151
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Waters DJ. Devising a new dialogue for nutrition science: how life course perspective, U-shaped thinking, whole organism thinking, and language precision contribute to our understanding of biological heterogeneity and forge a fresh advance toward precision medicine. J Anim Sci 2020; 98:5736391. [PMID: 32060544 DOI: 10.1093/jas/skaa051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 02/12/2020] [Indexed: 11/13/2022] Open
Abstract
The process of designing and implementing individualized health-promoting interventions, nutritional or otherwise, is fraught with great difficulty owing to the heterogeneity inherent in factors that influence healthy longevity. This article proposes that careful attention to three principles-life course perspective, U-shaped thinking, and whole organism thinking-creates an attitudinal framework that can be used to reframe biological heterogeneity into the clinically relevant question: Who will benefit? The search for tools to cope with the complexity of this heterogeneity has been dominated by technological advances, including state-of-the-art "-omics" approaches and machine-based handling of "big data." Here, it is proposed that language precision and nuanced category usage could provide critical tools for coping with heterogeneity, thereby enabling interventionalists to design and implement strategies to promote healthy longevity with greater precision. The lack of a clear understanding of "Who will benefit?" stands as a major obstacle to the design and implementation of nutritional strategies to optimize healthy longevity. This article opens a new dialogue situating the principles of life course perspective, U-shaped thinking, and whole organism thinking, along with cultivating an attitude of language precision at the very core of accelerating creative discovery and refining practical advance in the field of nutrition science.
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Affiliation(s)
- David J Waters
- Center for Exceptional Longevity Studies, Gerald P. Murphy Cancer Foundation, West Lafayette, IN
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152
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Paulus MP. Pragmatic and Explanatory Progress Using Statistical Models of Disturbed Mind, Brain, and Behavior to Improve Mental Health. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:723-725. [PMID: 32771178 DOI: 10.1016/j.bpsc.2020.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 06/09/2020] [Indexed: 11/30/2022]
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153
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Holman D, Salway S, Bell A. Mapping intersectional inequalities in biomarkers of healthy ageing and chronic disease in older English adults. Sci Rep 2020; 10:13522. [PMID: 32782305 PMCID: PMC7419497 DOI: 10.1038/s41598-020-69934-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 05/05/2020] [Indexed: 11/29/2022] Open
Abstract
Chronic diseases and their inequalities amongst older adults are a significant public health challenge. Prevention and treatment of chronic diseases will benefit from insight into which population groups show greatest risk. Biomarkers are indicators of the biological mechanisms underlying health and disease. We analysed disparities in a common set of biomarkers at the population level using English national data (n = 16,437). Blood-based biomarkers were HbA1c, total cholesterol and C-reactive protein. Non-blood biomarkers were systolic blood pressure, resting heart rate and body mass index. We employed an intersectionality perspective which is concerned with how socioeconomic, gender and ethnic disparities combine to lead to varied health outcomes. We find granular intersectional disparities, which vary by biomarker, with total cholesterol and HbA1c showing the greatest intersectional variation. These disparities were additive rather than multiplicative. Each intersectional subgroup has its own profile of biomarkers. Whilst the majority of variation in biomarkers is at the individual rather than intersectional level (i.e. intersections exhibit high heterogeneity), the average differences are potentially associated with important clinical outcomes. An intersectional perspective helps to shed light on how socio-demographic factors combine to result in differential risk for disease or potential for healthy ageing.
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Affiliation(s)
- Daniel Holman
- Department of Sociological Studies, University of Sheffield, Sheffield, UK.
| | - Sarah Salway
- Department of Sociological Studies, University of Sheffield, Sheffield, UK
| | - Andrew Bell
- Sheffield Methods Institute, University of Sheffield, Sheffield, UK
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154
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Abstract
The rising burden of healthcare costs suggests that the healthcare system could benefit from novel methods that allow for continuous learning to provide more data-driven, individualised care at lower costs and with improved outcomes. Here, we present our synergistic Learning approach for Prediction, Interpretation/Inference and Communication (Learning PIC) framework to address the challenges hindering the successful implementation of learning healthcare systems and to enable the effective delivery of evidence-based medicine.
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Affiliation(s)
- Shannon Wongvibulsin
- Johns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Scott L. Zeger
- Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
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155
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Berns A, Ringborg U, Celis JE, Heitor M, Aaronson NK, Abou‐Zeid N, Adami H, Apostolidis K, Baumann M, Bardelli A, Bernards R, Brandberg Y, Caldas C, Calvo F, Dive C, Eggert A, Eggermont A, Espina C, Falkenburg F, Foucaud J, Hanahan D, Helbig U, Jönsson B, Kalager M, Karjalainen S, Kásler M, Kearns P, Kärre K, Lacombe D, de Lorenzo F, Meunier F, Nettekoven G, Oberst S, Nagy P, Philip T, Price R, Schüz J, Solary E, Strang P, Tabernero J, Voest E. Towards a cancer mission in Horizon Europe: recommendations. Mol Oncol 2020; 14:1589-1615. [PMID: 32749074 PMCID: PMC7400777 DOI: 10.1002/1878-0261.12763] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 07/01/2020] [Indexed: 12/26/2022] Open
Abstract
A comprehensive translational cancer research approach focused on personalized and precision medicine, and covering the entire cancer research-care-prevention continuum has the potential to achieve in 2030 a 10-year cancer-specific survival for 75% of patients diagnosed in European Union (EU) member states with a well-developed healthcare system. Concerted actions across this continuum that spans from basic and preclinical research through clinical and prevention research to outcomes research, along with the establishment of interconnected high-quality infrastructures for translational research, clinical and prevention trials and outcomes research, will ensure that science-driven and social innovations benefit patients and individuals at risk across the EU. European infrastructures involving comprehensive cancer centres (CCCs) and CCC-like entities will provide researchers with access to the required critical mass of patients, biological materials and technological resources and can bridge research with healthcare systems. Here, we prioritize research areas to ensure a balanced research portfolio and provide recommendations for achieving key targets. Meeting these targets will require harmonization of EU and national priorities and policies, improved research coordination at the national, regional and EU level and increasingly efficient and flexible funding mechanisms. Long-term support by the EU and commitment of Member States to specialized schemes are also needed for the establishment and sustainability of trans-border infrastructures and networks. In addition to effectively engaging policymakers, all relevant stakeholders within the entire continuum should consensually inform policy through evidence-based advice.
