Novel application of natural language processing and machine learning techniques to analyze qualitative patient-reported outcomes data: a report from the PEPR pediatric cancer survivorship study

Lu Zhaohua; Baker Justin; Krull Kevin; Srivastava Kumar; Robison Leslie; Hudson Melissa; Huang I-Chan

首页> 外文期刊>Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation >Novel application of natural language processing and machine learning techniques to analyze qualitative patient-reported outcomes data: a report from the PEPR pediatric cancer survivorship study

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Novel application of natural language processing and machine learning techniques to analyze qualitative patient-reported outcomes data: a report from the PEPR pediatric cancer survivorship study

机译：新颖的自然语言处理和机器学习技术分析定性患者报告的结果数据：PEPR儿科癌症生存研究的报告

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来源
《Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation》 |2019年第1期|共2页
作者
Lu Zhaohua; Baker Justin; Krull Kevin; Srivastava Kumar; Robison Leslie; Hudson Melissa; Huang I-Chan;
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作者单位

St Jude Childrens Res Hosp Dept Biostat 332 N Lauderdale St Memphis TN 38105 USA;

St Jude Childrens Res Hosp Dept Oncol 332 N Lauderdale St Memphis TN 38105 USA;

St Jude Childrens Res Hosp Dept Epidemiol &

Canc Control 332 N Lauderdale St Memphis TN 38105;

St Jude Childrens Res Hosp Dept Biostat 332 N Lauderdale St Memphis TN 38105 USA;

St Jude Childrens Res Hosp Dept Epidemiol &

Canc Control 332 N Lauderdale St Memphis TN 38105;

St Jude Childrens Res Hosp Dept Oncol 332 N Lauderdale St Memphis TN 38105 USA;

St Jude Childrens Res Hosp Dept Epidemiol &

Canc Control 332 N Lauderdale St Memphis TN 38105;

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原文格式 PDF
正文语种 eng
中图分类医药、卫生;
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1. Novel application of natural language processing and machine learning techniques to analyze qualitative patient-reported outcomes data: a report from the PEPR pediatric cancer survivorship study [J] . Lu Zhaohua, Baker Justin, Krull Kevin, Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation . 2019,第Suppla1期

机译：新颖的自然语言处理和机器学习技术分析定性患者报告的结果数据：PEPR儿科癌症生存研究的报告
2. Predicting patient-reported outcomes following mastectomy and breast reconstruction: application of machine learning techniques to real patient data [J] . Sidey-Gibbons Chris, Mehara Babak, Nelson Jonas, Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation . 2019,第Suppla1期

机译：预测患者报告的乳房切除术和乳房重建后的结果：将机器学习技术应用于真实患者数据
3. Using a Natural Language Processing and Machine Learning Algorithm Program to Analyze Inter-Radiologist Report Style Variation and Compare Variation Between Radiologists When Using Highly Structured Versus More Free Text Reporting [J] . Lane F. Donnelly, Robert Grzeszczuk, Carolina V. Guimaraes, Current Problems in Diagnostic Radiology . 2019,第6期

机译：使用自然语言处理和机器学习算法程序来分析放射性学专家报告样式变化，并在使用高度结构化与更多自由文本报告时比较放射科学家之间的变化
4. Development of a Smartphone Application to Monitor Pediatric Patient-Reported Outcomes [C] . Jingting Wang, Nengliang(Aaron) Yao, Yanyan Liu, MEDINFO . 2017

机译：开发智能手机申请，以监测儿科患者报告的结果
5. Leveraging unstructured construction injury reports to predict safety outcomes and model safety risk using Natural Language Processing, Machine Learning, and probability theory [D] . Tixier, Antoine Jean-Pierre. 2015

机译：利用非结构化施工损伤报告以预测使用自然语言处理，机器学习和概率理论来预测安全结果和模型安全风险
6. Original research: Patient-reported outcomes in paediatric cancer survivorship: a qualitative study to elicit the content from cancer survivors and caregivers [O] . Jeanne A Pierzynski, Jennifer L Clegg, Jin-ah Sim, 2020

机译：原始研究：儿科癌症幸存者中患者报告的结果：一项定性研究从癌症幸存者和看护者中得出结论
7. Patient-reported outcomes in paediatric cancer survivorship: a qualitative study to elicit the content from cancer survivors and caregivers [O] . Jeanne A Pierzynski, Jennifer L Clegg, Jin-ah Sim, 2020

机译：儿科癌症生存的患者报告的结果：一种定性研究，以引发癌症幸存者和护理人员的内容

Novel application of natural language processing and machine learning techniques to analyze qualitative patient-reported outcomes data: a report from the PEPR pediatric cancer survivorship study

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