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Singh B, Jevnikar AM, Desjardins E. Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities. Arch Immunol Ther Exp (Warsz) 2024; 72:aite-2024-0006. [PMID: 38421272 DOI: 10.2478/aite-2024-0006] [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: 12/14/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.
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Affiliation(s)
- Bhagirath Singh
- Department of Microbiology and Immunology, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Rotman Institute of Philosophy, University of Western Ontario, London, ON, Canada
| | - Anthony M Jevnikar
- Department of Microbiology and Immunology, University of Western Ontario, London, ON, Canada
- Department of Medicine, University of Western Ontario, London, ON, Canada
| | - Eric Desjardins
- Rotman Institute of Philosophy, University of Western Ontario, London, ON, Canada
- Department of Philosophy, University of Western Ontario, London, ON, Canada
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2
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Han C, Pan Y, Liu C, Yang X, Li J, Wang K, Sun Z, Liu H, Jin G, Fang F, Pan X, Tang T, Chen X, Pang S, Ma L, Wang X, Ren Y, Liu M, Liu F, Jiang M, Zhao J, Lu C, Lu Z, Gao D, Jiang Z, Pei J. Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment. Front Oncol 2023; 13:1152013. [PMID: 37361565 PMCID: PMC10289408 DOI: 10.3389/fonc.2023.1152013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/26/2023] [Indexed: 06/28/2023] Open
Abstract
Background AI-based clinical decision support system (CDSS) has important prospects in overcoming the current informational challenges that cancer diseases faced, promoting the homogeneous development of standardized treatment among different geographical regions, and reforming the medical model. However, there are still a lack of relevant indicators to comprehensively assess its decision-making quality and clinical impact, which greatly limits the development of its clinical research and clinical application. This study aims to develop and application an assessment system that can comprehensively assess the decision-making quality and clinical impacts of physicians and CDSS. Methods Enrolled adjuvant treatment decision stage early breast cancer cases were randomly assigned to different decision-making physician panels (each panel consisted of three different seniority physicians in different grades hospitals), each physician made an independent "Initial Decision" and then reviewed the CDSS report online and made a "Final Decision". In addition, the CDSS and guideline expert groups independently review all cases and generate "CDSS Recommendations" and "Guideline Recommendations" respectively. Based on the design framework, a multi-level multi-indicator system including "Decision Concordance", "Calibrated Concordance", " Decision Concordance with High-level Physician", "Consensus Rate", "Decision Stability", "Guideline Conformity", and "Calibrated Conformity" were constructed. Results 531 cases containing 2124 decision points were enrolled; 27 different seniority physicians from 10 different grades hospitals have generated 6372 decision opinions before and after referring to the "CDSS Recommendations" report respectively. Overall, the calibrated decision concordance was significantly higher for CDSS and provincial-senior physicians (80.9%) than other physicians. At the same time, CDSS has a higher " decision concordance with high-level physician" (76.3%-91.5%) than all physicians. The CDSS had significantly higher guideline conformity than all decision-making physicians and less internal variation, with an overall guideline conformity variance of 17.5% (97.5% vs. 80.0%), a standard deviation variance of 6.6% (1.3% vs. 7.9%), and a mean difference variance of 7.8% (1.5% vs. 9.3%). In addition, provincial-middle seniority physicians had the highest decision stability (54.5%). The overall consensus rate among physicians was 64.2%. Conclusions There are significant internal variation in the standardization treatment level of different seniority physicians in different geographical regions in the adjuvant treatment of early breast cancer. CDSS has a higher standardization treatment level than all physicians and has the potential to provide immediate decision support to physicians and have a positive impact on standardizing physicians' treatment behaviors.