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156
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Mascitti M, Tempesta A, Togni L, Capodiferro S, Troiano G, Rubini C, Maiorano E, Santarelli A, Favia G, Limongelli L. Histological features and survival in young patients with HPV-negative oral squamous cell carcinoma. Oral Dis 2020; 26:1640-1648. [PMID: 32531817 DOI: 10.1111/odi.13479] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 05/10/2020] [Accepted: 06/04/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVES The frequency of oral squamous cell carcinoma in young adults has increased in the last decades, and there are conflicting results in literature about its prognosis in young subjects. The aim of this study was to analyse the clinical and pathological features of oral squamous cell carcinoma in a cohort of young adults in order to investigate the presence of new independent prognostic markers. MATERIALS AND METHODS Only HPV-negative young patients (under 40-year-old) affected by oral squamous cell carcinoma were considered in this study. Clinical and pathological data were collected. Patients were re-staged according to the 8th edition of AJCC. RESULTS Overall, 66 patients were considered in this study. Perineural invasion significant correlated with both 7th and 8th edition of AJCC, and lymphovascular invasion (p-value < .05). The multivariate survival analysis showed that patients with perineural invasion had a significant worse prognosis (HR = 6.384 95% C.I. 1.304-31.252; p-value = .022). CONCLUSIONS Perineural invasion emerged as an independent prognostic factor for disease-specific survival in young patients with oral squamous cell carcinoma. Furthermore, the evaluation of this parameter is simple, inexpensive and can be used to augment the risk stratification of oral cancer based on the 8th edition of AJCC.
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Affiliation(s)
- Marco Mascitti
- Department of Clinical Specialistic and Dental Sciences, Marche Polytechnic University, Ancona, Italy
| | - Angela Tempesta
- Interdisciplinary Department of Medicine, Section of Odontostomatology, University of Bari Aldo Moro, Bari, Italy
| | - Lucrezia Togni
- Department of Clinical Specialistic and Dental Sciences, Marche Polytechnic University, Ancona, Italy
| | - Saverio Capodiferro
- Interdisciplinary Department of Medicine, Section of Odontostomatology, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Troiano
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Corrado Rubini
- Department of Biomedical Sciences and Public Health, Marche Polytechnic University, Ancona, Italy
| | - Eugenio Maiorano
- Department of Emergency and Organ Transplantation, Section of Pathological Anatomy, University of Bari Aldo Moro, Bari, Italy
| | - Andrea Santarelli
- Department of Clinical Specialistic and Dental Sciences, Marche Polytechnic University, Ancona, Italy.,Dentistry Clinic, National Institute of Health and Science of Aging, IRCCS INRCA, Ancona, Italy
| | - Gianfranco Favia
- Interdisciplinary Department of Medicine, Section of Odontostomatology, University of Bari Aldo Moro, Bari, Italy
| | - Luisa Limongelli
- Interdisciplinary Department of Medicine, Section of Odontostomatology, University of Bari Aldo Moro, Bari, Italy
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157
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Schilaty ND, Bates NA, Ueno R, Hewett TE. Filtration Selection and Data Consilience: Distinguishing Signal from Artefact with Mechanical Impact Simulator Data. Ann Biomed Eng 2020; 49:334-344. [PMID: 32632532 DOI: 10.1007/s10439-020-02562-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 06/30/2020] [Indexed: 11/25/2022]
Abstract
A large variety of data filtration techniques exist in biomechanics literature. Data filtration is both an 'art' and a 'science' to eliminate noise and retain true signal to draw conclusions that will direct future hypotheses, experimentation, and technology development. Thus, data consilience is paramount, but is dependent on filtration methodologies. In this study, we utilized ligament strain, vertical ground reaction force, and kinetic data from cadaveric impact simulations to assess data from four different filters (12 vs. 50 Hz low-pass; forward vs. zero lag). We hypothesized that 50 Hz filtered data would demonstrate larger peak magnitudes, but exhibit consilience of waveforms and statistical significance as compared to 12 Hz filtered data. Results demonstrated high data consilience for matched pair t test correlations of peak ACL strain (≥ 0.97), MCL strain (≥ 0.93) and vertical ground reaction force (≥ 0.98). Kinetics had a larger range of correlation (0.06-0.96) that was dependent on both external load application and direction of motion monitored. Coefficients of multiple correlation demonstrated high data consilience for zero lag filtered data. With respect to in vitro mechanical data, selection of low-pass filter cutoff frequency will influence both the magnitudes of discrete and waveform data. Dependent on the data type (i.e., strain and ground reaction forces), this will not likely significantly alter conclusions of statistical significance previously reported in the literature with high consilience of matched pair t-test correlations and coefficients of multiple correlation demonstrated. However, rotational kinetics are more sensitive to filtration selection and could be suspect to errors, especially at lower magnitudes.
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Affiliation(s)
- Nathan D Schilaty
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.
- Sports Medicine Center, Mayo Clinic, Rochester, MN, USA.
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.
- Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, MN, USA.
