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7.27 Principles of Human Disease (MIT) 7.27 Principles of Human Disease (MIT)

Description

This course covers current understanding of, and modern approaches to human disease, emphasizing the molecular and cellular basis of both genetic disease and cancer. Topics include: The Genetics of Simple and Complex Traits; Karyotypic Analysis and Positional Cloning; Genetic Diagnosis; The Roles of Oncogenes and Tumor Suppressors in Tumor Initiation, Progression, and Treatment; The Interaction between Genetics and Environment; Animal Models of Human Disease; Cancer; and Conventional and Gene Therapy Treatment Strategies. This course covers current understanding of, and modern approaches to human disease, emphasizing the molecular and cellular basis of both genetic disease and cancer. Topics include: The Genetics of Simple and Complex Traits; Karyotypic Analysis and Positional Cloning; Genetic Diagnosis; The Roles of Oncogenes and Tumor Suppressors in Tumor Initiation, Progression, and Treatment; The Interaction between Genetics and Environment; Animal Models of Human Disease; Cancer; and Conventional and Gene Therapy Treatment Strategies.

Subjects

human disease | human disease | molecular basis of genetic disease | molecular basis of genetic disease | molecular basis of cancer | molecular basis of cancer | cellular basis of genetic disease | cellular basis of genetic disease | cellular basis of cancer | cellular basis of cancer | genetics of simple and complex traits | genetics of simple and complex traits | karyotypic analysis | karyotypic analysis | positional cloning | positional cloning | genetic diagnosis | genetic diagnosis | roles of oncogenes | roles of oncogenes | tumor suppressors | tumor suppressors | tumor initiation | tumor initiation | tumor progression | tumor progression | tumor treatment | tumor treatment | interaction between genetics and environment | interaction between genetics and environment | animal models of human disease | animal models of human disease | cancer | cancer | conventional treatment strategies | conventional treatment strategies | gene therapy treatment strategies | gene therapy treatment strategies

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Fighting malaria in Myanmar

Description

Professor Frank Smithuis is the director of MOCRU, Myanmar Oxford Clinical Research Unit. MOCRU involves a network of 6 clinics and 650 community health workers in remote areas. Up until now, Myanmar has spent little on heathcare and receive little assistance from rich countries. Prevention is difficult, which leaves diagnosis and treatment. MOCRU has set up a network of community health workers, trained and supplied with diagnostics and treatments, to help improve access to healthcare for remote communities. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

malaria | myanmar | clinical research | community health | diagnosis | treatment | malaria | myanmar | clinical research | community health | diagnosis | treatment

License

http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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20.453J Biomedical Information Technology (MIT) 20.453J Biomedical Information Technology (MIT)

Description

This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system desig This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system desig

Subjects

20.453 | 20.453 | 2.771 | 2.771 | HST.958 | HST.958 | imaging | imaging | medical imaging | medical imaging | metadata | metadata | molecular biology | molecular biology | medical records | medical records | DICOM | DICOM | RDF | RDF | OWL | OWL | SPARQL | SPARQL | SBML | SBML | CellML | CellML | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | computational biology | computational biology | clinical decision support | clinical decision support | clinical trial | clinical trial | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | pathway modeling | pathway modeling | XML | XML | SQL | SQL | relational database | relational database | biological data | biological data | ontologies | ontologies | drug development | drug development | drug discovery | drug discovery | drug target | drug target | pharmaceutical | pharmaceutical | gene sequencing | gene sequencing

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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20.453J Biomedical Information Technology (BE.453J) (MIT) 20.453J Biomedical Information Technology (BE.453J) (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | imaging | medical imaging | medical imaging | metadata | metadata | medical record | medical record | DICOM | DICOM | computer architecture | computer architecture | client-server architecture | client-server architecture | SEM | SEM | TEM | TEM | OME | OME | RDF | RDF | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | clinical decision support | clinical decision support | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | 20.453 | 20.453 | 2.771 | 2.771 | HST.958 | HST.958

License

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

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HST.582J Biomedical Signal and Image Processing (MIT) HST.582J Biomedical Signal and Image Processing (MIT)

Description

This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs. This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.

