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Mathematical Epidemiology, Data Science, and Artificial Intelligence: Measurement, Causation, Familial Risk, Prediction, and Public Decision ... Books from The Scottish Science Society) - Softcover
Book 16 of 22: Mathematical Graduate Books from The Scottish Science Society
Synopsis
This book began from a practical irritation, which is often a more faithful servant of science than serenity. In clinical medicine one repeatedly meets probabilities that have been made too smooth for the patient before us. They are not always false; that would be too simple, and perhaps too generous. They are sometimes true answers to questions that nobody in the consulting room has actually asked. The argument developed here is that mathematical epidemiology, data science, and artificial intelligence can serve medicine only when their quantities are disciplined by their objects. Prevalence is not incidence dressed for dinner. A posterior probability is not a moral instruction to distrust family history. Heritability is not individual destiny, yet neither is it a ceremonial phrase to be bowed out of the room whenever prediction becomes uncomfortable. The clinician who sees families, recurrences, temporal clustering, and accumulating evidence has not abandoned mathematics; she has asked mathematics to keep better company. Accordingly, the book proceeds from measurement to causation, from probability to prediction, from familial liability to artificial intelligence, and finally to governance and judgement. Equations are derived because a formula without ancestry is a small autocrat. Figures are included because many errors in epidemiology are not errors of calculation but errors of sight. Examples and exercises appear because a reader, colleague, clinician, investigator, analyst, sceptic, student, practitioner, statistician, epidemiologist, diagnostician, and patient advocate each deserves a way of testing whether the prose has paid its debts. The style of the work is deliberately argumentative. It grants the achievements of statistical and computational medicine; it also insists that methods acquire authority only after their assumptions have become visible. The book therefore praises mathematics by refusing to flatter it. That is not ingratitude. It is maintenance.
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Mathematical Epidemiology, Data Science, and Artificial Intelligence
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Mathematical Epidemiology, Data Science, and Artificial Intelligence (Paperback)
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Paperback. Condition: new. Paperback. This book began from a practical irritation, which is often a more faithful servant of science than serenity. In clinical medicine one repeatedly meets probabilities that have been made too smooth for the patient before us. They are not always false; that would be too simple, and perhaps too generous. They are sometimes true answers to questions that nobody in the consulting room has actually asked. The argument developed here is that mathematical epidemiology, data science, and artificial intelligence can serve medicine only when their quantities are disciplined by their objects. Prevalence is not incidence dressed for dinner. A posterior probability is not a moral instruction to distrust family history. Heritability is not individual destiny, yet neither is it a ceremonial phrase to be bowed out of the room whenever prediction becomes uncomfortable. The clinician who sees families, recurrences, temporal clustering, and accumulating evidence has not abandoned mathematics; she has asked mathematics to keep better company. Accordingly, the book proceeds from measurement to causation, from probability to prediction, from familial liability to artificial intelligence, and finally to governance and judgement. Equations are derived because a formula without ancestry is a small autocrat. Figures are included because many errors in epidemiology are not errors of calculation but errors of sight. Examples and exercises appear because a reader, colleague, clinician, investigator, analyst, sceptic, student, practitioner, statistician, epidemiologist, diagnostician, and patient advocate each deserves a way of testing whether the prose has paid its debts. The style of the work is deliberately argumentative. It grants the achievements of statistical and computational medicine; it also insists that methods acquire authority only after their assumptions have become visible. The book therefore praises mathematics by refusing to flatter it. That is not ingratitude. It is maintenance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798174388697
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Mathematical Epidemiology, Data Science, and Artificial Intelligence
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Mathematical Epidemiology, Data Science, and Artificial Intelligence: Measurement, Causation, Familial Risk, Prediction, and Public Decision ... Books from The Scottish Science Society)
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Mathematical Epidemiology, Data Science, and Artificial Intelligence (Paperback)
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Paperback. Condition: new. Paperback. This book began from a practical irritation, which is often a more faithful servant of science than serenity. In clinical medicine one repeatedly meets probabilities that have been made too smooth for the patient before us. They are not always false; that would be too simple, and perhaps too generous. They are sometimes true answers to questions that nobody in the consulting room has actually asked. The argument developed here is that mathematical epidemiology, data science, and artificial intelligence can serve medicine only when their quantities are disciplined by their objects. Prevalence is not incidence dressed for dinner. A posterior probability is not a moral instruction to distrust family history. Heritability is not individual destiny, yet neither is it a ceremonial phrase to be bowed out of the room whenever prediction becomes uncomfortable. The clinician who sees families, recurrences, temporal clustering, and accumulating evidence has not abandoned mathematics; she has asked mathematics to keep better company. Accordingly, the book proceeds from measurement to causation, from probability to prediction, from familial liability to artificial intelligence, and finally to governance and judgement. Equations are derived because a formula without ancestry is a small autocrat. Figures are included because many errors in epidemiology are not errors of calculation but errors of sight. Examples and exercises appear because a reader, colleague, clinician, investigator, analyst, sceptic, student, practitioner, statistician, epidemiologist, diagnostician, and patient advocate each deserves a way of testing whether the prose has paid its debts. The style of the work is deliberately argumentative. It grants the achievements of statistical and computational medicine; it also insists that methods acquire authority only after their assumptions have become visible. The book therefore praises mathematics by refusing to flatter it. That is not ingratitude. It is maintenance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798174388697
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Mathematical Epidemiology, Data Science, and Artificial Intelligence : Measurement, Causation, Familial Risk, Prediction, and Public Decision
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Taschenbuch. Condition: Neu. Neuware - This book began from a practical irritation, which is often a more faithful servant of science than serenity. In clinical medicine one repeatedly meets probabilities that have been made too smooth for the patient before us. They are not always false; that would be too simple, and perhaps too generous. They are sometimes true answers to questions that nobody in the consulting room has actually asked. The argument developed here is that mathematical epidemiology, data science, and artificial intelligence can serve medicine only when their quantities are disciplined by their objects. Prevalence is not incidence dressed for dinner. A posterior probability is not a moral instruction to distrust family history. Heritability is not individual destiny, yet neither is it a ceremonial phrase to be bowed out of the room whenever prediction becomes uncomfortable. The clinician who sees families, recurrences, temporal clustering, and accumulating evidence has not abandoned mathematics; she has asked mathematics to keep better company. Accordingly, the book proceeds from measurement to causation, from probability to prediction, from familial liability to artificial intelligence, and finally to governance and judgement. Equations are derived because a formula without ancestry is a small autocrat. Figures are included because many errors in epidemiology are not errors of calculation but errors of sight. Examples and exercises appear because a reader, colleague, clinician, investigator, analyst, sceptic, student, practitioner, statistician, epidemiologist, diagnostician, and patient advocate each deserves a way of testing whether the prose has paid its debts. The style of the work is deliberately argumentative. It grants the achievements of statistical and computational medicine; it also insists that methods acquire authority only after their assumptions have become visible. The book therefore praises mathematics by refusing to flatter it. That is not ingratitude. It is maintenance. Seller Inventory # 9798174388697