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.
Up to 40% of patients undergoing surgery will suffer from moderate to severe postoperative pain. While regional anesthetics offer the possibility of a pain-free surgical experience, their use is limited by their inherent risk and cost. This project aims t accurately determine which surgical patients will suffer from severe acute postoperative pain through the use of advanced mathematical algorithms, permitting anesthesiologists and surgeons to efficiently target pain therapies in a cost-effective manner.
|Tighe, Patrick J; Nickerson, Paul; Fillingim, Roger B et al. (2017) Characterizations of Temporal Postoperative Pain Signatures With Symbolic Aggregate Approximations. Clin J Pain 33:1-11|
|Zou, Baiming; Zou, Fei; Shuster, Jonathan J et al. (2016) On variance estimate for covariate adjustment by propensity score analysis. Stat Med 35:3537-48|
|Tighe, Patrick J; King, Christopher D; Zou, Baiming et al. (2016) Time to Onset of Sustained Postoperative Pain Relief (SuPPR): Evaluation of a New Systems-level Metric for Acute Pain Management. Clin J Pain 32:371-9|
|Nickerson, Paul; Tighe, Patrick; Shickel, Benjamin et al. (2016) Deep neural network architectures for forecasting analgesic response. Conf Proc IEEE Eng Med Biol Soc 2016:2966-2969|
|Tighe, Patrick J; Bzdega, Matthew; Fillingim, Roger B et al. (2016) Markov chain evaluation of acute postoperative pain transition states. Pain 157:717-28|
|Tighe, Patrick J; Goldsmith, Ryan C; Gravenstein, Michael et al. (2015) The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain. J Med Internet Res 17:e84|
|Tighe, Patrick J; Le-Wendling, Linda T; Patel, Ameet et al. (2015) Clinically derived early postoperative pain trajectories differ by age, sex, and type of surgery. Pain 156:609-17|
|Tighe, Patrick; Buckenmaier 3rd, Chester C; Boezaart, Andre P et al. (2015) Acute Pain Medicine in the United States: A Status Report. Pain Med 16:1806-26|
|Tighe, Patrick J; Harle, Christopher A; Hurley, Robert W et al. (2015) Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain. Pain Med 16:1386-401|
|Deal, Litisha G; Nyland, Michael E; Gravenstein, Nikolaus et al. (2014) Are anesthesia start and end times randomly distributed? The influence of electronic records. J Clin Anesth 26:264-70|
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