- Biomechanics Laboratories, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Nathaniel A Bates
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Sports Medicine Center, Mayo Clinic, Rochester, MN, USA
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Biomechanics Laboratories, 200 First Street SW, Rochester, MN, 55905, USA
| | - Ryo Ueno
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Sports Medicine Center, Mayo Clinic, Rochester, MN, USA
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158
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Birkenbihl C, Emon MA, Vrooman H, Westwood S, Lovestone S, Hofmann-Apitius M, Fröhlich H. Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice. EPMA J 2020; 11:367-376. [PMID: 32843907 PMCID: PMC7429672 DOI: 10.1007/s13167-020-00216-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/05/2020] [Indexed: 12/23/2022]
Abstract
Artificial intelligence (AI) approaches pose a great opportunity for individualized, pre-symptomatic disease diagnosis which plays a key role in the context of personalized, predictive, and finally preventive medicine (PPPM). However, to translate PPPM into clinical practice, it is of utmost importance that AI-based models are carefully validated. The validation process comprises several steps, one of which is testing the model on patient-level data from an independent clinical cohort study. However, recruitment criteria can bias statistical analysis of cohort study data and impede model application beyond the training data. To evaluate whether and how data from independent clinical cohort studies differ from each other, this study systematically compares the datasets collected from two major dementia cohorts, namely, the Alzheimer's Disease Neuroimaging Initiative (ADNI) and AddNeuroMed. The presented comparison was conducted on individual feature level and revealed significant differences among both cohorts. Such systematic deviations can potentially hamper the generalizability of results which were based on a single cohort dataset. Despite identified differences, validation of a previously published, ADNI trained model for prediction of personalized dementia risk scores on 244 AddNeuroMed subjects was successful: External validation resulted in a high prediction performance of above 80% area under receiver operator characteristic curve up to 6 years before dementia diagnosis. Propensity score matching identified a subset of patients from AddNeuroMed, which showed significantly smaller demographic differences to ADNI. For these patients, an even higher prediction performance was achieved, which demonstrates the influence systematic differences between cohorts can have on validation results. In conclusion, this study exposes challenges in external validation of AI models on cohort study data and is one of the rare cases in the neurology field in which such external validation was performed. The presented model represents a proof of concept that reliable models for personalized predictive diagnostics are feasible, which, in turn, could lead to adequate disease prevention and hereby enable the PPPM paradigm in the dementia field.
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Affiliation(s)
- Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Mohammad Asif Emon
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Henri Vrooman
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands.,Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Sarah Westwood
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Simon Lovestone
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | | | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany.,UCB Biosciences GmbH, Alfred-Nobel Str. 10, 40789 Monheim am Rhein, Germany
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159
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Er Saw P, Jiang S. The Significance of Interdisciplinary Integration in Academic Research and Application. BIO INTEGRATION 2020. [DOI: 10.15212/bioi-2020-0005] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Phei Er Saw
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, People’s Republic of China
| | - Shanping Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, People’s Republic of China
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160
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Wang L, Alexander CA. Big data analytics in medical engineering and healthcare: methods, advances and challenges. J Med Eng Technol 2020; 44:267-283. [PMID: 32498594 DOI: 10.1080/03091902.2020.1769758] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Big data analytics are gaining popularity in medical engineering and healthcare use cases. Stakeholders are finding big data analytics reduce medical costs and personalise medical services for each individual patient. Big data analytics can be used in large-scale genetics studies, public health, personalised and precision medicine, new drug development, etc. The introduction of the types, sources, and features of big data in healthcare as well as the applications and benefits of big data and big data analytics in healthcare is key to understanding healthcare big data and will be discussed in this article. Major methods, platforms and tools of big data analytics in medical engineering and healthcare are also presented. Advances and technology progress of big data analytics in healthcare are introduced, which includes artificial intelligence (AI) with big data, infrastructure and cloud computing, advanced computation and data processing, privacy and cybersecurity, health economic outcomes and technology management, and smart healthcare with sensing, wearable devices and Internet of things (IoT). Current challenges of dealing with big data and big data analytics in medical engineering and healthcare as well as future work are also presented.
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Affiliation(s)
- Lidong Wang
- Institute for Systems Engineering Research, Mississippi State University, Vicksburg, MS, USA
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161
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Gardner W. Privacy-preserving statistical analyses in Learning Health Systems. Pediatr Res 2020; 87:978-979. [PMID: 32172277 DOI: 10.1038/s41390-020-0835-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 11/11/2019] [Indexed: 11/09/2022]
Affiliation(s)
- William Gardner
- University of Ottawa, Ottawa, ON, Canada. .,CHEO Research Institute, Ottawa, ON, Canada.
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162
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Thomford NE, Bope CD, Agamah FE, Dzobo K, Owusu Ateko R, Chimusa E, Mazandu GK, Ntumba SB, Dandara C, Wonkam A. Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology. ACTA ACUST UNITED AC 2020; 24:264-277. [DOI: 10.1089/omi.2019.0142] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Nicholas Ekow Thomford
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- School of Medical Sciences, Department of Medical Biochemistry, University of Cape Coast, Cape Coast, Ghana
| | - Christian Domilongo Bope
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- School of Medical Sciences, Department of Medical Biochemistry, University of Cape Coast, Cape Coast, Ghana
- Department of Mathematics and Computer Sciences, Faculty of Sciences, University of Kinshasa, Kinshasa, D.R. Congo
| | - Francis Edem Agamah
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Kevin Dzobo
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Division of Medical Biochemistry, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Richmond Owusu Ateko
- University of Ghana Medical School, Department of Chemical Pathology, University of Ghana, Accra, Ghana
| | - Emile Chimusa
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Gaston Kuzamunu Mazandu
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Simon Badibanga Ntumba
- Department of Mathematics and Computer Sciences, Faculty of Sciences, University of Kinshasa, Kinshasa, D.R. Congo
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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163
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Cuocolo R, Caruso M, Perillo T, Ugga L, Petretta M. Machine Learning in oncology: A clinical appraisal. Cancer Lett 2020; 481:55-62. [PMID: 32251707 DOI: 10.1016/j.canlet.2020.03.032] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/11/2020] [Accepted: 03/31/2020] [Indexed: 02/07/2023]
Abstract
Machine learning (ML) is a branch of artificial intelligence centered on algorithms which do not need explicit prior programming to function but automatically learn from available data, creating decision models to complete tasks. ML-based tools have numerous promising applications in several fields of medicine. Its use has grown following the increased availability of patient data due to technological advances such as digital health records and high-volume information extraction from medical images. Multiple ML algorithms have been proposed for applications in oncology. For instance, they have been employed for oncological risk assessment, automated segmentation, lesion detection, characterization, grading and staging, prediction of prognosis and therapy response. In the near future, ML could become essential part of every step of oncological screening strategies and patients' management thus leading to precision medicine.
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Affiliation(s)
- Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, 80131, Naples, Italy
| | - Martina Caruso
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, 80131, Naples, Italy
| | - Teresa Perillo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, 80131, Naples, Italy.
