network analysis techniques. RESULTS Data from IPOS showed considerable variation with phase. Also, network analysis showed clear associations between items by phase. Six machine learning techniques identified the most important variables for predicting possible transition between phases of illness. Network analysis for all patients showed that Poor Appetite and Loss of Energy were central IPOS items, with Loss of Energy linked to Drowsiness, Shortness of Breath and Lack of Mobility on the one hand, and Poor Appetite linked to Nausea, Vomiting, Constipation and Sore and Dry Mouth on the other. CONCLUSION These preliminary results, when coupled with the latest technological developments in mobile apps and wearable technology, could point the way to increased use of digital therapeutics in continuous palliative care monitoring.
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Affiliation(s)
- Margaret H Sandham
- School of Clinical Sciences (M.S., R.S.), Auckland University of Technology, Auckland, New Zealand.
| | - Emma A Hedgecock
- Specialty Medicine and Health of Older People, Waitemata District Health Board, Private Bag (E.A.H.), Takapuna, New Zealand
| | - Richard J Siegert
- School of Clinical Sciences (M.S., R.S.), Auckland University of Technology, Auckland, New Zealand
| | - Ajit Narayanan
- School of Engineering, Computer and Mathematical Sciences (A.N.), Auckland University of Technology, Auckland, New Zealand
| | - Mevhibe B Hocaoglu
- Cicely Saunders Institute of Palliative Care, Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care (M.B.H., I.J.H.), King's College London, London, UK
| | - Irene J Higginson
- Cicely Saunders Institute of Palliative Care, Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care (M.B.H., I.J.H.), King's College London, London, UK
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Chi S, Guo A, Heard K, Kim S, Foraker R, White P, Moore N. Development and Structure of an Accurate Machine Learning Algorithm to Predict Inpatient Mortality and Hospice Outcomes in the Coronavirus Disease 2019 Era. Med Care 2022; 60:381-386. [PMID: 35230273 PMCID: PMC8989608 DOI: 10.1097/mlr.0000000000001699] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has challenged the accuracy and racial biases present in traditional mortality scores. An accurate prognostic model that can be applied to hospitalized patients irrespective of race or COVID-19 status may benefit patient care. RESEARCH DESIGN This cohort study utilized historical and ongoing electronic health record features to develop and validate a deep-learning model applied on the second day of admission predicting a composite outcome of in-hospital mortality, discharge to hospice, or death within 30 days of admission. Model features included patient demographics, diagnoses, procedures, inpatient medications, laboratory values, vital signs, and substance use history. Conventional performance metrics were assessed, and subgroup analysis was performed based on race, COVID-19 status, and intensive care unit admission. SUBJECTS A total of 35,521 patients hospitalized between April 2020 and October 2020 at a single health care system including a tertiary academic referral center and 9 community hospitals. RESULTS Of 35,521 patients, including 9831 non-White patients and 2020 COVID-19 patients, 2838 (8.0%) met the composite outcome. Patients who experienced the composite outcome were older (73 vs. 61 y old) with similar sex and race distributions between groups. The model achieved an area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.88, 0.91) and an average positive predictive value of 0.46 (0.40, 0.52). Model performance did not differ significantly in White (0.89) and non-White (0.90) subgroups or when grouping by COVID-19 status and intensive care unit admission. CONCLUSION A deep-learning model using large-volume, structured electronic health record data can effectively predict short-term mortality or hospice outcomes on the second day of admission in the general inpatient population without significant racial bias.
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Affiliation(s)
- Stephen Chi
- Division of Pulmonary and Critical Care Medicine
| | - Aixia Guo
- Institute for Informatics, Washington University in St. Louis
| | | | - Seunghwan Kim
- Division of General Medical Sciences, School of Medicine, Washington University in St. Louis
| | - Randi Foraker
- Institute for Informatics, Washington University in St. Louis
| | - Patrick White
- Division of Palliative Medicine, Department of Medicine, Washington University in St. Louis
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Blanes-Selva V, Doñate-Martínez A, Linklater G, García-Gómez JM. Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs. Health Informatics J 2022; 28:14604582221092592. [PMID: 35642719 DOI: 10.1177/14604582221092592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Bad survival prognosis and patients' decline are working criteria to guide PC decision-making for older patients. Still, there is not a clear consensus on when to initiate early PC. This work aims to propose machine learning approaches to predict frailty and mortality in older patients in supporting PC decision-making. Predictive models based on Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) were implemented for binary 1-year mortality classification, survival estimation and 1-year frailty classification. Besides, we tested the similarity between mortality and frailty distributions. The 1-year mortality classifier achieved an Area Under the Curve Receiver Operating Characteristic (AUC ROC) of 0.87 [0.86, 0.87], whereas the mortality regression model achieved an mean absolute error (MAE) of 333.13 [323.10, 342.49] days. Moreover, the 1-year frailty classifier obtained an AUC ROC of 0.89 [0.88, 0.90]. Mortality and frailty criteria were weakly correlated and had different distributions, which can be interpreted as these assessment measurements are complementary for PC decision-making. This study provides new models that can be part of decision-making systems for PC services in older patients after their external validation.
