Superficial inferior epigastric artery (SIEA) flap breast reconstruction has advantages over deep inferior epigastric perforator flap (DIEP) and muscle sparing transverse rectus abdominus myocutaneous flap (TRAM) reconstructions with less donor site morbidity and less complicated flap dissection. Not all patients have an adequate SIEA and superficial inferior epigastric vein (SIEV) to support free tissue breast reconstruction, and dissection of the SIEA in all patients can be time consuming. Preoperative computed tomography (CT) angiograms can be used to identify the SIEA and SIEV in patients planning to undergo free abdominal tissue breast reconstruction and direct more efficient dissection in patients with a large SIEA. Retrospective analysis of free abdominal tissue flap breast reconstruction from a single plastic surgeon was performed. All patients undergoing free abdominal tissue breast reconstruction had a preoperative CT angiogram using a protocol for the evaluation of the abdominal wall perforating arteries. CT scans were reviewed by the surgeon preoperatively and evaluated for the presence, caliber, and image quality of the SIEA and SIEV. All patients, regardless of CT angiogram findings, had operative dissection and evaluation of the SIEA and SIEV. A total of 177 free flaps were performed on 113 patients who underwent preoperative CT angiogram and free abdominal tissue breast reconstruction. Of them, 64 patients had bilateral breast reconstruction. Twelve SIEA flaps (10.6%) were performed on 12 patients. During preoperative CT angiographic evaluation, 49 patients (43%) were noted to have at least one visible SIEA, whereas only 24 of those patients (21%) were felt to have an SIEA of adequate caliber. No flaps were lost during the postoperative period. All 12 SIEA flaps performed had an adequate SIEA when observed on preoperative CT angiogram. Overall, 50% of patients found to have at least one adequate SIEA on CT angiogram had a single breast reconstructed with an SIEA flap. If the SIEA was not visualized on CT angiogram, no usable SIEA was found during surgery. Preoperative CT angiogram of the abdominal wall perforating arteries can help predict which patients may have adequate anatomy for an SIEA-based free flap. This information may help direct more efficient dissection of the abdominal flaps by selecting out patients who do not have an adequate SIEA.

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Response to prescribed analgesic drugs varies between individuals, and choosing the right drug/dose often involves a lengthy, iterative process of trial and error. Furthermore, a significant portion of patients experience adverse events such as post-operative urinary retention (POUR) during inpatient management of acute postoperative pain. To better forecast analgesic responses, we compared conventional machine learning methods with modern neural network architectures to gauge their effectiveness at forecasting temporal patterns of postoperative pain and analgesic use, as well as predicting the risk of POUR. Our results indicate that simpler machine learning approaches might offer superior results; however, all of these techniques may play a promising role for developing smarter post-operative pain management strategies.

As self-directed online anxiety treatment and e-mental health programs become more prevalent and begin to rapidly scale to a large number of users, the need to develop automated techniques for monitoring patient progress and detecting early warning signs is at an alltime high. While current online therapy systems work based on explicit quantitative feedback from various survey measures, little attention has been paid thus far to the large amount of unstructured free text present in the monitoring logs and journals submitted by patients as part of the treatment process. In this paper, we automatically categorize patients' internal sentiment and emotions using machine learning classifiers based on n-grams, syntactic patterns, sentiment lexicon features, and distributed word embeddings. We report classification metrics on a novel mental health dataset.

