Query: NLP pain management

title: Predicting inadequate postoperative pain management in depressed patients: A machine learning approach
abstract: Widely prescribed prodrug opioids (e.g. hydrocodone) require conversion by liver enzyme CYP 2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs) inhibits CYP 2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic medical center, we identified patients receiving surgery over a 9 year period. We developed and validated natural language processing (NLP) algorithms to extract depression related information (diagnosis, SSRI use, symptoms) from structured and unstructured data elements. The primary outcome was the difference between preoperative pain score and postoperative pain at discharge, 3 week and 8 week time points. We developed computational models to predict the increase or decrease in the postoperative pain across the 3 time points by using the patient's EHR data (e.g. medications, vitals, demographics) captured before surgery. We evaluate the generalizability of the model using 10 fold cross validation method where the holdout test method is repeated 10 times and mean area under the curve (AUC) is considered as evaluation metrics for the prediction performance. We identified 4,306 surgical patients with symptoms of depression. A total of 14.1% were prescribed both an SSRI and a prodrug opioid, 29.4% were prescribed an SSRI and a non prodrug opioid, 18.6% were prescribed a prodrug opioid but were not on SSRIs, and 37.5% were prescribed a non prodrug opioid but were not on SSRIs. Our NLP algorithm identified depression with a F1 score of 0.95 against manual annotation of 300 randomly sampled clinical notes. On average, patients receiving prodrug opioids had lower average pain scores (p<0.05) with the exception of the SSRI+ group at 3 weeks postoperative follow up. However, SSRI+/Prodrug+ had significantly worse pain control at discharge, 3 and 8 week follow up (p .01) compared to SSRI+/Prodrug patients, whereas there was no difference in pain control among the SSRI patients by prodrug opioid (p>0.05) The machine learning algorithm accurately predicted the increase or decrease of the discharge, 3 week and 8 week follow up pain scores when compared to the pre operative pain score using 10 fold cross validation (mean area under the receiver operating characteristic curve 0.87, 0.81, and 0.69, respectively) Preoperative pain, surgery type, and opioid tolerance were the strongest predictors of postoperative pain control. We provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. Current prescribing patterns indicate that prescribers may not account for this interaction when choosing an opioid. The study results imply that prescribers might instead choose direct acting opioids (e.g. oxycodone or morphine) in depressed patients on SSRIs.

title: Psychological approach to pain management with the methods of NLP (Neurolinguisitic programming)
abstract: H. Siegmund* A. Hovestadt* Psychologische Praxis, Jakobikirchhof 9, O 2000 Hamburg 1 Aim of Investigation: changing psychic experience of pain especially when it is chronic or psychosomatic pain or when medical pain treatment is not effective or not possible (for example: thalamic pain)  Developing the experience of the desired state with all the sensory specific datas: with this state connected internal pictures, sounds, feelings and taste/smell. Developing an individual pain management strategy that fits to the desired outcome and matches the resources and abilities of the patient. Reframing the experience of the pain.  We present 10 case studies concerning: psychosomatic pain chronic pain of organic origin Number of sessions: up to 25 sessions 30 minutes each The psychic effects of the pain in the patient's life decreases, the pain isn't so important any longer or vanishes. Detailed susoestions for further scientific researches can be aiven.  To the members of the "Schmerztherapeutisches Kolloquium Hamburg" and the "Arbeitskreis fiir psychologische Schmerzbehandlung Hamburg" the effects of this method seem to be significant enough to make controled use of these therapeutic instruments.

title: NLP and Relief of Chronic Pain
abstract: Being both health professionals and NLP Practitioners has had its advantages for the two of us writing this article (Libu_ka is a Physiotherapist and Richard is a Nurse) Each of us has been able to apply a combination of NLP and standard medical treatments to the management of clients with chronic (long term) pain. However, as the case studies we explore here will show, an NLP Practitioner with no previous medical knowledge can achieve seemingly miraculous results in pain relief.