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Affiliation(s)
- Chunguang Han
- Department of Pediatric Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yubo Pan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chang Liu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaowei Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jianbin Li
- Department of Breast Cancer, Fifth Medical Center, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Kun Wang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhengkui Sun
- Department of Breast Oncology Surgery, Jiangxi Cancer Hospital (The Second People's Hospital of Jiangxi Province), Nanchang, China
| | - Hui Liu
- Department of Breast Surgery, Henan Provincial People's Hospital, Zhengzhou, China
| | - Gongsheng Jin
- Department of Oncological Surgery, the First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Fang Fang
- Department of Thyroid and Breast surgery, the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhhu, China
| | - Xiaofeng Pan
- Department of Thyroid and Breast surgery, the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhhu, China
| | - Tong Tang
- Department of General Surgury, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiao Chen
- Department of General Surgury, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shiyong Pang
- Department of General Surgery, Lu'an People's Hospital of Anhui Province (Lu'an Hospital of Anhui Medical University), Lu'an, China
| | - Li Ma
- Department of Thyroid and Breast Surgery, Anqing Municipal Hospital (Anqing Hospital Affiliated to Anhui Medical University), Anqing, China
| | - Xiaodong Wang
- Department of Thyroid and Breast Surgery, The people's hospital of Bozhou (Bozhou Hospital Affiliated to Anhui Medical University), Bozhou, China
| | - Yun Ren
- Department of Thyroid and Breast surgery, Department of Oncological Surgery, Taihe county people's hospital (The Taihe hospital of Wannan Medical College), Fuyang, China
| | - Mengyou Liu
- Department of Thyroid and Breast surgery, Lixin County People's Hospital, Bozhou, China
| | - Feng Liu
- Department of Breast Surgery, Fuyang Cancer Hospital, Fuyang, China
| | - Mengxue Jiang
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jiqi Zhao
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chenyang Lu
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhengdong Lu
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Dongjing Gao
- Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zefei Jiang
- Department of Breast Cancer, Fifth Medical Center, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Jing Pei
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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3
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Niraula D, Sun W, Jin J, Dinov ID, Cuneo K, Jamaluddin J, Matuszak MM, Luo Y, Lawrence TS, Jolly S, Ten Haken RK, El Naqa I. A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS). Sci Rep 2023; 13:5279. [PMID: 37002296 PMCID: PMC10066294 DOI: 10.1038/s41598-023-32032-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/21/2023] [Indexed: 04/03/2023] Open
Abstract
Involvement of many variables, uncertainty in treatment response, and inter-patient heterogeneity challenge objective decision-making in dynamic treatment regime (DTR) in oncology. Advanced machine learning analytics in conjunction with information-rich dense multi-omics data have the ability to overcome such challenges. We have developed a comprehensive artificial intelligence (AI)-based optimal decision-making framework for assisting oncologists in DTR. In this work, we demonstrate the proposed framework to Knowledge Based Response-Adaptive Radiotherapy (KBR-ART) applications by developing an interactive software tool entitled Adaptive Radiotherapy Clinical Decision Support (ARCliDS). ARCliDS is composed of two main components: Artifcial RT Environment (ARTE) and Optimal Decision Maker (ODM). ARTE is designed as a Markov decision process and modeled via supervised learning. Given a patient's pre- and during-treatment information, ARTE can estimate treatment outcomes for a selected daily dosage value (radiation fraction size). ODM is formulated using reinforcement learning and is trained on ARTE. ODM can recommend optimal daily dosage adjustments to maximize the tumor local control probability and minimize the side effects. Graph Neural Networks (GNN) are applied to exploit the inter-feature relationships for improved modeling performance and a novel double GNN architecture is designed to avoid nonphysical treatment response. Datasets of size 117 and 292 were available from two clinical trials on adaptive RT in non-small cell lung cancer (NSCLC) patients and adaptive stereotactic body RT (SBRT) in hepatocellular carcinoma (HCC) patients, respectively. For training and validation, dense data with 297 features were available for 67 NSCLC patients and 110 features for 71 HCC patients. To increase the sample size for ODM training, we applied Generative Adversarial Networks to generate 10,000 synthetic patients. The ODM was trained on the synthetic patients and validated on the original dataset. We found that, Double GNN architecture was able to correct the nonphysical dose-response trend and improve ARCliDS recommendation. The average root mean squared difference (RMSD) between ARCliDS recommendation and reported clinical decisions using double GNNs were 0.61 [0.03] Gy/frac (mean [sem]) for adaptive RT in NSCLC patients and 2.96 [0.42] Gy/frac for adaptive SBRT HCC compared to the single GNN's RMSDs of 0.97 [0.12] Gy/frac and 4.75 [0.16] Gy/frac, respectively. Overall, For NSCLC and HCC, ARCliDS with double GNNs was able to reproduce 36% and 50% of the good clinical decisions (local control and no side effects) and improve 74% and 30% of the bad clinical decisions, respectively. In conclusion, ARCliDS is the first web-based software dedicated to assist KBR-ART with multi-omics data. ARCliDS can learn from the reported clinical decisions and facilitate AI-assisted clinical decision-making for improving the outcomes in DTR.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA.