Subjects

HST.582 | HST.582 | 6.555 | 6.555 | 16.456 | 16.456 | signal processing | signal processing | medicine | medicine | biological signal | biological signal | diagnosis | diagnosis | diagnostic tool | diagnostic tool | physiology | physiology | cardiology | cardiology | speech recognition | speech recognition | speech processing | speech processing | imaging | imaging | medical imaging | medical imaging | MRI | MRI | ultrasound | ultrasound | ECG | ECG | electrocardiogram | electrocardiogram | fourier | fourier | FFT | FFT | applications of probabilitym | applications of probabilitym | noise | noise | MATLAB | MATLAB | digital filter | digital filter | DSP | DSP

License

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Personalised Medicine

Description

Dr Jenny Taylor is the Programme Director for the Genomic Medicine Theme, Wellcome Trust Centre for Human Genetics. Her research bridges the gap between genetics research and the use of its discoveries in diagnosis or treatment of medical conditions. Clinical diagnoses can be broad descriptions, but today's test results can help better understand the condition as well as target treatment. Cancer is a good example in which personalised medicine can help decide which molecular targeted therapy is most appropriate. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

genetics | diagnosis | treatment | clinical | personalised | targeted | genetics | diagnosis | treatment | clinical | personalised | targeted

License

http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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HST.161 Molecular Biology and Genetics in Modern Medicine (MIT) HST.161 Molecular Biology and Genetics in Modern Medicine (MIT)

Description

This course provides a foundation for understanding the relationship between molecular biology, developmental biology, genetics, genomics, bioinformatics, and medicine. It develops explicit connections between basic research, medical understanding, and the perspective of patients. Principles of human genetics are reviewed. We translate clinical understanding into analysis at the level of the gene, chromosome and molecule; we cover the concepts and techniques of molecular biology and genomics, and the strategies and methods of genetic analysis, including an introduction to bioinformatics. Material in the course extends beyond basic principles to current research activity in human genetics. This course provides a foundation for understanding the relationship between molecular biology, developmental biology, genetics, genomics, bioinformatics, and medicine. It develops explicit connections between basic research, medical understanding, and the perspective of patients. Principles of human genetics are reviewed. We translate clinical understanding into analysis at the level of the gene, chromosome and molecule; we cover the concepts and techniques of molecular biology and genomics, and the strategies and methods of genetic analysis, including an introduction to bioinformatics. Material in the course extends beyond basic principles to current research activity in human genetics.

Subjects

Genetics | Genetics | genes | genes | genetic disorders | genetic disorders | inborn error | inborn error | muscular dystrophy | muscular dystrophy | PKU | PKU | phenylketoneuria | phenylketoneuria | cancer | cancer | tumors | tumors | gene therapy | gene therapy | disease | disease | birth defects | birth defects | chromosomes | chromosomes | leukemia | leukemia | RNAi | RNAi | hemophilia | hemophilia | thalassemia | thalassemia | deafness | deafness | mutations | mutations | hypertrophic cardiomyopathy | hypertrophic cardiomyopathy | epigenetics | epigenetics | rett syndrome | rett syndrome | prenatal diagnosis | prenatal diagnosis | LOD scores | LOD scores | gene linkage | gene linkage | mitochondrial disorders | mitochondrial disorders | degenerative disorders | degenerative disorders | complex traits | complex traits | Mendelian inheritance | Mendelian inheritance

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Molecular diagnosis and bacterial genotyping

Description

Dr Janjira Thaipadungpanit from our MORU unit in Bangkok, Thailand, tells us about her research on molecular diagnosis and bacterial genotyping A molecular microbiologist, Dr Janjira?s research focusses on using bacterial typing based on genome to confirm which disease is present in a patient. She aims to develop a single whole genome sequence type test using mutliple-PCR assays that can determine from a single sample of blood what bacteria or viruses are present in a patient?s blood ? thereby speeding up diagnosis and potentially saving lives in resource-limited settings. Head of Molecular Microbiology at MORU, Dr Janjira Thaipadungpanit?s research interests include the molecular epidemiology of leptospirosis and melioidosis using multilocus sequence typing or genome data and mol Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

translational medicine | Global health | diagnosis | genotyping | translational medicine | Global health | diagnosis | genotyping

