Construction of Dominant Factor Presumption Model for Postoperative Hospital Days from Operation Records

The secondary use of clinical text data to improve the quality and the efficiency of medical care is gaining much attention. However, there are few previous researches that have given feedback to clinical situations. The present paper analyzes the words that appear in operation records to predict the postoperative length of stay. SVM (support vector machine) and feature selection are applied to predict if a stay is longer than the standard length of 25 days. It was confirmed that with less than 20 feature words we can predict if a stay is longer or not with almost the optimal prediction performance.

[1]  Zhou Qi,et al.  Exploring the Rules of Chinese and Western Medicine Used in the Treatment of Osteoporosis by Text Mining , 2012 .

[2]  Christian Blaschke,et al.  Status of text-mining techniques applied to biomedical text. , 2006, Drug discovery today.

[3]  Anthony N. Nguyen,et al.  Application of Information Technology: Collection of Cancer Stage Data by Classifying Free-text Medical Reports , 2007, J. Am. Medical Informatics Assoc..

[4]  John F. Hurdle,et al.  Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research , 2008, Yearbook of Medical Informatics.

[5]  Huilong Duan,et al.  Summarizing clinical pathways from event logs , 2013, J. Biomed. Informatics.

[6]  Sachio Hirokawa,et al.  Feature words that classify problem sentence in scientific article , 2012, IIWAS '12.

[7]  Junichi Tsujii,et al.  Event extraction for systems biology by text mining the literature. , 2010, Trends in biotechnology.

[8]  A. Nguyen,et al.  Multi-class Classification of Cancer Stages from Free-text Histology Reports using Support Vector Machines , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Anthony N. Nguyen,et al.  Symbolic rule-based classification of lung cancer stages from free-text pathology reports , 2010, J. Am. Medical Informatics Assoc..

[10]  William C. Paganelli,et al.  Assessing surgical site infection risk factors using electronic medical records and text mining. , 2014, American journal of infection control.

[11]  Naoki Nakashima,et al.  Factors associated with the postoperative status of donor patients for living donor liver transplantation , 2011, Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society.

[12]  D. S. Parker,et al.  Term Domain Distribution Analysis: a Data Mining Tool for Text Databases , 1999, Methods of Information in Medicine.

[13]  Thomas Werner,et al.  The next generation of literature analysis: Integration of genomic analysis into text mining , 2005, Briefings Bioinform..

[14]  Yung-Chun Chang,et al.  TEMPTING system: A hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries , 2013, J. Biomed. Informatics.

[15]  Cheng Zhang,et al.  Biomedical text mining and its applications in cancer research , 2013, J. Biomed. Informatics.

[16]  Shusaku Tsumoto,et al.  Mining Clinical Pathway Based on Clustering and Feature Selection , 2013, Brain and Health Informatics.

[17]  Ting-Ting Lee,et al.  Application of data mining to the identification of critical factors in patient falls using a web-based reporting system , 2011, Int. J. Medical Informatics.

[18]  Michael Krauthammer,et al.  Term identification in the biomedical literature , 2004, J. Biomed. Informatics.

[19]  T Suzuki,et al.  Automatic DPC code selection from electronic medical records: text mining trial of discharge summary. , 2008, Methods of information in medicine.