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, 80131, Naples, Italy
| | - Mario Petretta
- Department of Translational Medical Sciences, University of Naples "Federico II", Via S. Pansini 5, 80131, Naples, Italy
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164
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Govender R, Abrahmsén-Alami S, Larsson A, Folestad S. Therapy for the individual: Towards patient integration into the manufacturing and provision of pharmaceuticals. Eur J Pharm Biopharm 2020; 149:58-76. [DOI: 10.1016/j.ejpb.2020.01.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 12/23/2019] [Accepted: 01/08/2020] [Indexed: 12/18/2022]
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165
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Conrad K, Shoenfeld Y, Fritzler MJ. Precision health: A pragmatic approach to understanding and addressing key factors in autoimmune diseases. Autoimmun Rev 2020; 19:102508. [PMID: 32173518 DOI: 10.1016/j.autrev.2020.102508] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 11/06/2019] [Indexed: 02/07/2023]
Abstract
The past decade has witnessed a significant paradigm shift in the clinical approach to autoimmune diseases, lead primarily by initiatives in precision medicine, precision health and precision public health initiatives. An understanding and pragmatic implementation of these approaches require an understanding of the drivers, gaps and limitations of precision medicine. Gaining the trust of the public and patients is paramount but understanding that technologies such as artificial intelligences and machine learning still require context that can only be provided by human input or what is called augmented machine learning. The role of genomics, the microbiome and proteomics, such as autoantibody testing, requires continuing refinement through research and pragmatic approaches to their use in applied precision medicine.
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Affiliation(s)
- Karsten Conrad
- Institute of Immunology, Medical Faculty "Carl Gustav Carus", Technical University of Dresden, Dresden, Germany
| | - Yehuda Shoenfeld
- Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel; Department of Medicine, Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Marvin J Fritzler
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
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166
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Gray ID, Kross AR, Renfrew ME, Wood P. Precision Medicine in Lifestyle Medicine: The Way of the Future? Am J Lifestyle Med 2020; 14:169-186. [PMID: 32231483 PMCID: PMC7092395 DOI: 10.1177/1559827619834527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/21/2018] [Accepted: 02/08/2019] [Indexed: 02/06/2023] Open
Abstract
Precision medicine has captured the imagination of the medical community with visions of therapies precisely targeted to the specific individual's genetic, biological, social, and environmental profile. However, in practice it has become synonymous with genomic medicine. As such its successes have been limited, with poor predictive or clinical value for the majority of people. It adds little to lifestyle medicine, other than in establishing why a healthy lifestyle is effective in combatting chronic disease. The challenge of lifestyle medicine remains getting people to actually adopt, sustain, and naturalize a healthy lifestyle, and this will require an approach that treats the patient as a person with individual needs and providing them with suitable types of support. The future of lifestyle medicine is holistic and person-centered rather than technological.
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Affiliation(s)
- Ian D. Gray
- Avondale College of Higher Education, Cooranbong,
New South Wales, Australia
| | - Andrea R. Kross
- Avondale College of Higher Education, Cooranbong,
New South Wales, Australia
| | - Melanie E. Renfrew
- Avondale College of Higher Education, Cooranbong,
New South Wales, Australia
| | - Paul Wood
- Avondale College of Higher Education, Cooranbong,
New South Wales, Australia
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167
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Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine. Genes (Basel) 2020; 11:genes11030263. [PMID: 32121160 PMCID: PMC7140855 DOI: 10.3390/genes11030263] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 02/20/2020] [Accepted: 02/25/2020] [Indexed: 12/22/2022] Open
Abstract
Background: Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient’s genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. Methods: An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient’s preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells’ mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Results: Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. Conclusion: A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs’ recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions.
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168
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Geneviève LD, Martani A, Shaw D, Elger BS, Wangmo T. Structural racism in precision medicine: leaving no one behind. BMC Med Ethics 2020; 21:17. [PMID: 32075640 PMCID: PMC7031946 DOI: 10.1186/s12910-020-0457-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 02/06/2020] [Indexed: 01/17/2023] Open
Abstract
Background Precision medicine (PM) is an emerging approach to individualized care. It aims to help physicians better comprehend and predict the needs of their patients while effectively adopting in a timely manner the most suitable treatment by promoting the sharing of health data and the implementation of learning healthcare systems. Alongside its promises, PM also entails the risk of exacerbating healthcare inequalities, in particular between ethnoracial groups. One often-neglected underlying reason why this might happen is the impact of structural racism on PM initiatives. Raising awareness as to how structural racism can influence PM initiatives is paramount to avoid that PM ends up reproducing the pre-existing health inequalities between different ethnoracial groups and contributing to the loss of trust in healthcare by minority groups. Main body We analyse three nodes of a process flow where structural racism can affect PM’s implementation. These are: (i) the collection of biased health data during the initial encounter of minority groups with the healthcare system and researchers, (ii) the integration of biased health data for minority groups in PM initiatives and (iii) the influence of structural racism on the deliverables of PM initiatives for minority groups. We underscore that underappreciation of structural racism by stakeholders involved in the PM ecosystem can be at odds with the ambition of ensuring social and racial justice. Potential specific actions related to the analysed nodes are then formulated to help ensure that PM truly adheres to the goal of leaving no one behind, as endorsed by member states of the United Nations for the 2030 Agenda for Sustainable Development. Conclusion Structural racism has been entrenched in our societies for centuries and it would be naïve to believe that its impacts will not spill over in the era of PM. PM initiatives need to pay special attention to the discriminatory and harmful impacts that structural racism could have on minority groups involved in their respective projects. It is only by acknowledging and discussing the existence of implicit racial biases and trust issues in healthcare and research domains that proper interventions to remedy them can be implemented.
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Affiliation(s)
| | - Andrea Martani
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - David Shaw
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.,Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands
| | - Bernice Simone Elger
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.,University Center of Legal Medicine, University of Geneva, Geneva, Switzerland
| | - Tenzin Wangmo
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
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169
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Oliveira IM, Hernandez B, Kenny RA, Reilly RB. Automatic Disability Categorisation based on ADLs among Older Adults in a Nationally Representative Population using Data Mining Methods. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2466-2469. [PMID: 31946397 DOI: 10.1109/embc.2019.8856780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The world's ageing population is rapidly increasing but people's healthspan is not being sustained. Activities of daily living and Montreal Cognitive Assessment scores from the first wave of a large nationally representative longitudinal study in ageing (TILDA) were analysed using multiple correspondence analysis, k-means clustering, network analysis and association rules mining, to find latent patterns in the data and categorise disability among older adults. It was observed that 6.2% of the population had a greater degree of frailty, specifically cognitive impairment. Additionally, the overall population showed difficulty in performing physically demanding activities. Thus, self-reported ADLs have a diagnostic importance as they indicate the level of cognitive and physical functional decline in the older population.