License

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BE.453J Biomedical Information Technology (MIT) BE.453J Biomedical Information Technology (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | imaging | medical imaging | medical imaging | metadata | metadata | medical record | medical record | DICOM | DICOM | computer architecture | computer architecture | client-server architecture | client-server architecture | SEM | SEM | TEM | TEM | OME | OME | RDF | RDF | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | clinical decision support | clinical decision support | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | 2.771J | 2.771J | 2.771 | 2.771 | HST.958J | HST.958J | HST.958 | HST.958

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Energy resources: Geothermal energy Energy resources: Geothermal energy

Description

Energy from sources other than fossil and nuclear fuels is, to a large extent, free of the concerns about environmental effects and renewability that characterise those two sources. Each alternative source supplies energy continually, whether or not we use it. This free course, Energy resources: Geothermal energy, considers one of these alternative sources, geothermal energy, derived from the interior heat of the Earth, and the potential for this alternative to supplant fossil and nuclear fuel to power social needs fast enough to avoid the likelihood of future global warming and other kinds of pollution. First published on Tue, 22 Mar 2016 as Energy resources: Geothermal energy. To find out more visit The Open University's Openlearn website. Creative-Commons 2016 Energy from sources other than fossil and nuclear fuels is, to a large extent, free of the concerns about environmental effects and renewability that characterise those two sources. Each alternative source supplies energy continually, whether or not we use it. This free course, Energy resources: Geothermal energy, considers one of these alternative sources, geothermal energy, derived from the interior heat of the Earth, and the potential for this alternative to supplant fossil and nuclear fuel to power social needs fast enough to avoid the likelihood of future global warming and other kinds of pollution. First published on Tue, 22 Mar 2016 as Energy resources: Geothermal energy. To find out more visit The Open University's Openlearn website. Creative-Commons 2016

Subjects

Environmental Science | Environmental Science | diagnosis | diagnosis

License

Except for third party materials and otherwise stated (see http://www.open.ac.uk/conditions terms and conditions), this content is made available under a http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ Creative Commons Attribution-NonCommercial-ShareAlike 2.0 Licence Licensed under a Creative Commons Attribution - NonCommercial-ShareAlike 2.0 Licence - see http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ - Original copyright The Open University

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Malaria laboratory at MORU

Description

Dr Kesinee Chotivanich's laboratory provides facilities and resources to researchers, students, and collaborators who are interested in tropical diseases, with the aim to improve patients? care. More effective diagnosis and treatments are needed to reduce the morbidity and mortality affecting malaria patients. Researchers at the Malaria Laboratory at MORU study the pathophysiology of the disease, and test new compound drugs for anti-malarial activity. In the context of growing artemisinin resistance, this research will have a global impact. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

tropical | disease | diagnosis | treatment | malaria | pathophysiology | artemisinin resistance | tropical | disease | diagnosis | treatment | malaria | pathophysiology | artemisinin resistance

License

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Microbiology research in SE Asia

Description

Dr Direk Limmathurotsakul's research focuses on the epidemiology of melioidosis, a bacterial infection caused by Burkholderia pseudomallei. Melioidosis is endemic in at least 45 countries, but greatly under-reported. Up to 50% of cases seen in hospital die. Our researchers at MORU have produced a rapid diagnostic test that aims to improve both diagnosis and public awareness. Better coordination between researchers and policy makers is needed to face upcoming emerging infectious diseases. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

melioidosis | Epidemiology | baterial infection | diagnosis | public awareness | melioidosis | Epidemiology | baterial infection | diagnosis | public awareness

License

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HST.071 Human Reproductive Biology (MIT) HST.071 Human Reproductive Biology (MIT)

Description

This course is designed to give the student a clear understanding of the pathophysiology of the menstrual cycle, fertilization, implantation, ovum growth development, differentiation and associated abnormalities. Disorders of fetal development including the principles of teratology and the mechanism of normal and abnormal parturition will be covered as well as the pathophysiology of the breast and disorders of lactation. Fetal asphyxia and its consequences will be reviewed with emphasis on the technology currently available for its detection. In addition the conclusion of the reproductive cycle, menopause, and the use of hormonal replacement will be covered. This course is designed to give the student a clear understanding of the pathophysiology of the menstrual cycle, fertilization, implantation, ovum growth development, differentiation and associated abnormalities. Disorders of fetal development including the principles of teratology and the mechanism of normal and abnormal parturition will be covered as well as the pathophysiology of the breast and disorders of lactation. Fetal asphyxia and its consequences will be reviewed with emphasis on the technology currently available for its detection. In addition the conclusion of the reproductive cycle, menopause, and the use of hormonal replacement will be covered.