| | - Wenbo Sun
- University of Michigan Transport Research Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jionghua Jin
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ivo D Dinov
- Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jamalina Jamaluddin
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
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4
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Sun W, Niraula D, El Naqa I, Ten Haken RK, Dinov ID, Cuneo K, Jin JJ. Precision radiotherapy via information integration of expert human knowledge and AI recommendation to optimize clinical decision making. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106927. [PMID: 35675722 PMCID: PMC11058561 DOI: 10.1016/j.cmpb.2022.106927] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 05/10/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
In the precision medicine era, there is a growing need for precision radiotherapy where the planned radiation dose needs to be optimally determined by considering a myriad of patient-specific information in order to ensure treatment efficacy. Existing artificial-intelligence (AI) methods can recommend radiation dose prescriptions within the scope of this available information. However, treating physicians may not fully entrust the AI's recommended prescriptions due to known limitations or at instances when the AI recommendation may go beyond physicians' current knowledge. This paper lays out a systematic method to integrate expert human knowledge with AI recommendations for optimizing clinical decision making. Towards this goal, Gaussian process (GP) models are integrated with deep neural networks (DNNs) to quantify the uncertainty of the treatment outcomes given by physicians and AI recommendations, respectively, which are further used as a guideline to educate clinical physicians and improve AI models performance. The proposed method is demonstrated in a comprehensive dataset where patient-specific information and treatment outcomes are prospectively collected during radiotherapy of 67 non-small cell lung cancer (NSCLC) patients and are retrospectively analyzed.
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Affiliation(s)
- Wenbo Sun
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA.
| | - Dipesh Niraula
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
| | | | - Ivo D Dinov
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA
| | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA.
| | - Judy Jionghua Jin
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA.
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5
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Abstract
Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.
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Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, 7984Ryerson University, Toronto, ON, Canada
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6
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Niraula D, Jamaluddin J, Matuszak MM, Haken RKT, Naqa IE. Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy. Sci Rep 2021; 11:23545. [PMID: 34876609 PMCID: PMC8651664 DOI: 10.1038/s41598-021-02910-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 11/24/2021] [Indexed: 01/31/2023] Open
Abstract
Subtle differences in a patient's genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient's dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients' specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
| | - Jamalina Jamaluddin
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Martha M Matuszak
- Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
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Chua IS, Gaziel-Yablowitz M, Korach ZT, Kehl KL, Levitan NA, Arriaga YE, Jackson GP, Bates DW, Hassett M. Artificial intelligence in oncology: Path to implementation. Cancer Med 2021; 10:4138-4149. [PMID: 33960708 PMCID: PMC8209596 DOI: 10.1002/cam4.3935] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer‐related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high‐value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration.