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170
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Primorac D, Bach-Rojecky L, Vađunec D, Juginović A, Žunić K, Matišić V, Skelin A, Arsov B, Boban L, Erceg D, Ivkošić IE, Molnar V, Ćatić J, Mikula I, Boban L, Primorac L, Esquivel B, Donaldson M. Pharmacogenomics at the center of precision medicine: challenges and perspective in an era of Big Data. Pharmacogenomics 2020; 21:141-156. [PMID: 31950879 DOI: 10.2217/pgs-2019-0134] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Pharmacogenomics (PGx) is one of the core elements of personalized medicine. PGx information reduces the likelihood of adverse drug reactions and optimizes therapeutic efficacy. St Catherine Specialty Hospital in Zagreb/Zabok, Croatia has implemented a personalized patient approach using the RightMed® Comprehensive PGx panel of 25 pharmacogenes plus Facor V Leiden, Factor II and MTHFR genes, which is interpreted by a special counseling team to offer the best quality of care. With the advent of significant technological advances comes another challenge: how can we harness the data to inform clinically actionable measures and how can we use it to develop better predictive risk models? We propose to apply the principles artificial intelligence to develop a medication optimization platform to prevent, manage and treat different diseases.
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Affiliation(s)
- Dragan Primorac
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,University of Split School of Medicine, 21 000 Split, Croatia.,Eberly College of Science, 517 Thomas St, State College, Penn State University, PA 16803, USA.,The Henry C Lee College of Criminal Justice & Forensic Sciences, University of New Haven, West Haven, CT 06516, USA.,University of Osijek School of Medicine, 31000 Osijek, Croatia.,University of Rijeka School of Medicine, 51000 Rijeka, Croatia.,Srebrnjak Children's Hospital, 10000 Zagreb, Croatia.,University of Osijek Faculty of Dental Medicine & Health, 31000 Osijek, Croatia
| | - Lidija Bach-Rojecky
- University of Zagreb Faculty of Pharmacy & Biochemistry, 10000 Zagreb, Croatia
| | - Dalia Vađunec
- University of Zagreb Faculty of Pharmacy & Biochemistry, 10000 Zagreb, Croatia
| | - Alen Juginović
- University of Split School of Medicine, 21 000 Split, Croatia
| | | | - Vid Matišić
- University of Zagreb School of Medicine, 10000 Zagreb, Croatia
| | - Andrea Skelin
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia
| | - Borna Arsov
- University of Zagreb School of Medicine, 10000 Zagreb, Croatia
| | - Luka Boban
- University of Zagreb School of Medicine, 10000 Zagreb, Croatia
| | - Damir Erceg
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,Srebrnjak Children's Hospital, 10000 Zagreb, Croatia.,University of Osijek Faculty of Dental Medicine & Health, 31000 Osijek, Croatia.,Croatian Catholic University, 10000 Zagreb, Croatia
| | - Ivana Erceg Ivkošić
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,University of Osijek Faculty of Dental Medicine & Health, 31000 Osijek, Croatia
| | - Vilim Molnar
- University of Zagreb School of Medicine, 10000 Zagreb, Croatia
| | - Jasmina Ćatić
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,University of Osijek School of Medicine, 31000 Osijek, Croatia.,Clinical Hospital Dubrava, Department of Cardiology, 10000 Zagreb, Croatia
| | - Ivan Mikula
- St Catherine Specialty Hospital, 10000 Zagreb & 49210 Zabok, Croatia.,University North, Nursing Department, 42000 Varaždin, Croatia
| | | | - Lara Primorac
- Wharton Business School, University of Pennsylvania, Philadelphia, PA 19104, USA
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171
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Golriz Khatami S, Robinson C, Birkenbihl C, Domingo-Fernández D, Hoyt CT, Hofmann-Apitius M. Challenges of Integrative Disease Modeling in Alzheimer's Disease. Front Mol Biosci 2020; 6:158. [PMID: 31993440 PMCID: PMC6971060 DOI: 10.3389/fmolb.2019.00158] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Christine Robinson
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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172
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Porumb M, Stranges S, Pescapè A, Pecchia L. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep 2020; 10:170. [PMID: 31932608 PMCID: PMC6957484 DOI: 10.1038/s41598-019-56927-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 01/21/2023] Open
Abstract
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.
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Affiliation(s)
- Mihaela Porumb
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - Saverio Stranges
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Family Medicine, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Population Health, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Antonio Pescapè
- Department of Electrical Engineering, University of Napoli "Federico II", Naples, Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
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173
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Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett 2019; 471:61-71. [PMID: 31830558 DOI: 10.1016/j.canlet.2019.12.007] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/04/2019] [Accepted: 12/06/2019] [Indexed: 02/06/2023]
Abstract
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.
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Affiliation(s)
- Shigao Huang
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China
| | - Jie Yang
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, China.
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China.
