Matches in SemOpenAlex for { <https://semopenalex.org/work/W2548932017> ?p ?o ?g. }
- W2548932017 endingPage "166" @default.
- W2548932017 startingPage "139" @default.
- W2548932017 abstract "The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text—found in biomedical publications and clinical notes—is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine." @default.
- W2548932017 created "2016-11-11" @default.
- W2548932017 creator A5036103922 @default.
- W2548932017 creator A5058959286 @default.
- W2548932017 creator A5083081872 @default.
- W2548932017 date "2016-01-01" @default.
- W2548932017 modified "2023-09-23" @default.
- W2548932017 title "Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health" @default.
- W2548932017 cites W1177723 @default.
- W2548932017 cites W128719466 @default.
- W2548932017 cites W129507607 @default.
- W2548932017 cites W135286464 @default.
- W2548932017 cites W1512404644 @default.
- W2548932017 cites W1550258693 @default.
- W2548932017 cites W1602694398 @default.
- W2548932017 cites W1610821757 @default.
- W2548932017 cites W1759667598 @default.
- W2548932017 cites W1779612606 @default.
- W2548932017 cites W1808652302 @default.
- W2548932017 cites W1850642846 @default.
- W2548932017 cites W1939796399 @default.
- W2548932017 cites W1961457095 @default.
- W2548932017 cites W1964670939 @default.
- W2548932017 cites W1979421770 @default.
- W2548932017 cites W1983651981 @default.
- W2548932017 cites W1987979808 @default.
- W2548932017 cites W2008998978 @default.
- W2548932017 cites W2011726136 @default.
- W2548932017 cites W2011980051 @default.
- W2548932017 cites W2018377599 @default.
- W2548932017 cites W2023153884 @default.
- W2548932017 cites W2024723752 @default.
- W2548932017 cites W2026479502 @default.
- W2548932017 cites W2030390110 @default.
- W2548932017 cites W2030597870 @default.
- W2548932017 cites W2032729847 @default.
- W2548932017 cites W2033853342 @default.
- W2548932017 cites W2034269086 @default.
- W2548932017 cites W2038732679 @default.
- W2548932017 cites W2052639054 @default.
- W2548932017 cites W2053870274 @default.
- W2548932017 cites W2055097264 @default.
- W2548932017 cites W2057443209 @default.
- W2548932017 cites W2057871132 @default.
- W2548932017 cites W2057913811 @default.
- W2548932017 cites W2060427373 @default.
- W2548932017 cites W2065391044 @default.
- W2548932017 cites W2067096102 @default.
- W2548932017 cites W2067235700 @default.
- W2548932017 cites W2067465901 @default.
- W2548932017 cites W2075897300 @default.
- W2548932017 cites W2077567681 @default.
- W2548932017 cites W2082618767 @default.
- W2548932017 cites W2083721602 @default.
- W2548932017 cites W2083994216 @default.
- W2548932017 cites W2085860988 @default.
- W2548932017 cites W2094726706 @default.
- W2548932017 cites W2096951189 @default.
- W2548932017 cites W2103151642 @default.
- W2548932017 cites W2103724490 @default.
- W2548932017 cites W2105481534 @default.
- W2548932017 cites W2105637130 @default.
- W2548932017 cites W2105714645 @default.
- W2548932017 cites W2107021160 @default.
- W2548932017 cites W2113784410 @default.
- W2548932017 cites W2113952938 @default.
- W2548932017 cites W2114388055 @default.
- W2548932017 cites W2116972869 @default.
- W2548932017 cites W2117692326 @default.
- W2548932017 cites W2118582067 @default.
- W2548932017 cites W2119091936 @default.
- W2548932017 cites W2120992942 @default.
- W2548932017 cites W2121382432 @default.
- W2548932017 cites W2121926780 @default.
- W2548932017 cites W2133594846 @default.
- W2548932017 cites W2135206202 @default.
- W2548932017 cites W2136127280 @default.
- W2548932017 cites W2139031446 @default.
- W2548932017 cites W2142016317 @default.
- W2548932017 cites W2142136061 @default.
- W2548932017 cites W2145522203 @default.
- W2548932017 cites W2146089916 @default.
- W2548932017 cites W2148130205 @default.
- W2548932017 cites W2151363315 @default.
- W2548932017 cites W2155518941 @default.
- W2548932017 cites W2158906375 @default.
- W2548932017 cites W2163924952 @default.
- W2548932017 cites W2165456953 @default.
- W2548932017 cites W2166071169 @default.
- W2548932017 cites W2167032822 @default.
- W2548932017 cites W2167708617 @default.
- W2548932017 cites W2168517816 @default.
- W2548932017 cites W2169448034 @default.
- W2548932017 cites W2171231355 @default.
- W2548932017 cites W2171374484 @default.
- W2548932017 cites W2171756499 @default.
- W2548932017 cites W2245472176 @default.
- W2548932017 cites W2257858553 @default.