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Affiliation(s)
- Isaac S Chua
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Michal Gaziel-Yablowitz
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Zfania T Korach
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Kenneth L Kehl
- Harvard Medical School, Boston, MA, USA.,Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Gretchen P Jackson
- IBM Watson Health, Cambridge, MA, USA.,Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David W Bates
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Michael Hassett
- Harvard Medical School, Boston, MA, USA.,Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
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Herter WE, Khuc J, Cinà G, Knottnerus BJ, Numans ME, Wiewel MA, Bonten TN, de Bruin DP, van Esch T, Chavannes NH, Verheij RA. Impact of a machine learning based decision support for Urinary Tract Infections: Prospective observational study in 36 primary care practices (Preprint). JMIR Med Inform 2021; 10:e27795. [PMID: 35507396 PMCID: PMC9118012 DOI: 10.2196/27795] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/28/2021] [Accepted: 02/13/2022] [Indexed: 11/25/2022] Open
Abstract
Background There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice. We implemented a CDSS to aid general practitioners (GPs) in treating patients with urinary tract infections (UTIs), which are a significant health burden worldwide. Objective This study aims to prospectively assess the impact of this CDSS on treatment success and change in antibiotic prescription behavior of the physician. In doing so, we hope to identify drivers and obstacles that positively impact the quality of health care practice with ML. Methods The CDSS was developed by Pacmed, Nivel, and Leiden University Medical Center (LUMC). The CDSS presents the expected outcomes of treatments, using interpretable decision trees as ML classifiers. Treatment success was defined as a subsequent period of 28 days during which no new antibiotic treatment for UTI was needed. In this prospective observational study, 36 primary care practices used the software for 4 months. Furthermore, 29 control practices were identified using propensity score-matching. All analyses were performed using electronic health records from the Nivel Primary Care Database. Patients for whom the software was used were identified in the Nivel database by sequential matching using CDSS use data. We compared the proportion of successful treatments before and during the study within the treatment arm. The same analysis was performed for the control practices and the patient subgroup the software was definitely used for. All analyses, including that of physicians’ prescription behavior, were statistically tested using 2-sided z tests with an α level of .05. Results In the treatment practices, 4998 observations were included before and 3422 observations (of 2423 unique patients) were included during the implementation period. In the control practices, 5044 observations were included before and 3360 observations were included during the implementation period. The proportion of successful treatments increased significantly from 75% to 80% in treatment practices (z=5.47, P<.001). No significant difference was detected in control practices (76% before and 76% during the pilot, z=0.02; P=.98). Of the 2423 patients, we identified 734 (30.29%) in the CDSS use database in the Nivel database. For these patients, the proportion of successful treatments during the study was 83%—a statistically significant difference, with 75% of successful treatments before the study in the treatment practices (z=4.95; P<.001). Conclusions The introduction of the CDSS as an intervention in the 36 treatment practices was associated with a statistically significant improvement in treatment success. We excluded temporal effects and validated the results with the subgroup analysis in patients for whom we were certain that the software was used. This study shows important strengths and points of attention for the development and implementation of an ML-based CDSS in clinical practice. Trial Registration ClinicalTrials.gov NCT04408976; https://clinicaltrials.gov/ct2/show/NCT04408976
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Affiliation(s)
- Willem Ernst Herter
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
- Pacmed, Amsterdam, Netherlands
| | | | | | - Bart J Knottnerus
- Nivel Netherlands Institute for Health Services Research, Utrecht, Netherlands
| | - Mattijs E Numans
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Maryse A Wiewel
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
- Pacmed, Amsterdam, Netherlands
| | - Tobias N Bonten
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | | | - Thamar van Esch
- Nivel Netherlands Institute for Health Services Research, Utrecht, Netherlands
| | - Niels H Chavannes
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Robert A Verheij
- Nivel Netherlands Institute for Health Services Research, Utrecht, Netherlands
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9
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Walsh K, Wroe C. Clinical decision support: what will happen in the 2020s? Postgrad Med J 2020; 97:72-74. [PMID: 33060190 DOI: 10.1136/postgradmedj-2020-137658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 09/10/2020] [Accepted: 09/30/2020] [Indexed: 11/04/2022]