title: Identifying Goals of Care Conversations in the Electronic Health Record, Using Natural Language Processing and Machine Learning.
abstract: Goals of care discussions are an important quality metric in palliative care. However, goals of care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health record (EHR) efficiently.  To develop, train, and test an automated approach to identifying goals of care discussions in the EHR, using natural language processing (NLP) and machine learning (ML) METHODS From the electronic health records of an academic health system, we collected a purposive sample of 3,183 EHR notes (1,435 inpatient notes and 1,748 outpatient notes) from 1,426 patients with serious illness over 2008 2016, and manually reviewed each note for documentation of goals of care discussions. Separately, we developed a program to identify notes containing documentation of goals of care discussions using NLP and supervised ML. We estimated the performance characteristics of the NLP/ML program across 100 pairs of randomly partitioned training and test sets. We repeated these methods for inpatient only and outpatient only subsets.  Of 3,183 notes, 689 contained documentation of goals of care discussions. The mean sensitivity of the NLP/ML program was 82.3% (SD 3.2% and the mean specificity was 97.4% (SD 0.7% NLP/ML results had a median positive likelihood ratio of 32.2 (IQR 27.5 39.2) and a median negative likelihood ratio of 0.18 (IQR 0.16 0.20) Performance was better in inpatient only samples than outpatient only samples.  Using NLP and ML techniques, we developed a novel approach to identifying goals of care discussions in the EHR. NLP and ML represent a potential approach toward measuring goals of care discussions as a research outcome and quality metric.

title: Deep natural language processing to identify symptom documentation in clinical notes for patients with heart failure undergoing cardiac resynchronization therapy.
abstract: Clinicians lack reliable methods to predict which patients with congestive heart failure (CHF) will benefit from cardiac resynchronization therapy (CRT) Symptom burden may help to predict response, but this information is buried in free text clinical notes. Natural language processing (NLP) may identify symptoms recorded in the electronic health record (EHR) and thereby enable this information to inform clinical decisions about the appropriateness of CRT.  To develop, train, and test a deep NLP model that identifies documented symptoms in CHF patients receiving CRT.  We identified a random sample of clinical notes from a cohort of patients with CHF who later received CRT. Investigators labeled documented symptoms as present, absent, and context dependent (pathologic depending on the clinical situation) The algorithm was trained on 80% and fine tuned parameters on 10% of the notes. We tested the model on the remaining 10% We compared the model's performance to investigators' annotations using accuracy, precision (positive predictive value) recall (sensitivity) and F1 score (a combined measure of precision and recall) RESULTS Investigators annotated 154 notes (352,157 words) and identified 1340 present, 1300 absent, and 221 context dependent symptoms. In the test set of 15 notes (35,467 words) the model's accuracy was 99.4% and recall was 66.8% Precision was 77.6% and overall F1 score was 71.8. F1 scores for present (70.8) and absent (74.7) symptoms were higher than that for context dependent symptoms (48.3) CONCLUSION A deep NLP algorithm can be trained to capture symptoms in CHF patients who received CRT with promising precision and recall.

title: Natural Language Processing Accurately Measures Adherence to Best Practice Guidelines for Palliative Care in Trauma.
abstract: The Trauma Quality Improvement Program Best Practice Guidelines recommend palliative care (PC) concurrent with restorative treatment for patients with life threatening injuries. Measuring PC delivery is challenging: administrative data is non specific and manual review is time intensive.  To identify PC delivery to patients with life threatening trauma and compare the performance of natural language processing (NLP) a form of computer assisted data abstraction, to administrative coding and gold standard manual review.  Patients =18 years admitted with life threatening trauma were identified from Two Level I trauma centers (7/2016 6/2017) Four PC process measures were examined during the trauma admission: code status clarification, goals of care (GOC) discussion, PC consult, and hospice assessment. The performance of NLP and administrative coding were compared to manual review. Multivariable regression was used to determine patient and admission factors associated with PC delivery.  There were 76,791 notes associated with 2,093 admissions. NLP identified PC delivery in 33% of admissions compared to 8% using administrative coding. Using NLP, code status clarification was most commonly documented (27% followed by GOC (18% PC consult (4% and hospice assessment (4% Compared to manual review, NLP performed >50 times faster and had a sensitivity of 93% specificity of 96% and accuracy of 95% Administrative coding had a sensitivity of 21% specificity of 92% and accuracy of 68% Factors associated with PC delivery included older age, increased comorbidities, and longer intensive care unit stay.  NLP performs with similar accuracy to manual review but with improved efficiency. NLP has the potential to accurately identify PC delivery and benchmark performance of best practice guidelines.

