YEARS

2012-2016

AUTHORS

Patrick J Tighe

TITLE

Use of Machine Learning Classifiers to Forecast Severe Acute Postoperative Pain F

ABSTRACT

DESCRIPTION (provided by applicant): Up to 40% of patients undergoing surgery report moderate to severe pain in the postoperative period. The development of a clinical decision support system to allow preoperative intervention for this subset of patients may have a profound impact on their recovery, and potentially their long-term outcome. To accurately forecast severe postoperative pain, we propose the use of machine learning classifiers (MLC's), which are classification algorithms employing a range of novel search and classification methodologies that continually update their performance as new information becomes available. This award will permit the applicant to complete a rigorous didactic curriculum emphasizing classification theory, algorithm evaluation, and development of clinical decision support systems. The nature of these studies place them far outside the realm of traditional medical education. By protecting time for continued mentorship from experts in pain biology and psychology, machine learning, and clinical regional anesthesia, the candidate is well-positioned to become an independently-funded researcher in the field of perioperative pain prediction. In Specific Aim 1 of this study, we will test the hypothesis that Machine Learning Classifiers can accurately predict severe post-operative pain in patients undergoing cancer surgery. This portion of the study will retrospectively test MLC's ability to predict severe pain on post-operatie day 1. An array of MLC's will be tested amongst each other, both with and without the implementation of text analytics. Additionally, all MLC's will be compared against more traditional multiple variable regression techniques such as logistic regression. In Specific Aim 2, we will test the hypothesis that the addition of prospectively obtained attributes and instances will permit continued improvement in MLC performance. This prospective portion of the study will examine the role of prospectively-obtained psychometric attributes, as well as the ability of MLC's to learn and adapt their accuracy during continued refinements to surgical and anesthetic care.

FUNDED PUBLICATIONS

  • Time to Onset of Sustained Postoperative Pain Relief (SuPPR): Evaluation of a New Systems-level Metric for Acute Pain Management.
  • Thalamic activity and biochemical changes in individuals with neuropathic pain after spinal cord injury.
  • The evolution and practice of acute pain medicine.
  • Are anesthesia start and end times randomly distributed? The influence of electronic records.
  • Sex differences in the incidence of severe pain events following surgery: a review of 333,000 pain scores.
  • Acute Pain Medicine in the United States: A Status Report.
  • The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain.
  • Geospatial analysis of hospital consumer assessment of healthcare providers and systems pain management experience scores in U.S. hospitals.
  • Use of machine-learning classifiers to predict requests for preoperative acute pain service consultation.
  • Of rough starts and smooth finishes: correlations between post-anesthesia care unit and postoperative days 1-5 pain scores.
  • Acute pain medicine in anesthesiology.
  • Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.
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    29 TRIPLES      17 PREDICATES      30 URIs      9 LITERALS

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    26 sg:title Use of Machine Learning Classifiers to Forecast Severe Acute Postoperative Pain F
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