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Petch J, Nelson W, Wu M, Ghassemi M, Benz A, Fatemi M, Di S, Carnicelli A, Granger C, Giugliano R, Hong H, Patel M, Wallentin L, Eikelboom J, Connolly SJ. Optimizing warfarin dosing for patients with atrial fibrillation using machine learning. Sci Rep 2024; 14:4516. [PMID: 38402362 PMCID: PMC10894214 DOI: 10.1038/s41598-024-55110-9] [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: 05/02/2023] [Accepted: 02/20/2024] [Indexed: 02/26/2024] Open
Abstract
While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.
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Affiliation(s)
- Jeremy Petch
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.
- Population Health Research Institute, Hamilton, ON, Canada.
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada.
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
| | - Walter Nelson
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Mary Wu
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical and Evaluative Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Vector Institute, Toronto, ON, Canada
| | - Alexander Benz
- Population Health Research Institute, Hamilton, ON, Canada
- Department of Cardiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | | | - Shuang Di
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Anthony Carnicelli
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
| | - Christopher Granger
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
| | - Robert Giugliano
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hwanhee Hong
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Manesh Patel
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Lars Wallentin
- Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - John Eikelboom
- Population Health Research Institute, Hamilton, ON, Canada
- Division of Hematology and Thromboembolism, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Stuart J Connolly
- Population Health Research Institute, Hamilton, ON, Canada
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada
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Lu Y, Chu Q, Li Z, Wang M, Gatenby R, Zhang Q. Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer. Brief Bioinform 2024; 25:bbae071. [PMID: 38493345 DOI: 10.1093/bib/bbae071] [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: 10/09/2023] [Revised: 01/11/2024] [Accepted: 02/03/2024] [Indexed: 03/18/2024] Open
Abstract
The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.
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Affiliation(s)
- Yitao Lu
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Qian Chu
- Department of Thoracic Oncology, Tongji Hospital, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Mengdi Wang
- Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning, Princeton University, 08544, NJ, U.S.A
| | - Robert Gatenby
- Department of Integrated Mathematical Oncology and the Cancer Biology and Evolution Program, H. Lee Moffitt Cancer Center and Research Institute, 33612, FL, USA
| | - Qingpeng Zhang
- Musketeers Foundation Institute of Data Science and the Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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Woodman RJ, Mangoni AA. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clin Exp Res 2023; 35:2363-2397. [PMID: 37682491 PMCID: PMC10627901 DOI: 10.1007/s40520-023-02552-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023]
Abstract
The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.
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Affiliation(s)
- Richard J Woodman
- Centre of Epidemiology and Biostatistics, College of Medicine and Public Health, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia.
| | - Arduino A Mangoni
- Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, SA, Australia
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Walter W, Pohlkamp C, Meggendorfer M, Nadarajah N, Kern W, Haferlach C, Haferlach T. Artificial intelligence in hematological diagnostics: Game changer or gadget? Blood Rev 2023; 58:101019. [PMID: 36241586 DOI: 10.1016/j.blre.2022.101019] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 09/21/2022] [Accepted: 10/03/2022] [Indexed: 11/30/2022]
Abstract
The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have shown that AI-based models can already be used to automatically differentiate cells, reliably detect malignant cell populations, support chromosome banding analysis, and interpret clinical variants, contributing to early disease detection and prognosis. However, even the best tool can become useless if it is misapplied or the results are misinterpreted. Therefore, in order to comprehensively judge and correctly apply newly developed AI-based systems, the hematologist must have a basic understanding of the general concepts of machine learning. In this review, we provide the hematologist with a comprehensive overview of various machine learning techniques, their current implementations and approaches in different diagnostic subfields (e.g., cytogenetics, molecular genetics), and the limitations and unresolved challenges of the systems.
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Affiliation(s)
- Wencke Walter
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Christian Pohlkamp
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Manja Meggendorfer
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Niroshan Nadarajah
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Wolfgang Kern
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Claudia Haferlach
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
| | - Torsten Haferlach
- MLL Munich Leukemia Laboratory, Max-Lebsche-Platz 31, 81377 München, Germany.
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Milani R, Moll M, De Leone R, Pickl S. A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information. SENSORS (BASEL, SWITZERLAND) 2023; 23:2013. [PMID: 36850617 PMCID: PMC9961455 DOI: 10.3390/s23042013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/27/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Nowadays, Artificial Intelligence systems have expanded their competence field from research to industry and daily life, so understanding how they make decisions is becoming fundamental to reducing the lack of trust between users and machines and increasing the transparency of the model. This paper aims to automate the generation of explanations for model-free Reinforcement Learning algorithms by answering "why" and "why not" questions. To this end, we use Bayesian Networks in combination with the NOTEARS algorithm for automatic structure learning. This approach complements an existing framework very well and demonstrates thus a step towards generating explanations with as little user input as possible. This approach is computationally evaluated in three benchmarks using different Reinforcement Learning methods to highlight that it is independent of the type of model used and the explanations are then rated through a human study. The results obtained are compared to other baseline explanation models to underline the satisfying performance of the framework presented in terms of increasing the understanding, transparency and trust in the action chosen by the agent.
