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DTSTART:20181104T020000
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UID:calendar.347866.field_event_date.0@www.wright.edu
DTSTAMP:20260219T203930Z
CREATED:20181201T220944Z
DESCRIPTION:Committee:Ā  Drs. Tanvi Banerjee\, Advisor\, Mateen Rizki\, and 
 Michelle CheathamABSTRACT:Sickle Cell Disease (SCD) is a hereditary disord
 er in red blood cells that can lead to excruciating pain episodes. SCD cau
 ses the normal red blood cells to distort its shape and turn into sickle s
 hape. The distorted shape makes the hemoglobin inflexible and stick to the
  walls of the vessels thereby obstructing the free flow of blood and event
 ually making the tissues suffer from lack of oxygen.Ā Lack of oxygen causes
  serious problems including Acute Chest Syndrome (ACS)\, stroke\, infectio
 n\, organ damage\, and over the lifetime an SCD can harm a persons spleen\
 , brain\, kidneys\, eyes\, bones.Ā It is believed that 90\,000 to 100\,000 
 American are affected by SCD. Myriad number of studies have been working o
 n gaining better understanding of the disease and predict pain crisis and 
 pain level.Our study focuses on four research problems namely patient info
 rmative\, pain informative\, pain sentiment and pain scores using SCD data
 . Notes are taken for a patient during hospitalization but only few provid
 e beneficial information\, therefore patient informative and pain informat
 ive helps healthcare professionals to scan through the notes that can pro-
  vide valuable information from all the clinical notes maintained. Pain se
 ntiment and pain score predict the change in pain and pain level for a par
 ticular note. Our study experimented with two feature sets\, firstly featu
 res obtained from cTAKES\, a Natural Language Processing (NLP) and secondl
 y features obtained from text using NLP techniques. Four supervised machin
 e learning models namely Logistic Regression\, Random Forest\, Support Vec
 tor Machines\, and Multinomial Naive Bayes are built on these different se
 ts of features. From the results\, it can be noted that cTAKES features ar
 e performing well for SCD problem for all the four research problems with 
 F1 score ranging from 0.40 to 0.86. This indicates that there is promise f
 or using NLP techniques in clinical notes as a means to better understand 
 pain in SCD patients.
DTSTART;TZID=America/New_York:20181205T140000
DTEND;TZID=America/New_York:20181205T160000
LAST-MODIFIED:20181203T150825Z
LOCATION:304 Russ Engineering
SUMMARY:Masters Thesis Defense ā€œUsing Clinical Notes and Natural Language P
 rocessing To Understand Sickle Cell Diseaseā€ By Shufa Khizra
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 using-clinical-notes-natural-language-processing-understand-sickle
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