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10
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Iseli T, Fischer GF, Panje CM, Glatzer M, Hundsberger T, Rothermundt C, Schmidt B, Sirén C, Plasswilm L, Putora PM. Insular Decision Criteria in Clinical Practice: Analysis of Decision-Making in Oncology. Oncology 2020; 98:438-444. [DOI: 10.1159/000508132] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 04/24/2020] [Indexed: 11/19/2022]
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11
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Global updates in the treatment of gastric cancer: a systematic review. Part 2: perioperative management, multimodal therapies, new technologies, standardization of the surgical treatment and educational aspects. Updates Surg 2020; 72:355-378. [PMID: 32306277 DOI: 10.1007/s13304-020-00771-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/11/2020] [Indexed: 12/24/2022]
Abstract
Gastric cancer is the fifth malignancy and the third cause of cancer death worldwide, according to the global cancer statistics presented in 2018. Its definition and staging have been revised in the eight edition of the AJCC/TNM classification, which took effect in 2018. Novel molecular classifications for GC have been recently established and the process of translating these classifications into clinical practice is ongoing. The cornerstone of GC treatment is surgical, in a context of multimodal therapy. Surgical treatment is being standardized, and is evolving according to new anatomical concepts and to the recent technological developments. This is leading to a massive improvement in the use of mini-invasive techniques. Mini-invasive techniques aim to be equivalent to open surgery from an oncologic point of view, with better short-term outcomes. The persecution of better short-term outcomes also includes the optimization of the perioperative management, which is being implemented on large scale according to the enhanced recovery after surgery principles. In the era of precision medicine, multimodal treatment is also evolving. The long-time-awaited results of many trials investigating the role for preoperative and postoperative management have been published, changing the clinical practice. Novel investigations focused both on traditional chemotherapeutic regimens and targeted therapies are currently ongoing. Modern platforms increase the possibility for further standardization of the different treatments, promote the use of big data and open new possibilities for surgical learning. This systematic review in two parts assesses all the current updates in GC treatment.
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Treffers T, Putora PM. Emotions as Social Information in Shared Decision-Making in Oncology. Oncology 2020; 98:430-437. [PMID: 31901906 DOI: 10.1159/000505341] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 12/10/2019] [Indexed: 11/19/2022]
Abstract
Emotions play an important role in decision-making and they can impact individual as well as shared decisions. With increasing complexity of the decision, the potential for emotions to influence the outcome increases. Emotions are thus an influential factor in oncological decision-making which is a complex and high-stakes situation. As the shared decision-making process is at the center of patient-centric decisions, we model emotions as social information that inform the shared decision-making process. We present and explain a range of emotional concepts, together with a specific clinical example, that can impact the shared decision-making process. Our process model shows that emotions are experienced in various combinations before, during, and after a shared decision is made and how patients' and physicians' emotions interact and spill over during a shared decision situation. Overall, our process model and specific example show how emotions can impact shared decision-making in oncology in a multitude of ways. With this paper, we want to raise awareness of the role of emotions in the shared decision-making process, as emotions are often not explicitly recognized as decision criteria. Increased awareness of emotions may help their optimal utilization and reduce their influence as a bias in shared decision-making.
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Affiliation(s)
- Theresa Treffers
- Seeburg Castle University, Seekirchen am Wallersee, Austria, .,TUM School of Management, Technical University of Munich, Munich, Germany,
| | - Paul Martin Putora
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland.,Department of Radiation Oncology, Inselspital Bern, University of Bern, Bern, Switzerland
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Luo Y, Tseng HH, Cui S, Wei L, Ten Haken RK, El Naqa I. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR Open 2019; 1:20190021. [PMID: 33178948 PMCID: PMC7592485 DOI: 10.1259/bjro.20190021] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/18/2019] [Accepted: 06/25/2019] [Indexed: 12/17/2022] Open
Abstract
Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.
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Affiliation(s)
- Yi Luo
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Sunan Cui
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
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