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Affiliation(s)
- Rudy Milani
- Faculty of Computer Science, Universitaet der Bundeswehr Muenchen, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany
| | - Maximilian Moll
- Faculty of Computer Science, Universitaet der Bundeswehr Muenchen, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany
| | - Renato De Leone
- School of Science and Technology, University of Camerino, via Madonna delle Carceri 9, 62032 Camerino, Italy
| | - Stefan Pickl
- Faculty of Computer Science, Universitaet der Bundeswehr Muenchen, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany
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Yang CY, Shiranthika C, Wang CY, Chen KW, Sumathipala S. Reinforcement learning strategies in cancer chemotherapy treatments: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107280. [PMID: 36529000 DOI: 10.1016/j.cmpb.2022.107280] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Cancer is one of the major causes of death worldwide and chemotherapies are the most significant anti-cancer therapy, in spite of the emerging precision cancer medicines in the last 2 decades. The growing interest in developing the effective chemotherapy regimen with optimal drug dosing schedule to benefit the clinical cancer patients has spawned innovative solutions involving mathematical modeling since the chemotherapy regimens are administered cyclically until the futility or the occurrence of intolerable adverse events. Thus, in this present work, we reviewed the emerging trends involved in forming a computational solution from the aspect of reinforcement learning. METHODS Initially, this survey in-depth focused on the details of the dynamic treatment regimens from a broad perspective and then narrowed down to inspirations from reinforcement learning that were advantageous to chemotherapy dosing, including both offline reinforcement learning and supervised reinforcement learning. RESULTS The insights established in the chemotherapy-planning problem associated with the Reinforcement Learning (RL) has been discussed in this study. It showed that the researchers were able to widen their perspectives in comprehending the theoretical basis, dynamic treatment regimens (DTR), use of the adaptive control on DTR, and the associated RL techniques. CONCLUSIONS This study reviewed the recent researches relevant to the topic, and highlighted the challenges, open questions, possible solutions, and future steps in inventing a realistic solution for the aforementioned problem.
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Affiliation(s)
- Chan-Yun Yang
- Department of Electrical Engineering, National Taipei University, New Taipei City, Taiwan
| | - Chamani Shiranthika
- Department of Electrical Engineering, National Taipei University, New Taipei City, Taiwan
| | - Chung-Yih Wang
- Department of Radiation Oncology, Cheng Hsin General Hospital, Taipei City, Taiwan
| | - Kuo-Wei Chen
- Section of Hematology and Oncology, Department of Internal Medicine, Cheng Hsin General Hospital, Taipei City, Taiwan.
| | - Sagara Sumathipala
- Faculty of Information Technology, University of Moratuwa, Katubedda, Moratuwa, Sri Lanka
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Shah SIH, De Pietro G, Paragliola G, Coronato A. Projection based inverse reinforcement learning for the analysis of dynamic treatment regimes. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04173-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractDynamic Treatment Regimes (DTRs) are adaptive treatment strategies that allow clinicians to personalize dynamically the treatment for each patient based on their step-by-step response to their treatment. There are a series of predefined alternative treatments for each disease and any patient may associate with one of these treatments according to his/her demographics. DTRs for a certain disease are studied and evaluated by means of statistical approaches where patients are randomized at each step of the treatment and their responses are observed. Recently, the Reinforcement Learning (RL) paradigm has also been applied to determine DTRs. However, such approaches may be limited by the need to design a true reward function, which may be difficult to formalize when the expert knowledge is not well assessed, as when the DTR is in the design phase. To address this limitation, an extension of the RL paradigm, namely Inverse Reinforcement Learning (IRL), has been adopted to learn the reward function from data, such as those derived from DTR trials. In this paper, we define a Projection Based Inverse Reinforcement Learning (PB-IRL) approach to learn the true underlying reward function for given demonstrations (DTR trials). Such a reward function can be used both to evaluate the set of DTRs determined for a certain disease, as well as to enable an RL-based intelligent agent to self-learn the best way and then act as a decision support system for the clinician.