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Affiliation(s)
- Vicent Blanes-Selva
- Biomedical Data Science Lab, Instituto Universitarios de Tecnologías de La Información y Comunicaciones (ITACA), Universitat Politècnica de València, Valencia, Spain
| | | | | | - Juan M García-Gómez
- Biomedical Data Science Lab, Instituto Universitarios de Tecnologías de La Información y Comunicaciones (ITACA), Universitat Politècnica de València, Valencia, Spain
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Stone K, Zwiggelaar R, Jones P, Mac Parthaláin N. A systematic review of the prediction of hospital length of stay: Towards a unified framework. PLOS DIGITAL HEALTH 2022; 1:e0000017. [PMID: 36812502 PMCID: PMC9931263 DOI: 10.1371/journal.pdig.0000017] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 01/06/2022] [Indexed: 05/09/2023]
Abstract
Hospital length of stay of patients is a crucial factor for the effective planning and management of hospital resources. There is considerable interest in predicting the LoS of patients in order to improve patient care, control hospital costs and increase service efficiency. This paper presents an extensive review of the literature, examining the approaches employed for the prediction of LoS in terms of their merits and shortcomings. In order to address some of these problems, a unified framework is proposed to better generalise the approaches that are being used to predict length of stay. This includes the investigation of the types of routinely collected data used in the problem as well as recommendations to ensure robust and meaningful knowledge modelling. This unified common framework enables the direct comparison of results between length of stay prediction approaches and will ensure that such approaches can be used across several hospital environments. A literature search was conducted in PubMed, Google Scholar and Web of Science from 1970 until 2019 to identify LoS surveys which review the literature. 32 Surveys were identified, from these 32 surveys, 220 papers were manually identified to be relevant to LoS prediction. After removing duplicates, and exploring the reference list of studies included for review, 93 studies remained. Despite the continuing efforts to predict and reduce the LoS of patients, current research in this domain remains ad-hoc; as such, the model tuning and data preprocessing steps are too specific and result in a large proportion of the current prediction mechanisms being restricted to the hospital that they were employed in. Adopting a unified framework for the prediction of LoS could yield a more reliable estimate of the LoS as a unified framework enables the direct comparison of length of stay methods. Additional research is also required to explore novel methods such as fuzzy systems which could build upon the success of current models as well as further exploration of black-box approaches and model interpretability.
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Affiliation(s)
- Kieran Stone
- Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3DB, Wales, United Kingdom
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3DB, Wales, United Kingdom
| | - Phil Jones
- Bronglais District General Hospital, Aberystwyth, Ceredigion, SY23 1ER, Wales, United Kingdom
| | - Neil Mac Parthaláin
- Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3DB, Wales, United Kingdom
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Nwosu AC, McGlinchey T, Sanders J, Stanley S, Palfrey J, Lubbers P, Chapman L, Finucane A, Mason S. Identification of Digital Health Priorities for Palliative Care Research: Modified Delphi Study. JMIR Aging 2022; 5:e32075. [PMID: 35311674 PMCID: PMC9090235 DOI: 10.2196/32075] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/09/2021] [Accepted: 12/02/2021] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Developments in digital health have the potential to transform the delivery of health and social care to help citizens manage their health. Currently, there is a lack of consensus about digital health research priorities in palliative care and a lack of theories about how these technologies might improve care outcomes. Therefore, it is important for health care leaders to identify innovations to ensure that an increasingly frail population has appropriate access to palliative care services. Consequently, it is important to articulate research priorities as the first step in determining how finite resources should be allocated to a field saturated with rapidly developing innovation. OBJECTIVE The aim of this study is to identify research priority areas for digital health in palliative care. METHODS We selected digital health trends, most relevant to palliative care, from a list of emerging trends reported by a leading institute of quantitative futurists. We conducted 2 rounds of the Delphi questionnaire, followed by a consensus meeting and public engagement workshop to establish a final consensus on research priorities for digital technology in palliative care. We used the views of public representatives to gain their perspectives on the agreed priorities. RESULTS A total of 103 experts (representing 11 countries) participated in the first Delphi round. Of the 103 experts, 55 (53.3%) participated in the second round. The final consensus meetings were attended by 10.7% (11/103) of the experts. We identified 16 priority areas, which involved many applications of technologies, including care for patients and caregivers, self-management and reporting of diseases, education and training, communication, care coordination, and research methodology. We summarized the priority areas into eight topics: big data, mobile devices, telehealth and telemedicine, virtual reality, artificial intelligence, smart home, biotechnology, and digital legacy. CONCLUSIONS The priorities identified in this study represent a wide range of important emerging areas in the fields of digital health, personalized medicine, and data science. Human-centered design and robust governance systems should be considered in future research. It is important that the risks of using these technologies in palliative care are properly addressed to ensure that these tools are used meaningfully, wisely, and safely and do not cause unintentional harm.
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Affiliation(s)
- Amara Callistus Nwosu
- Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
- Marie Curie Hospice Liverpool, Liverpool, United Kingdom
- Liverpool University Hospitals National Health Service Foundation Trust, Liverpool, United Kingdom
| | - Tamsin McGlinchey
- Palliative Care Unit, University of Liverpool, Liverpool, United Kingdom
| | - Justin Sanders
- Dana-Farber Cancer Institute, Boston, MA, United States
- Ariadne Labs, Brigham and Women's Hospital and Harvard T H Chan School of Public Health, Boston, MA, United States
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - Sarah Stanley
- Marie Curie Hospice Liverpool, Liverpool, United Kingdom
| | | | - Patrick Lubbers
- Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, Netherlands
| | - Laura Chapman
- Marie Curie Hospice Liverpool, Liverpool, United Kingdom
| | - Anne Finucane
- Clinical Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen Mason
- Palliative Care Unit, University of Liverpool, Liverpool, United Kingdom
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