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174
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175
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Berns A, Ringborg U, Eggermont A, Baumann M, Calvo F, Eggert A, Espina C, Hanahan D, Lacombe D, de Lorenzo F, Oberst S, Philip T, Schüz J, Tabernero J, Celis JE. Towards a Cancer Mission in Horizon Europe. Mol Oncol 2019; 13:2301-2304. [PMID: 31670486 PMCID: PMC6822240 DOI: 10.1002/1878-0261.12585] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- Anton Berns
- The Netherlands Cancer Institute, Amsterdam, The Netherlands
- European Academy of Cancer Sciences
| | - Ulrik Ringborg
- European Academy of Cancer Sciences
- Cancer Center Karolinska, Karolinska University Hospital, Stockholm, Sweden
| | | | - Michael Baumann
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Fabien Calvo
- Gustave Roussy Cancer Campus Grand Paris, Villejuif, France
| | | | - Carolina Espina
- International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Douglas Hanahan
- Swiss Institute for Experimental Cancer Research (ISREC), Federal Institute of Technology in Lausanne (EPFL), and Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | | | | | - Simon Oberst
- Cancer Research UK Cambridge Centre, UK
- Organisation of European Cancer Institutes (OECI)
| | - Thierry Philip
- Organisation of European Cancer Institutes (OECI)
- Institut Curie, Paris, France
| | - Joachim Schüz
- International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Josep Tabernero
- Medical Oncology Department, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Universitat Autonoma de Barcelona, Spain
| | - Julio E Celis
- European Academy of Cancer Sciences
- Danish Cancer Society Research Centre, Copenhagen, Denmark
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176
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Dong Y, Xu L, Fan Y, Xiang P, Gao X, Chen Y, Zhang W, Ge Q. A novel surgical predictive model for Chinese Crohn's disease patients. Medicine (Baltimore) 2019; 98:e17510. [PMID: 31725605 PMCID: PMC6867775 DOI: 10.1097/md.0000000000017510] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Due to the complexity of Crohn's disease (CD), it is difficult to predict disease course with a single stratification factor or biomarker. A logistic regression (LR) model has been proposed by Guizzetti et al to stratify patients with CD-related surgical risk, which could help decision-making on disease treatment. However, there are no reports on relevant studies on Chinese population. The aim of the study is to present and validate a novel surgical predictive model to facilitate therapeutic decision-making for Chinese CD patients. Data was extracted from retrospective full-mode electronic medical records, which contained 239 CD patients and 1524 instances. Two sub-datasets were generated according to different attribute selection strategies, both of which were split into training and testing sets randomly. The imbalanced data in the training sets was addressed by synthetic minority over-sampling technique (SMOTE) algorithm before model development. Seven predictive models were employed using 5 popular machine learning algorithms: random forest (RF), LR, support vector machine (SVM), decision tree (DT) and artificial neural networks (ANN). The performance of each model was evaluated by accuracy, precision, F1-score, true negative (TN) rate, and the area under the receiver operating characteristic curve (AuROC). The result revealed that RF outperformed all other baseline models on both sub-datasets. The 10 leading risk factors for CD-related surgery returned from RF for attribute ranking were changes of radiology, presence of a fistula, presence of an abscess, no infliximab use, enteroscopy findings, C-reactive protein, abdominal pain, white blood cells, erythrocyte sedimentation rate and platelet count. The proposed machine learning model can accurately predict the risk of surgical intervention in Chinese CD patients, which could be used to tailor and modify the treatment strategies for CD patients in clinical practice.
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Affiliation(s)
| | - Li Xu
- Department of Anorectal Surgery
| | | | - Ping Xiang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of TCM, Zhejiang International Exchange Center of Clinical TCM
| | - Xuning Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of TCM, Zhejiang International Exchange Center of Clinical TCM
| | - Yong Chen
- School of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China
| | - Wenyu Zhang
- School of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China
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177
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Hansen C, Sanchez-Ferro A, Maetzler W. How Mobile Health Technology and Electronic Health Records Will Change Care of Patients with Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2019; 8:S41-S45. [PMID: 30584169 PMCID: PMC6311372 DOI: 10.3233/jpd-181498] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Care of patients with Parkinson’s disease (PD) will dramatically change in the upcoming years. The nationwide implementations of the patient-controlled electronic health record (EHR) and the technology-based home monitoring system will most probably be the cornerstones of this revolution. We speculate that, within the course of the next decade, EHRs will lead to a substantial empowerment of patients, and monitoring of motor and non-motor manifestations of PD will shift from the clinic to the home. As far as this can be foreseen, small, partly clothing-embedded and implanted sensor systems allowing passive (i.e., non-obtrusive) data collection will dominate the market. They will interoperate with the personal EHR and other potentially health-related electronic databases such as clinical warehouses and population health analytics platforms. Analysis software will be mainly built on artificial intelligence, and presentation of data will be intuitive. This scenario will eventually help both the patient and the medical professional by providing higher amounts of quality information about daily-relevant effects of disease and treatment, eventually allowing for a better and more personalized care.
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Affiliation(s)
- Clint Hansen
- Department of Neurology, Christian-Albrechts-Universität Kiel and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Alvaro Sanchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, Móstoles, Madrid, Spain
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts-Universität Kiel and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
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178
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Le Corre PA. Prescriptome analytics: an opportunity for clinical pharmacy. Int J Clin Pharm 2019; 41:1394-1397. [PMID: 31531814 DOI: 10.1007/s11096-019-00900-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 09/05/2019] [Indexed: 11/26/2022]
Abstract
Clinical pharmacists have unique opportunities to be more involved in prescriptome analytics to expand research horizon in clinical pharmacy as an academic discipline. The development of predictive analytics with machine learning algorithms could have the potential to redesign the way we care for patients in our institutions for a more personalized medication therapy.
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Affiliation(s)
- Pascal A Le Corre
- Pôle Pharmacie, Service Hospitalo-Universitaire de Pharmacie, CHU de Rennes, 35033, Rennes, France.
- Laboratoire de Biopharmacie et Pharmacie Clinique, Faculté de Pharmacie, Université de Rennes 1, 35043, Rennes, France.
- Univ Rennes, CHU Rennes, Inserm, EHESP, Irset - UMR_S 1085, 35000, Rennes, France.
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179
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Schoeberl B. Quantitative Systems Pharmacology models as a key to translational medicine. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.10.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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180
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Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol 2019; 16:601-607. [PMID: 31555327 PMCID: PMC6748901 DOI: 10.11909/j.issn.1671-5411.2019.08.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 07/24/2019] [Accepted: 07/26/2019] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) is a software solution with the ability of making predictions without prior explicit programming, aiding in the analysis of large amounts of data. These algorithms can be trained through supervised or unsupervised learning. Cardiology is one of the fields of medicine with the highest interest in its applications. They can facilitate every step of patient care, reducing the margin of error and contributing to precision medicine. In particular, ML has been proposed for cardiac imaging applications such as automated computation of scores, differentiation of prognostic phenotypes, quantification of heart function and segmentation of the heart. These tools have also demonstrated the capability of performing early and accurate detection of anomalies in electrocardiographic exams. ML algorithms can also contribute to cardiovascular risk assessment in different settings and perform predictions of cardiovascular events. Another interesting research avenue in this field is represented by genomic assessment of cardiovascular diseases. Therefore, ML could aid in making earlier diagnosis of disease, develop patient-tailored therapies and identify predictive characteristics in different pathologic conditions, leading to precision cardiology.