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Fiste O, Liontos M, Zagouri F, Stamatakos G, Dimopoulos MA. Machine learning applications in gynecological cancer: A critical review. Crit Rev Oncol Hematol 2022; 179:103808. [PMID: 36087852 DOI: 10.1016/j.critrevonc.2022.103808] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/18/2022] [Accepted: 09/05/2022] [Indexed: 11/30/2022] Open
Abstract
Machine Learning (ML) represents a computer science capable of generating predictive models, by exposure to raw, training data, without being rigidly programmed. Over the last few years, ML has gained attention within the field of oncology, with considerable strides in both diagnostic, predictive, and prognostic spectrum of malignancies, but also as a catalyst of cancer research. In this review, we discuss the state of ML applications on gynecologic oncology and systematically address major technical and ethical concerns, with respect to their real-world medical practice translation. Undoubtedly, advances in ML will enable the analysis of large, rather complex, datasets for improved, cost-effective, and efficient clinical decisions.
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Affiliation(s)
- Oraianthi Fiste
- Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece.
| | - Michalis Liontos
- Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece
| | - Flora Zagouri
- Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Meletios Athanasios Dimopoulos
- Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece
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Kohli M, Kar AK, Bangalore A, AP P. Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study. Brain Inform 2022; 9:16. [PMID: 35879626 PMCID: PMC9311349 DOI: 10.1186/s40708-022-00164-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/25/2022] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum is a brain development condition that impairs an individual's capacity to communicate socially and manifests through strict routines and obsessive-compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81-84%, with a normalized discounted cumulative gain of 79-81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models' treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.
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Affiliation(s)
- Manu Kohli
- Indian Institute of Technology-Delhi, Department of Management Studies, IV Floor, Vishwakarma Bhavan, Shaheed Jeet Singh Marg, Hauz Khas, New Delhi, 110016 India
| | - Arpan Kumar Kar
- Indian Institute of Technology-Delhi, Department of Management Studies, IV Floor, Vishwakarma Bhavan, Shaheed Jeet Singh Marg, Hauz Khas, New Delhi, 110016 India
| | - Anjali Bangalore
- ICON Centre, K. M. Chavan chawk, Shivajinagar Road, Garkheda, Aurangabad, 431005 India
| | - Prathosh AP
- Indian Institute of Science, CV Raman Rd, Bengaluru, 560012 Karnataka India
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Effects of a continuous nursing care model on elderly patients with total hip arthroplasty: a randomized controlled trial. Aging Clin Exp Res 2022; 34:1603-1611. [PMID: 34476774 DOI: 10.1007/s40520-021-01965-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/14/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND Continuous nursing care (CNC) is an extended service based on meeting the needs of discharged patients for post-discharge treatment and rehabilitation. This research aimed to investigate the effects of CNC on older patients with total hip arthroplasty and to offer a scientific basis for improving the prognosis. METHODS A total of 134 patients with total hip arthroplasty were randomly divided into the control group (n = 67) and the intervention group (n = 67). The control group was treated by conventional nursing care and the intervention group was treated by CNC. Harris hip score, Barthel index, the activities of daily living (ADL) scale, self-rating depression scale (SDS) and self-rating anxiety scale (SAS) in these two groups were evaluated. Demographic characteristics between groups were analyzed by unpaired t test. The observation indexes between groups were assessed by two-way ANOVA test followed by Tukey's multiple comparisons test. RESULTS The scores of Harris hip score, Barthel index, ADL, SDS and SAS in the intervention group after intervention and after follow-up were better than the intervention group before intervention (all p < 0.01). Meanwhile, the scores of Harris hip score, Barthel index, ADL, SDS and SAS in the intervention group were better than the control group both after intervention and after follow-up (all p < 0.01). CONCLUSION In conclusion, CNC showed better efficacy than conventional nursing care in promoting hip joint function recovery, improving quality of life and alleviating anxiety and depression for older patients with total hip arthroplasty.
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Ma P, Liu J, Shen F, Liao X, Xiu M, Zhao H, Zhao M, Xie J, Wang P, Huang M, Li T, Duan M, Qian K, Peng Y, Zhou F, Xin X, Wan X, Wang Z, Li S, Han J, Li Z, Ding G, Deng Q, Zhang J, Zhu Y, Ma W, Wang J, Kang Y, Zhang Z. Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen. Crit Care 2021; 25:243. [PMID: 34253228 PMCID: PMC8273991 DOI: 10.1186/s13054-021-03682-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/06/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class. METHODS Patients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset. RESULTS A total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion. CONCLUSIONS Septic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine.
