Self-Check Quizzes, Physics: Principles and Problems

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Chapter 1: A Physics Toolkit Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz
Chapter 2 Representing Motion Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz Section 4: Self-Check Quiz
Chapter 3 Accelerated Motion Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz
Chapter 4 Forces in One Dimension Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz
Chapter 5 Forces in Two Dimensions Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz
Chapter 6 Motion in Two Dimensions Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz
Chapter 7 Gravitation Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 8 Rotational Motion Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz
Chapter 9 Momentum and Its Conservation Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 10 Energy, Work, and Simple Machines Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 11 Energy and Its Conservation Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 12 Thermal Energy Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 13 States of Matter Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz Section 4: Self-Check Quiz
Chapter 14 Vibrations and Waves Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz
Chapter 15 Sound Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 16 Fundamentals of Light Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 17 Reflection and Mirrors Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 18 Refraction and Lenses Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz
Chapter 19 Interference and Diffraction Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 20 Static Electricity Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 21 Electric Fields Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 22 Current Electricity Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 23 Series and Parallel Circuits Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 24 Magnetic Fields Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 25 Electromagnetic Induction Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 26 Electromagnetism Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 27 Quantum Theory Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 28 The Atom Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 29 Solid-State Electronics Section 1: Self-Check Quiz Section 2: Self-Check Quiz
Chapter 30 Nuclear Physics Section 1: Self-Check Quiz Section 2: Self-Check Quiz Section 3: Self-Check Quiz

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    JAMIA: Making EHR data extraction more exact is possible

    The medical information contained within EHRs is undoubtedly valuable to many of healthcare’s stakeholders, but its presentation as unstructured text often makes locating necessary information difficult, according to a study published July 6 in the Journal of the American Medical Informatics Association.

    Using a novel method to detect named entities within EHRs, however, researchers demonstrated that constructing more effective information extraction systems is possible at low costs.

    Most named entity recognition methods for collecting information from datasets use conditional random field (CRF) recognizers, which rely heavily on local contexts surrounding named entities and assume that similar local contexts lead to the same judgements, according to the study’s author, Eric I-Chao Chang, MD, of Microsoft Research Asia. The problem with data extraction methods relying solely on CRF recognizers is that they often deliver contradictory information.

    Using 20,000 radiology reports as a dataset to test multiple extraction systems’ ability to parse through records and deliver relevant patient follow-up information through an EHR system, Chang determined that pairing a labeled sequential pattern (LSP) classifier with a CRF recognizer was an effective method for filtering out irrelevant information.

    The radiology reports contained a total of 121,748 sentences of which only 3,997 contained follow-up information. A data extraction method using both an LSP classifier and a CRF recognizer was able to reduce inexact matching by 6 percent compared to a data extraction method using just a CRF recognizer.

    “In our method, LSP captures global patterns to choose candidate sentences before CRF identifies NEs [named entities] or relevant phrases,” Chang wrote. “The experiment shows that filtering out a large number of negative examples from the training set by an LSP classifier can significantly improve the performance of a CRF recognizer.”

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