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Affiliation(s)
- Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Teresa Perillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Eliana De Rosa
- Department of Translational Medical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
| | - Mario Petretta
- Department of Translational Medical Sciences, University of Naples “Federico II”, Naples, Italy
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181
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McCarthy TW, Chou HC, Brendel VP. SRAssembler: Selective Recursive local Assembly of homologous genomic regions. BMC Bioinformatics 2019; 20:371. [PMID: 31266441 PMCID: PMC6604332 DOI: 10.1186/s12859-019-2949-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 06/13/2019] [Indexed: 11/16/2022] Open
Abstract
Background The falling cost of next-generation sequencing technology has allowed deep sequencing across related species and of individuals within species. Whole genome assemblies from these data remain high time- and resource-consuming computational tasks, particularly if best solutions are sought using different assembly strategies and parameter sets. However, in many cases, the underlying research questions are not genome-wide but rather target specific genes or sets of genes. We describe a novel assembly tool, SRAssembler, that efficiently assembles only contigs containing potential homologs of a gene or protein query, thus enabling gene-specific genome studies over large numbers of short read samples. Results We demonstrate the functionality of SRAssembler with examples largely drawn from plant genomics. The workflow implements a recursive strategy by which relevant reads are successively pulled from the input sets based on overlapping significant matches, resulting in virtual chromosome walking. The typical workflow behavior is illustrated with assembly of simulated reads. Applications to real data show that SRAssembler produces homologous contigs of equivalent quality to whole genome assemblies. Settings can be chosen to not only assemble presumed orthologs but also paralogous gene loci in distinct contigs. A key application is assembly of the same locus in many individuals from population genome data, which provides assessment of structural variation beyond what can be inferred from read mapping to a reference genome alone. SRAssembler can be used on modest computing resources or used in parallel on high performance computing clusters (most easily by invoking a dedicated Singularity image). Conclusions SRAssembler offers an efficient tool to complement whole genome assembly software. It can be used to solve gene-specific research questions based on large genomic read samples from multiple sources and would be an expedient choice when whole genome assembly from the reads is either not feasible, too costly, or unnecessary. The program can also aid decision making on the depth of sequencing in an ongoing novel genome sequencing project or with respect to ultimate whole genome assembly strategies. Electronic supplementary material The online version of this article (10.1186/s12859-019-2949-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Thomas W McCarthy
- Department of Biology, Indiana University, Bloomington, 47405, Indiana, USA
| | - Hsien-Chao Chou
- Department of Oncology, St Jude Children's Research Hospital, Memphis, 38105, Tennessee, USA
| | - Volker P Brendel
- Department of Biology, Indiana University, Bloomington, 47405, Indiana, USA. .,Department of Computer Science, Indiana University, Bloomington, 47405, Indiana, USA.
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182
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Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging. Cancers (Basel) 2019; 11:cancers11060829. [PMID: 31207930 PMCID: PMC6627902 DOI: 10.3390/cancers11060829] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 06/06/2019] [Accepted: 06/11/2019] [Indexed: 02/07/2023] Open
Abstract
Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic characterization of tissue, and (3) prognostication. We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities.
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183
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Affiliation(s)
- Ivan Y. Iourov
- Yurov's Laboratory of Molecular Genetics and Cytogenomics of the Brain, Mental Health Research Center, Moscow 117152, Russian Federation
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184
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Lavigne M, Mussa F, Creatore MI, Hoffman SJ, Buckeridge DL. A population health perspective on artificial intelligence. Healthc Manage Forum 2019; 32:173-177. [PMID: 31106580 PMCID: PMC7323781 DOI: 10.1177/0840470419848428] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly
impact the public’s health. Yet, to make the most of this opportunity, decision-makers
must understand AI concepts. In this article, we describe approaches and fields within AI
and illustrate through examples how they can contribute to informed decisions, with a
focus on population health applications. We first introduce core concepts needed to
understand modern uses of AI and then describe its sub-fields. Finally, we examine four
sub-fields of AI most relevant to population health along with examples of available tools
and frameworks. Artificial intelligence is a broad and complex field, but the tools that
enable the use of AI techniques are becoming more accessible, less expensive, and easier
to use than ever before. Applications of AI have the potential to assist clinicians,
health system managers, policy-makers, and public health practitioners in making more
precise, and potentially more effective, decisions.
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Affiliation(s)
- Maxime Lavigne
- 1 Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada.,2 Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Fatima Mussa
- 3 CIHR Institute of Population and Public Health, Canadian Institutes of Health Research, Toronto, Ontario, Canada
| | - Maria I Creatore
- 3 CIHR Institute of Population and Public Health, Canadian Institutes of Health Research, Toronto, Ontario, Canada.,4 Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Steven J Hoffman
- 3 CIHR Institute of Population and Public Health, Canadian Institutes of Health Research, Toronto, Ontario, Canada.,5 Global Strategy Lab, York University, Toronto, Canada.,6 Dahdaleh Institute for Global Health Research, Faculty of Health and Osgoode Hall Law School, York University, Toronto, Ontario, Canada
| | - David L Buckeridge
- 1 Surveillance Lab, McGill Clinical and Health Informatics, McGill University, Montreal, Quebec, Canada.,2 Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
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185
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Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP, Klein JJ, Hickey AJ, Clark AM. Exploiting machine learning for end-to-end drug discovery and development. NATURE MATERIALS 2019; 18:435-441. [PMID: 31000803 PMCID: PMC6594828 DOI: 10.1038/s41563-019-0338-z] [Citation(s) in RCA: 230] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 03/07/2019] [Indexed: 05/20/2023]
Abstract
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA.
| | - Ana C Puhl
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
| | | | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
| | - Daniel P Russo
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA
| | | | - Anthony J Hickey
- RTI International, Research Triangle Park, NC, USA
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alex M Clark
- Molecular Materials Informatics, Inc., Montreal, Quebec, Canada
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186
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Neumuth T, Franke S. Clear oxygen-level forecasts during anaesthesia. Nat Biomed Eng 2019; 2:715-716. [PMID: 31015648 DOI: 10.1038/s41551-018-0313-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Thomas Neumuth
- ICCAS Innovation Center, Medical School, Universität Leipzig, Leipzig, Germany.