Subjects

clinical case | clinical case | physiology | physiology | endocrinology | endocrinology | pathology | pathology | human reproduction | human reproduction | quantitative analysis | quantitative analysis | reproductive technology | reproductive technology | reproduction | reproduction | prenatal diagnosis | prenatal diagnosis | in vitro fertilization | in vitro fertilization | abortion | abortion | menopause | menopause | contraception | contraception | reproductive biology | reproductive biology | menstrual cycle | menstrual cycle | fertility | fertility | impotence | impotence | anatomy | anatomy | sexual differentiation | sexual differentiation | sex | sex | pregnancy | pregnancy

License

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HST.951J Medical Decision Support (MIT) HST.951J Medical Decision Support (MIT)

Description

This course presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. The advantages and disadvantages of using these methods in real-world systems are emphasized, while students gain hands-on experience with application specific methods. The technical focus of the course includes decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems. This course presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. The advantages and disadvantages of using these methods in real-world systems are emphasized, while students gain hands-on experience with application specific methods. The technical focus of the course includes decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems.

Subjects

HST.951 | HST.951 | 6.873 | 6.873 | decision analysis | decision analysis | artificial intelligence | artificial intelligence | predictive model construction | predictive model construction | evaluation | evaluation | medical software | medical software | decision support | decision support | knowledge-based systems | knowledge-based systems | learning systems | learning systems | logistic regression | logistic regression | classification trees | classification trees | neural networks | neural networks | rough sets | rough sets | computer-based diagnosis | computer-based diagnosis | planning monitoring | planning monitoring | therapeutic interventions | therapeutic interventions | machine learning methods | machine learning methods

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Dengue diagnosis and management

Description

With 390 million infections occuring each year, dengue is the most important mosquito-borne viral infection, and no vaccine is currently available. DENGUE The majority of people infected with the dengue virus experience a flu-like febrile illness, but in a small proportion of patients, particularly children, the virus causes the blood vessels to become leaky which can induce shock and lead to death. Improved diagnosis and understanding of the disease process enable better outcomes for patients with severe dengue. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

Dengue | virus | diagnosis | mosquito | infectious diseases | Dengue | virus | diagnosis | mosquito | infectious diseases

License

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HST.947 Medical Artificial Intelligence (MIT) HST.947 Medical Artificial Intelligence (MIT)

Description

This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers. This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers.

Subjects

Introduces representations | techniques | and architectures used to build applied systems | Introduces representations | techniques | and architectures used to build applied systems | computational intelligence | computational intelligence | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | problem-solving paradigms | problem-solving paradigms | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | learning paradigms | learning paradigms | Speculations on the contributions of human vision and language systems to human intelligence | Speculations on the contributions of human vision and language systems to human intelligence | Meets with HST.947 spring only | Meets with HST.947 spring only | 4 Engineering Design Points | 4 Engineering Design Points | artificial intelligence | artificial intelligence | applied systems | applied systems | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systems | diagnosis | diagnosis | clinical simulation | clinical simulation | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Personalised Medicine

Description

Dr Jenny Taylor is the Programme Director for the Genomic Medicine Theme, Wellcome Trust Centre for Human Genetics. Her research bridges the gap between genetics research and the use of its discoveries in diagnosis or treatment of medical conditions. Clinical diagnoses can be broad descriptions, but today's test results can help better understand the condition as well as target treatment. Cancer is a good example in which personalised medicine can help decide which molecular targeted therapy is most appropriate. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

genetics | diagnosis | treatment | clinical | personalised | targeted | genetics | diagnosis | treatment | clinical | personalised | targeted

License

http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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HST.035 Principle and Practice of Human Pathology (MIT) HST.035 Principle and Practice of Human Pathology (MIT)