title: A method for determining the number of documents needed for a gold standard corpus
abstract: The unstructured narratives in medicine have been increasingly targeted for content extraction using the techniques of natural language processing (NLP) In most cases, these efforts are facilitated by creating a manually annotated set of narratives containing the ground truth; commonly referred to as a gold standard corpus. This corpus is used for modeling, fine tuning, and testing NLP software as well as providing the basis for training in machine learning. Determining the number of annotated documents (size) for this corpus is important, but rarely described; rather, the factors of cost and time appear to dominate decision making about corpus size. In this report, a method is outlined to determine gold standard size based on the capture probabilities for the unique words within a target corpus. To demonstrate this method, a corpus of dictation letters from the Michigan Pain Consultant (MPC) clinics for pain management are described and analyzed. A well formed working corpus of 10,000 dictations was first constructed to provide a representative subset of the total, with no more than one dictation letter per patient. Each dictation was divided into words and common words were removed. The Poisson function was used to determine probabilities of word capture within samples taken from the working corpus, and then integrated over word length to give a single capture probability as a function of sample size. For these MPC dictations, a sample size of 500 documents is predicted to give a capture probability of approximately 0.95. Continuing the demonstration of sample selection, a provisional gold standard corpus of 500 documents was selected and examined for its similarity to the MPC structured coding and demographic data available for each patient. It is shown that a representative sample, of justifiable size, can be selected for use as a gold standard.

title: A Comprehensive Study of Text Analytics
abstract: The interactions between human languages and computers is termed as Natural Language Processing. These interactions are between the fields of computer science, artificial intelligence, and computational linguistics. As it involves human languages, so the term NLP is referred. NLP requires the expertise in statistics, linguistics, expert systems etc. Basically it aims at text analysis by using automation methods. On the other hand, customer relationship management is an integral part of any organization where in it is structured to fetch all the relevant data pertaining to its customer's viz. customer profile, customer feedback. Transactions on all customer activities etc. The paper aims at the comprehensive study of text analytics using natural language processing. The natural language processing tools can be utilized to identify customer pain points to improve customer relationship management of any organization, an experiment is conducted to use statistical and graphical visualization to arrive at a conclusion by using various techniques of text mining using open source tool.

title: Neovascular glaucoma following central retinal vein occlusion a case report
abstract: Glaucoma is an Important cause of blindness, in particular neovascular glaucoma which is a complex condition and always becomes challenging for the clinician in the past, present and the future. A 54 year old man suffering from blindness of neovascular glaucoma following central retinal vein occlusion in the right eye with no response to Pan Retinal Photocoagulation (PRP) and open angle glaucoma in the left eye has been reported. Systemic conditions as a predisposing factor were not found, so the local ocular background i.e open angle glaucoma was suspected to be the cause of central retinal vein occlusion. In this case the management must be focused on the left eye to avoid further complication. The right eye was on end stage and no response to PRP and the visual acuity Is no light perception (NLP) Enucleation should be considered as the high intraocular pressure (IOP) and ocular pain persisted.  neovascular glaucoma central retinal vein occlusion open angle glaucoma Pan retinal photocoagulation

title: Self hypnosis and osteopathic soft tissue manipulation with a ballet dancer
abstract: This single case study of a ballet dancer incorporates essentially osteopathy, self hypnosis and neurolinguistic programming (NLP) in the management of exhaustion, stress, anxiety and pain symptoms of the right thigh, calf and ankle combined with confidence strengthening techniques. Physical and mental burnout symptoms had reached the point of constant pain and overstrain of the muscles of the right leg and hip, spine and neck. In parallel with these were symptoms of exhaustion, unhappiness and a tendency to over focus on other dancers at the expense of self focus and motivation. The osteo/physical therapy entailed the rebalancing of overused, strained and hyper tense muscular tissues using myofascial release techniques, together with the deactivation of trigger points and postural integration. The mental attitude was supported through reframing techniques during self hypnosis directed at general feelings of being blocked, imprisoned, cramped, exhausted and weakened in the right leg. Psychophysiological assessment post therapy was compatible with self regulation training.