scholarly journals Patient perspectives on delays in diagnosis and treatment of cancer: a qualitative analysis of free-text data

2016 ◽  
Vol 67 (654) ◽  
pp. e49-e56 ◽  
Author(s):  
Rachel K Parsonage ◽  
Julia Hiscock ◽  
Rebecca-Jane Law ◽  
Richard D Neal
Keyword(s):  
Qualitative Analysis ◽  
Patient Perspectives ◽  
Diagnosis And Treatment ◽  
Free Text ◽  
Text Data ◽  
Delays In Diagnosis

A Numerically Coded File of Operative Procedures Derived from a Free Text Data Collection System : A Measure of the Accuracy

1976 ◽  
Vol 15 (01) ◽  
pp. 21-28 ◽  
Author(s):  
Carmen A. Scudiero ◽  
Ruth L. Wong
Keyword(s):  
Data Collection ◽  
Pap Smear ◽  
Operative Procedures ◽  
Free Text ◽  
Collection System ◽  
Process Data ◽  
Text Data ◽  
Data Collection System ◽  
History Of ◽  
Correlation System

A free text data collection system has been developed at the University of Illinois utilizing single word, syntax free dictionary lookup to process data for retrieval. The source document for the system is the Surgical Pathology Request and Report form. To date 12,653 documents have been entered into the system.The free text data was used to create an IRS (Information Retrieval System) database. A program to interrogate this database has been developed to numerically coded operative procedures. A total of 16,519 procedures records were generated. One and nine tenths percent of the procedures could not be fitted into any procedures category; 6.1% could not be specifically coded, while 92% were coded into specific categories. A system of PL/1 programs has been developed to facilitate manual editing of these records, which can be performed in a reasonable length of time (1 week). This manual check reveals that these 92% were coded with precision = 0.931 and recall = 0.924. Correction of the readily correctable errors could improve these figures to precision = 0.977 and recall = 0.987. Syntax errors were relatively unimportant in the overall coding process, but did introduce significant error in some categories, such as when right-left-bilateral distinction was attempted.The coded file that has been constructed will be used as an input file to a gynecological disease/PAP smear correlation system. The outputs of this system will include retrospective information on the natural history of selected diseases and a patient log providing information to the clinician on patient follow-up.Thus a free text data collection system can be utilized to produce numerically coded files of reasonable accuracy. Further, these files can be used as a source of useful information both for the clinician and for the medical researcher.

scholarly journals Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eyal Klang ◽  
Benjamin R. Kummer ◽  
Neha S. Dangayach ◽  
Amy Zhong ◽  
M. Arash Kia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Learning Model ◽  
Free Text ◽  
Combined Model ◽  
Text Data ◽  
Machine Learning Model ◽  
Record Data

AbstractEarly admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200–256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80–324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87–0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91–0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92–0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.

What are the concerns and goals of women attending a urogynaecology clinic? Content analysis of free-text data from an electronic pelvic floor assessment questionnaire (ePAQ-PF)

2018 ◽  
Vol 30 (1) ◽  
pp. 33-41 ◽  
Author(s):  
Thomas Gray ◽  
Scarlett Strickland ◽  
Sarita Pooranawattanakul ◽  
Weiguang Li ◽  
Patrick Campbell ◽  
...  
Keyword(s):  
Content Analysis ◽  
Pelvic Floor ◽  
Free Text ◽  
Text Data ◽  
Assessment Questionnaire

scholarly journals An exploration of text mining of narrative reports of injury incidents to assess risk

2018 ◽  
Vol 251 ◽  
pp. 06020 ◽  
Author(s):  
David Passmore ◽  
Chungil Chae ◽  
Yulia Kustikova ◽  
Rose Baker ◽  
Jeong-Ha Yim
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Surface Mining ◽  
Modeling Processes ◽  
Free Text ◽  
Text Data ◽  
Injury Occurrence ◽  
The Usa ◽  
Musculoskeletal Systems ◽  
Topic Mining

A topic model was explored using unsupervised machine learning to summarized free-text narrative reports of 77,215 injuries that occurred in coal mines in the USA between 2000 and 2015. Latent Dirichlet Allocation modeling processes identified six topics from the free-text data. One topic, a theme describing primarily injury incidents resulting in strains and sprains of musculoskeletal systems, revealed differences in topic emphasis by the location of the mine property at which injuries occurred, the degree of injury, and the year of injury occurrence. Text narratives clustered around this topic refer most frequently to surface or other locations rather than underground locations that resulted in disability and that, also, increased secularly over time. The modeling success enjoyed in this exploratory effort suggests that additional topic mining of these injury text narratives is justified, especially using a broad set of covariates to explain variations in topic emphasis and for comparison of surface mining injuries with injuries occurring during site preparation for construction.

scholarly journals Gender differences in delays in diagnosis and treatment of tuberculosis

2007 ◽  
Vol 22 (5) ◽  
pp. 329-334 ◽  
Author(s):  
F. Karim ◽  
Md. A. Islam ◽  
A. Chowdhury ◽  
E. Johansson ◽  
V. K Diwan
Keyword(s):  
Gender Differences ◽  
Diagnosis And Treatment ◽  
Delays In Diagnosis
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