Description

This course provides a comprehensive overview of human pathology with emphasis on mechanisms of disease and diagnostic medicine. Topics include:Cellular Mechanisms of DiseaseMolecular PathologyPathology of Major Organ SystemsReview of Diagnostic Tools from Traditional Surgical Pathology to Diagnostic SpectroscopyFunctional and Molecular ImagingMolecular DiagnosticsIn addition to lectures, one of the two weekly sessions includes a 2-3 hour laboratory component. Periodically, time will also be devoted to minicases.LecturersProf. Jon AsterProf. Frederick BieberProf. Carlo BrugnaraProf. Robert B. ColvinProf. Christopher CrumProf. Douglas DockeryProf. Mel FeanyProf. Michael FeldProf. Jonathan FletcherProf. Michael GimbroneProf. Todd GolubProf. Frank B. HuProf. Donald IngberProf. Hart LidovProf. This course provides a comprehensive overview of human pathology with emphasis on mechanisms of disease and diagnostic medicine. Topics include:Cellular Mechanisms of DiseaseMolecular PathologyPathology of Major Organ SystemsReview of Diagnostic Tools from Traditional Surgical Pathology to Diagnostic SpectroscopyFunctional and Molecular ImagingMolecular DiagnosticsIn addition to lectures, one of the two weekly sessions includes a 2-3 hour laboratory component. Periodically, time will also be devoted to minicases.LecturersProf. Jon AsterProf. Frederick BieberProf. Carlo BrugnaraProf. Robert B. ColvinProf. Christopher CrumProf. Douglas DockeryProf. Mel FeanyProf. Michael FeldProf. Jonathan FletcherProf. Michael GimbroneProf. Todd GolubProf. Frank B. HuProf. Donald IngberProf. Hart LidovProf.

Subjects

human pathology | human pathology | disease mechanisms | disease mechanisms | cellular pathology | cellular pathology | molecular pathology | molecular pathology | diagnostic tools | diagnostic tools | surgical pathology | surgical pathology | diagnostic spectroscopy | diagnostic spectroscopy | functional imaging | functional imaging | molecular imaging | molecular imaging | molecular diagnostics | molecular diagnostics | medicine | medicine | immune system | immune system | transplantation | transplantation | diagnosis | diagnosis | neoplasia | neoplasia | pathobiology | pathobiology | pathophysiology | pathophysiology

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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HST.951J Medical Decision Support (MIT) HST.951J Medical Decision Support (MIT)

Description

This course presents the main concepts of decision analysis, artificial intelligence and predictive model construction and evaluation in the specific context of medical applications. It emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Its technical focus is on decision support, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks, rough sets), and techniques to evaluate the performance of such systems. It reviews computer-based diagnosis, planning and monitoring of therapeutic interventions. It also discusses implemented medical applications and the software tools used in their construction. Students produce a final project usin This course presents the main concepts of decision analysis, artificial intelligence and predictive model construction and evaluation in the specific context of medical applications. It emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Its technical focus is on decision support, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks, rough sets), and techniques to evaluate the performance of such systems. It reviews computer-based diagnosis, planning and monitoring of therapeutic interventions. It also discusses implemented medical applications and the software tools used in their construction. Students produce a final project usin

Subjects

decision analysis | decision analysis | artificial intelligence | artificial intelligence | predictive model construction | predictive model construction | evaluation | evaluation | medical software | medical software | decision support | decision support | knowledge-based systems | knowledge-based systems | learning systems | learning systems | logistic regression | logistic regression | classification trees | classification trees | neural networks | neural networks | rough sets | rough sets | computer-based diagnosis | computer-based diagnosis | planning monitoring | planning monitoring | therapeutic interventions | therapeutic interventions | machine learning methods | machine learning methods | HST.951 | HST.951 | 6.873 | 6.873

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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HST.071 Human Reproductive Biology (MIT) HST.071 Human Reproductive Biology (MIT)

Description

Lectures, laboratory sessions, and clinical case discussions designed to provide the student with a clear understanding of the physiology, endocrinology, and pathology of human reproduction. Emphasis is on quantitative analytic techniques and the role of technology in reproductive science. The course also involves the student in the wider aspects of reproduction, such as prenatal diagnosis, in vitro fertilization, abortion, menopause, and contraception. Lectures, laboratory sessions, and clinical case discussions designed to provide the student with a clear understanding of the physiology, endocrinology, and pathology of human reproduction. Emphasis is on quantitative analytic techniques and the role of technology in reproductive science. The course also involves the student in the wider aspects of reproduction, such as prenatal diagnosis, in vitro fertilization, abortion, menopause, and contraception.