Objectives The primary aim was to characterize the temporal dynamics of postoperative pain intensity using symbolic aggregate approximation (SAX). The secondary aim was to explore the effects of sociodemographic and clinical factors on the SAX representations of postoperative pain intensity. Materials and Methods We applied SAX to a large-scale time series database of 226,808 acute postoperative pain intensity ratings. Pain scores were stratified by patient age, gender, type of surgery, home opioid use, and postoperative day (POD), and co-stratified by age and gender. Cosine similarity, a metric that measures distance using vector angle, was applied to these motif data to compare pain behavior similarities across strata. Results Across age groups, SAX clusters revealed a shift from low-to-low pain score transitions in older patients to high-to-high pain score transitions in younger patients, whereas analyses stratified by gender showed that males had a greater focus of pain score transitions among lower intensity pain scores compared to females. Surgical stratification, using cardiovascular surgery as a reference, demonstrated that pulmonary surgery had the highest cosine similarity at 0.855. With POD stratification, POD 7 carried the greatest cosine similarity to POD 0 (0.611) after POD 1 (0.765), with POD3 (0.419) and POD4 (0.441) carrying the lowest cosine similarities with POD 0. Discussion SAX offers a feasible and effective framework for characterizing large-scale postoperative pain within the time domain. Stratification of SAX representations demonstrate unique temporal dynamic profiles on the basis of age group, sex, type of surgery, preoperative opioid use, and across postoperative days 1-7.

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

Background Increased acute postoperative pain intensity has been associated with the development of persistent postsurgical pain (PPP) in mechanistic and clinical investigations, but it remains unclear which aspects of acute pain explain this linkage. Methods We analysed clinical postoperative pain intensity assessments using symbolic aggregate approximations (SAX), a graphical way of representing changes between pain states from one patient evaluation to the next, to visualize and understand how pain intensity changes across sequential assessments are associated with the intensity of postoperative pain at 1 (M1) and 6 (M6) months after surgery. SAX‐based acute pain transition patterns were compared using cosine similarity, which indicates the degree to which patterns mirror each other. Results This single‐centre prospective cohort study included 364 subjects. Patterns of acute postoperative pain sequential transitions differed between the ‘None’ and ‘Severe’ outcomes at M1 (cosine similarity 0.44) and M6 (cosine similarity 0.49). Stratifications of M6 outcomes by preoperative pain intensity, sex, age group, surgery type and catastrophising showed significant heterogeneity of pain transition patterns within and across strata. Severe‐to‐severe acute pain transitions were common, but not exclusive, in patients with moderate or severe pain intensity at M6. Conclusions Clinically, these results suggest that individual pain‐state transitions, even within patient or procedural strata associated with PPP, may not alone offer good predictive information regarding PPP. Longitudinal observation in the immediate postoperative period and consideration of patient‐ and surgery‐specific factors may help indicate which patients are at increased risk of PPP. Significance Symbolic aggregate approximations of clinically obtained, acute postoperative pain intraday time series identify different motifs in patients suffering moderate to severe pain 6 months after surgery.

The objective of this analysis was to apply Symbolic Aggregate approXimation (SAX) time-series analysis to accelerometer data for activity pattern visualization stratified by self-reported mobility difficulty. A total of 2,393 (71.6 ± 7.9 years old) participants wore an accelerometer on the hip (4+ days; 10+ hours) during the National Health and Nutrition Examination Survey (NHANES), a biannual series of health assessments of the US population. One minute epoch data was used to perform SAX, which converted accelerometry time series data into four activity levels. Intelligent icons of normalized activity transition prevalence, a visual representation of time-series data, were examined among those who self-reported mobility difficulty. Mobility difficulty questions assessed various levels difficulty performing activities such as walking a quarter mile, walking up ten steps, stooping/crouching/kneeling, and walking between rooms on the same floor. Daily activity counts were estimated across difficulty level using weighted-linear regression after adjusted for demographics, lifestyle factors, medical conditions, and accelerometer wear time. Those reporting higher mobility difficulty tended to be older, female, less educated, not married, and smokers. Additionally, those with higher mobility difficulty self-reported lower education, lower income, lower moderate-to-vigorous physical activity, and higher history of adverse medical conditions. Using SAX-derived intelligent icons, those with no difficulty showed high variations of transitions across all activity levels. With higher difficulty, the variations in transitions were lower and constricted around low activity level transitions. Among those who reported unable/don't do mobility-related activities, there were only transitions in the lower tiered activity levels with high prevalence of prolonged low level activity. Adjusted estimates of daily activity were lower as higher difficulty reported occurred but only significant for those reporting unable/don't do mobility-related activity (p<0.01). In summary, this analysis showed apparent differences in stratified activity patterns even when traditional regression analyses on volumetric accelerometer data yielded null results.