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Affiliation(s)
- Penglin Ma
- Department of Critical Care Medicine, Guiqian International General Hospital, Guiyang, People's Republic of China
| | - Jingtao Liu
- Department of Critical Care Medicine, The 8th Medical Center of Chinese, PLA General Hospital, Beijing, 100091, People's Republic of China
| | - Feng Shen
- Department of Intensive Care Unit, Guizhou Medical University Affiliated Hospital, Guiyang, People's Republic of China
| | - Xuelian Liao
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, People's Republic of China
| | - Ming Xiu
- Department of Intensive Care Unit, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Heling Zhao
- Department of Critical Care Medicine, Hebei General Hospital, Shijiazhuang, People's Republic of China
| | - Mingyan Zhao
- Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, People's Republic of China
| | - Jing Xie
- General Intensive Care Unit Department, The First Affiliated Hospital of Fujian Medical University, Fuzhou, People's Republic of China
| | - Peng Wang
- Department of Critical Care Medicine, Fu Xing Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Man Huang
- General Intensive Care Unit, Second Affiliated Hospital of Zhejiang University, Hangzhou, People's Republic of China
| | - Tong Li
- Department of Critical Care Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Kejian Qian
- Department of Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Yue Peng
- Department of Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Feihu Zhou
- Department of Critical Care Medicine, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Xin Xin
- Surgical Intensive Care Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xianyao Wan
- The First Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - ZongYu Wang
- Department of Intensive Care, Peking University Third Hospital, Beijing, People's Republic of China
| | - Shusheng Li
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Jianwei Han
- Department of Critical Care Medicine, The 8th medical Center of Chinese, PLA General Hospital, Beijing, People's Republic of China
| | - Zhenliang Li
- Department of Critical Care, Beijing PingGu Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Guolei Ding
- Intensive Care Unit, The Hospital of Shunyi District, Beijing, People's Republic of China
| | - Qun Deng
- Department of Critical Care Medicine, The 4th Medical Center of Chinese, PLA General Hospital, Beijing, People's Republic of China
| | - Jicheng Zhang
- Department of Critical Care Medicine, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, Jinan, People's Republic of China
| | - Yue Zhu
- Department of Critical Care, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Wenjing Ma
- Department of Critical Care, Beijing Miyun Hospital, Beijing, People's Republic of China
| | - Jingwen Wang
- Intensive Care Unit, Beijing Changping District Hospital, Beijing, People's Republic of China
| | - Yan Kang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, People's Republic of China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People's Republic of China.
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12
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Seligson ND, Warner JL, Dalton WS, Martin D, Miller RS, Patt D, Kehl KL, Palchuk MB, Alterovitz G, Wiley LK, Huang M, Shen F, Wang Y, Nguyen KA, Wong AF, Meric-Bernstam F, Bernstam EV, Chen JL. Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity. J Am Med Inform Assoc 2021; 27:1808-1812. [PMID: 32885823 PMCID: PMC7671612 DOI: 10.1093/jamia/ocaa159] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 06/19/2020] [Accepted: 07/24/2020] [Indexed: 12/14/2022] Open
Abstract
Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.
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Affiliation(s)
- Nathan D Seligson
- University of Florida, Jacksonville, Florida, USA.,Nemours Children's Specialty Care, Jacksonville, Florida, USA
| | | | - William S Dalton
- M2Gen, Tampa, Florida, USA.,H. Lee Moffitt Cancer Center, Tampa, Florida, USA
| | - David Martin
- United States Food and Drug Administration, Silver Spring, Maryland, USA
| | - Robert S Miller
- American Society of Clinical Oncology, Alexandria, Virginia, USA
| | | | - Kenneth L Kehl
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Matvey B Palchuk
- Harvard Medical School, Boston, Massachusetts, USA.,TriNetX, Cambridge, Massachusetts, USA
| | - Gil Alterovitz
- Harvard Medical School, Boston, Massachusetts, USA.,Boston Children's Hospital, Boston, Massachusetts, USA
| | - Laura K Wiley
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | | | | | | | - Anthony F Wong
- Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | | | - Elmer V Bernstam
- The University of Texas Health Science Center at Houston, Texas, USA
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13
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Reinforcement Learning in Neurocritical and Neurosurgical Care: Principles and Possible Applications. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6657119. [PMID: 33680069 PMCID: PMC7925047 DOI: 10.1155/2021/6657119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/03/2021] [Accepted: 02/04/2021] [Indexed: 12/22/2022]
Abstract
Dynamic decision-making was essential in the clinical care of surgical patients. Reinforcement learning (RL) algorithm is a computational method to find sequential optimal decisions among multiple suboptimal options. This review is aimed at introducing RL's basic concepts, including three basic components: the state, the action, and the reward. Most medical studies using reinforcement learning methods were trained on a fixed observational dataset. This paper also reviews the literature of existing practical applications using reinforcement learning methods, which can be further categorized as a statistical RL study and a computational RL study. The review proposes several potential aspects where reinforcement learning can be applied in neurocritical and neurosurgical care. These include sequential treatment strategies of intracranial tumors and traumatic brain injury and intraoperative endoscope motion control. Several limitations of reinforcement learning are representations of basic components, the positivity violation, and validation methods.
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14
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Qayyum A, Qadir J, Bilal M, Al-Fuqaha A. Secure and Robust Machine Learning for Healthcare: A Survey. IEEE Rev Biomed Eng 2021; 14:156-180. [PMID: 32746371 DOI: 10.1109/rbme.2020.3013489] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.
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