| | - Stefan Franke
- ICCAS Innovation Center, Medical School, Universität Leipzig, Leipzig, Germany
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187
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Espay AJ, Hausdorff JM, Sánchez-Ferro Á, Klucken J, Merola A, Bonato P, Paul SS, Horak FB, Vizcarra JA, Mestre TA, Reilmann R, Nieuwboer A, Dorsey ER, Rochester L, Bloem BR, Maetzler W. A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies. Mov Disord 2019; 34:657-663. [PMID: 30901495 DOI: 10.1002/mds.27671] [Citation(s) in RCA: 174] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 02/02/2019] [Accepted: 02/28/2019] [Indexed: 12/16/2022] Open
Abstract
Obtaining reliable longitudinal information about everyday functioning from individuals with Parkinson's disease (PD) in natural environments is critical for clinical care and research. Despite advances in mobile health technologies, the implementation of digital outcome measures is hindered by a lack of consensus on the type and scope of measures, the most appropriate approach for data capture (eg, in clinic or at home), and the extraction of timely information that meets the needs of patients, clinicians, caregivers, and health care regulators. The Movement Disorder Society Task Force on Technology proposes the following objectives to facilitate the adoption of mobile health technologies: (1) identification of patient-centered and clinically relevant digital outcomes; (2) selection criteria for device combinations that offer an acceptable benefit-to-burden ratio to patients and that deliver reliable, clinically relevant insights; (3) development of an accessible, scalable, and secure platform for data integration and data analytics; and (4) agreement on a pathway for approval by regulators, adoption into e-health systems and implementation by health care organizations. We have developed a tentative roadmap that addresses these needs by providing the following deliverables: (1) results and interpretation of an online survey to define patient-relevant endpoints, (2) agreement on the selection criteria for use of device combinations, (3) an example of an open-source platform for integrating mobile health technology output, and (4) recommendations for assessing readiness for deployment of promising devices and algorithms suitable for regulatory approval. This concrete implementation guidance, harmonizing the collaborative endeavor among stakeholders, can improve assessments of individuals with PD, tailor symptomatic therapy, and enhance health care outcomes. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Alberto J Espay
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio, USA
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Department of Physical Therapy, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Rush Alzheimer's Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, Illinois, USA
| | | | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.,Fraunhofer Institut for Integrated Circuits, Digital Health Pathway Research Group, Erlangen, Germany
| | - Aristide Merola
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio, USA
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - Serene S Paul
- Discipline of Physiotherapy, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, Portland Veterans Affairs Medical System, Portland, Oregon, USA.,APDM, Inc, Portland, Oregon, USA
| | - Joaquin A Vizcarra
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio, USA
| | - Tiago A Mestre
- Parkinson's Disease and Movement Disorders Center, Division of Neurology, Department of Medicine, The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Ralf Reilmann
- George-Huntington-Institute, Technology Park, Muenster, Germany.,Department of Radiology, University of Muenster, Muenster, Germany.,Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Alice Nieuwboer
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Lynn Rochester
- Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.,Newcastle upon Tyne Hospitals National Health Service Foundation Trust, Newcastle upon Tyne, UK
| | - Bastiaan R Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, The Netherlands
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts University, Kiel, Germany
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188
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Kopczynski D, Bittremieux W, Bouyssié D, Dorfer V, Locard-Paulet M, Van Puyvelde B, Schwämmle V, Soggiu A, Willems S, Uszkoreit J. Proceedings of the EuBIC Winter School 2019. EUPA OPEN PROTEOMICS 2019; 22-23:4-7. [PMID: 31890545 PMCID: PMC6924290 DOI: 10.1016/j.euprot.2019.07.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 07/17/2019] [Indexed: 01/29/2023]
Abstract
The 2019 European Bioinformatics Community (EuBIC) Winter School was held from January 15th to January 18th 2019 in Zakopane, Poland. This year's meeting was the third of its kind and gathered international researchers in the field of (computational) proteomics to discuss (mainly) challenges in proteomics quantification and data independent acquisition (DIA). Here, we present an overview of the scientific program of the 2019 EuBIC Winter School. Furthermore, we can already give a small outlook to the upcoming EuBIC 2020 Developer's Meeting.
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Affiliation(s)
- Dominik Kopczynski
- Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V., Bunsen-Kirchhoff-Str. 11, D-44139, Dortmund, Germany
| | | | - David Bouyssié
- Institute of Pharmacology and Structural Biology, University of Toulouse, CNRS, UPS, Toulouse, France
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg, Austria
| | - Marie Locard-Paulet
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen. Denmark1
| | - Bart Van Puyvelde
- Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium
| | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark
| | - Alessio Soggiu
- Department of Veterinary Medicine, University of Milan, Milan, Italy
| | - Sander Willems
- Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium
| | - Julian Uszkoreit
- Ruhr University Bochum, Faculty of Medicine, Medizinisches Proteom-Center, Gesundheitscampus 4, D-44801, Bochum, Germany
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189
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Affiliation(s)
- Charles Auffray
- European Institute for Systems Biology and Medicine (EISBM), Vourles, France.
| | - Julian L Griffin
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Sanger Building, Tennis Court Road, Cambridge, CB2 1GA, UK
- Computational and Systems Medicine, Department of Surgery and Oncology, Imperial College London, London, SW7 2AZ, UK
| | - Muin J Khoury
- Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA, 30329, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Baylor Plaza, Houston, TX, 77030, USA
| | - Matthias Schwab
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Auerbachstraße, 70376, Stuttgart, Germany
- Department of Clinical Pharmacology, University Hospital Tübingen, Auf der Morgenstelle, 72076, Tübingen, Germany
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190
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Iourov IY. Cytopostgenomics: What is it and how does it work? Curr Genomics 2019; 20:77-78. [PMID: 31555057 PMCID: PMC6728900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Ivan Y. Iourov
- Yurov’s Laboratory of Molecular Genetics and Cytogenomics of the Brain, Mental Health Research Center, Moscow 117152, Russia
- Laboratory of Molecular Cytogenetics of Neuropsychiatric Diseases, Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Moscow 125412, Russia
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191
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