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Kryukov M, Moriarty KP, Villamea M, O'Dwyer I, Chow O, Dormont F, Hernandez R, Bar-Joseph Z, Rufino B. Proxy endpoints - bridging clinical trials and real world data. J Biomed Inform 2024; 158:104723. [PMID: 39299565 DOI: 10.1016/j.jbi.2024.104723] [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: 06/20/2024] [Revised: 08/15/2024] [Accepted: 09/03/2024] [Indexed: 09/22/2024]
Abstract
OBJECTIVE Disease severity scores, or endpoints, are routinely measured during Randomized Controlled Trials (RCTs) to closely monitor the effect of treatment. In real-world clinical practice, although a larger set of patients is observed, the specific RCT endpoints are often not captured, which makes it hard to utilize real-world data (RWD) to evaluate drug efficacy in larger populations. METHODS To overcome this challenge, we developed an ensemble technique which learns proxy models of disease endpoints in RWD. Using a multi-stage learning framework applied to RCT data, we first identify features considered significant drivers of disease available within RWD. To create endpoint proxy models, we use Explainable Boosting Machines (EBMs) which allow for both end-user interpretability and modeling of non-linear relationships. RESULTS We demonstrate our approach on two diseases, rheumatoid arthritis (RA) and atopic dermatitis (AD). As we show, our combined feature selection and prediction method achieves good results for both disease areas, improving upon prior methods proposed for predictive disease severity scoring. CONCLUSION Having disease severity over time for a patient is important to further disease understanding and management. Our results open the door to more use cases in the space of RA and AD such as treatment effect estimates or prognostic scoring on RWD. Our framework may be extended beyond RA and AD to other diseases where the severity score is not well measured in electronic health records.
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Affiliation(s)
- Maxim Kryukov
- Data & Computational Science, R&D, Sanofi, Barcelona, Spain.
| | - Kathleen P Moriarty
- Data & Computational Science, R&D, Sanofi, 240 Richmond Street West, 3rd Floor, Toronto, M5V 1V6, Ontario, Canada.
| | | | - Ingrid O'Dwyer
- Data & Computational Science, R&D, Sanofi, 240 Richmond Street West, 3rd Floor, Toronto, M5V 1V6, Ontario, Canada.
| | - Ohn Chow
- Clinical Immunology and Inflammation, R&D, Sanofi, 450 Water St, MA, Cambridge, 02141, MA, United States.
| | - Flavio Dormont
- Clinical Real World Evidence, R&D, Sanofi, 46 Av. de la Grande Armée, Paris, 75017, Île-de-France, France.
| | - Ramon Hernandez
- Clinical Real World Evidence, R&D, Sanofi, 46 Av. de la Grande Armée, Paris, 75017, Île-de-France, France.
| | - Ziv Bar-Joseph
- Data & Computational Science, R&D, Sanofi, 450 Water St, MA, Cambridge, 02141, MA, United States.
| | - Brandon Rufino
- Data & Computational Science, R&D, Sanofi, 240 Richmond Street West, 3rd Floor, Toronto, M5V 1V6, Ontario, Canada.
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Chauhan C, Gullapalli RR. Ethics of AI in Pathology: Current Paradigms and Emerging Issues. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 191:1673-1683. [PMID: 34252382 PMCID: PMC8485059 DOI: 10.1016/j.ajpath.2021.06.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/18/2021] [Accepted: 06/24/2021] [Indexed: 02/06/2023]
Abstract
Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured nature of pathology data repositories makes it highly attractive to AI researchers to train deep learning models to improve health care delivery. Additionally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the health care delivery process. AI, if used unethically, may exacerbate existing inequities of health care, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. This review explores the key issues involving AI ethics in pathology. Issues related to ethical design of pathology AI studies and the potential risks associated with implementation of AI and ADM within the pathology workflow are discussed. Three key foundational principles of ethical AI: transparency, accountability, and governance, are described in the context of pathology. The future practice of pathology must be guided by these principles. Pathologists should be aware of the potential of AI to deliver superlative health care and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive future implementation of ethical AI in the practice of pathology.
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Affiliation(s)
- Chhavi Chauhan
- American Society of Investigative Pathology, Rockville, Maryland
| | - Rama R Gullapalli
- Department of Pathology, University of New Mexico, Albuquerque, New Mexico; Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, New Mexico.
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Development of machine learning model for diagnostic disease prediction based on laboratory tests. Sci Rep 2021; 11:7567. [PMID: 33828178 PMCID: PMC8026627 DOI: 10.1038/s41598-021-87171-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 03/19/2021] [Indexed: 01/16/2023] Open
Abstract
The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.
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Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04051-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Ghosh S, Roy A, Chakraborty I, Mukhopadhyay M, DasGupta S, Sarkar D. Fractal Dimension of Erythrocyte Membranes: A Highly Useful Precursor for Rapid Morphological Assay. Ann Biomed Eng 2018; 46:1362-1375. [PMID: 29796956 DOI: 10.1007/s10439-018-2050-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 05/14/2018] [Indexed: 12/18/2022]
Abstract
Morphology of erythrocyte membrane has been recognized as an alternative biomarker of several patho-physiological states. Numerous attempts have been made to upgrade the existing method of primitive manual counting, particularly exploring the light scattering properties of erythrocyte. All the techniques are at best semi-empirical and heavily rely on the effectiveness of the statistical correlations. Precisely, this is due to the lack of a non-empirical scale of the so-called "morphological scores". In this article, fractal dimension of erythrocyte membrane has been used to formulate a suitable scoring scale. Subsequently, the rapid experimental output of flow-cytometry has been functionally related to the mean morphological quantifier of the whole cell population via an optimum neural network model (R2 = 0.98). Moreover, the fractal dimension has been further demonstrated to be an important parameter in early detection of an abnormal patho-physiological state, even without any noticeable poikilocytic transformation in micrometric domain.
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Affiliation(s)
- Sayari Ghosh
- Department of Chemical Engineering, University of Calcutta, Kolkata, 700 009, India
| | - Arpan Roy
- Department of Chemical Engineering, University of Calcutta, Kolkata, 700 009, India
| | - Ishita Chakraborty
- Department of Physiology, University of Calcutta, Kolkata, 700 009, India
| | | | - Sunando DasGupta
- Department of Chemical Engineering, Indian Institute of Technology, Kharagpur, 721302, India
| | - Debasish Sarkar
- Department of Chemical Engineering, University of Calcutta, Kolkata, 700 009, India.
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Hueso M, Vellido A, Montero N, Barbieri C, Ramos R, Angoso M, Cruzado JM, Jonsson A. Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy. KIDNEY DISEASES 2018; 4:1-9. [PMID: 29594137 DOI: 10.1159/000486394] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 12/14/2017] [Indexed: 12/14/2022]
Abstract
Background Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Summary and Key Messages Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.
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Affiliation(s)
- Miguel Hueso
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Alfredo Vellido
- bIntelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Nuria Montero
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | | | - Rosa Ramos
- cFresenius Medical Care, Bad Homburg, Germany
| | - Manuel Angoso
- dDialysis Unit, Clínica Virgen del Consuelo, Valencia, Spain
| | - Josep Maria Cruzado
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Anders Jonsson
- eArtificial Intelligence and Machine Learning Research Group, Universitat Pompeu Fabra (UPF), Barcelona, Spain
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