Subjects

clinical case | clinical case | physiology | physiology | endocrinology | endocrinology | pathology | pathology | human reproduction | human reproduction | quantitative analysis | quantitative analysis | reproductive technology | reproductive technology | reproduction | reproduction | prenatal diagnosis | prenatal diagnosis | in vitro fertilization | in vitro fertilization | abortion | abortion | menopause | menopause | contraception | contraception

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.780 Semiconductor Manufacturing (MIT) 6.780 Semiconductor Manufacturing (MIT)

Description

6.780 covers statistical modeling and the control of semiconductor fabrication processes and plants. Topics covered include: design of experiments, response surface modeling, and process optimization; defect and parametric yield modeling; process/device/circuit yield optimization; monitoring, diagnosis, and feedback control of equipment and processes; and analysis and scheduling of semiconductor manufacturing operations. 6.780 covers statistical modeling and the control of semiconductor fabrication processes and plants. Topics covered include: design of experiments, response surface modeling, and process optimization; defect and parametric yield modeling; process/device/circuit yield optimization; monitoring, diagnosis, and feedback control of equipment and processes; and analysis and scheduling of semiconductor manufacturing operations.

Subjects

Semiconductor manufacturing | Semiconductor manufacturing | statistics | statistics | distributions | distributions | estimation | estimation | hypothesis testing | hypothesis testing | statistical process control | statistical process control | control chart | control chart | control chart design | control chart design | design of experiments | design of experiments | empirical equipment | empirical equipment | process modeling | process modeling | experimental design | experimental design | yield models | yield models | spatial variation | spatial variation | spatial models | spatial models | design for manufacturability | design for manufacturability | equipment monitoring | equipment monitoring | equipment diagnosis | equipment diagnosis | equipment control | equipment control | run by run | run by run | multistage process control | multistage process control | scheduling | scheduling | planning | planning | factory modeling | factory modeling | factory infrastructure | factory infrastructure | environmental | environmental | health and safety | health and safety | computer integrated manufacturing | computer integrated manufacturing | factory operation | factory operation | factory design | factory design | advanced process control | advanced process control | yield learning | yield learning

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Inflammatory Bowel Disease

Description

Description: A focus on presentation, complications and differential diagnosis of inflammatory bowel disease. How to differentiate between Ulcerative Colitis and Crohn's disease.

Subjects

ukoer | ooer | medev | inflammatory bowel disease | ulcerative colitis | crohn's disease | presentation | symptoms | complications | differential diagnosis | diagnosis | dentistry | A000

License

Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-nd/2.0/uk/ http://creativecommons.org/licenses/by-nc-nd/2.0/uk/

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Cancer in Scotland : action for change resource stub

Description

A resource stub for the Cancer in Scotland: action for change website.

Subjects

neoplasms | prevention and control | diagnosis | cancer | scotland | Technology | Diseases | SAFETY | Subjects allied to Medicine | UK EL04 = SCQF 4 | Foundational Level | NICAT 1 | CQFW 1 | Foundation | GCSE D-G | NVQ 1 | Intermediate 1 | | UK EL05 = SCQF 5 | Intermediate level | Intermediate | NICAT 2 | CQFW 2 | Intermediate | GSCE A-C | NVQ 2 | | UK EL06 = SCQF 6 | Advanced courses | | NICAT 3 | CQFW 3 | Advanced | A/AS Level | NVQ 3 | Higher | SVQ 3 | UK EL07 = SCQF 7 | Higher Certificate | NICAT 4 | CQFW 4 | NVQ 4 | Advanced Higher | SVQ 4 | HN Certificate | Learning | Teaching | Students | Subjects allied to medicine | B000 | EDUCATION / TRAINING / TEACHING | HEALTH CARE / MEDICINE / HEALTH and SAFETY | INFORMATION TECHNOLOGY and INFORMATION | G | P | C

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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