Epidemiologic patterns of adolescent idiopathic scoliosis detection and treatment in new york state

The purpose of this study is to examine the epidemiologic trends of adolescent idiopathic scoliosis (AIS) detection and treatment in New York State (NYS), including disparities in access. The New York Statewide Planning and Research Cooperative System database was reviewed to identify patients who underwent treatment for, or were diagnosed with, AIS from 2008 to 2016. Age determined adolescence; and the surgery date, 3-digit zip code, sex, race, insurance status, institution and surgeon license number were recorded to identify such trends. The geographical distribution was assembled from an NYS shapefile, obtained from the Topologically Integrated Geographic Encoding and Referencing database with analysis performed using tigris R. In total 54 002 patients with AIS, 3967 of whom were surgically treated, were identified for analysis. Diagnoses spiked in 2010. Females were diagnosed and underwent surgical treatment more frequently than males. AIS was diagnosed and treated in white patients more frequently than in black and Asian patients combined. From 2010 to 2013, the patients self-paying for surgical treatment decreased more than other payment modalities. Medium-volume surgeons continually increased the number of cases performed, whereas low-volume surgeons exhibited the opposite pattern. High-volume hospitals had a decrease in the number of cases from 2012 and were overtaken by medium-volume hospitals in 2015. Most procedures are performed within the New York City (NYC) area, though AIS was common in all NYS counties. AIS diagnoses increased after 2010, with fewer patients self-paying for surgery. White patients underwent more procedures than minority patients. Surgical cases were disproportionally performed in the NYC area compared to statewide.

An Objective Framework for Evaluating Unrecognized Bias in Medical AI Models Predicting COVID-19 Outcomes

OBJECTIVE

The increasing translation of artificial intelligence (AI)/machine learning (ML) models into clinical practice brings an increased risk of direct harm from modeling bias; however, bias remains incompletely measured in many medical AI applications. This article aims to provide a framework for objective evaluation of medical AI from multiple aspects, focusing on binary classification models.

MATERIALS AND METHODS

Using data from over 56 thousand Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in four AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. Models were evaluated both retrospectively and prospectively using model-level metrics of discrimination, accuracy, and reliability, and a novel individual-level metric for error.

RESULTS

We found inconsistent instances of model-level bias in the prediction models. From an individual-level aspect, however, we found most all models performing with slightly higher error rates for older patients.

DISCUSSION

While a model can be biased against certain protected groups (i.e., perform worse) in certain tasks, it can be at the same time biased towards another protected group (i.e., perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations.

CONCLUSION

Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

Where Opioid Overdose Patients Live Far From Treatment: Geospatial Analysis of Underserved Populations in New York State

BACKGROUND

Opioid addiction and overdose have a large burden of disease and mortality in New York State (NYS). The medication naloxone can reverse an overdose, and buprenorphine can treat opioid use disorder. Efforts to increase the accessibility of both medications include a naloxone standing order and a waiver program for prescribing buprenorphine outside a licensed drug treatment program. However, only a slim majority of NYS pharmacies are listed as participating in the naloxone standing order, and less than 7% of prescribers in NYS have a buprenorphine waiver. Therefore, there is a significant opportunity to increase access.

OBJECTIVE

Identifying the geographic regions of NYS that are farthest from resources can help target interventions to improve access to naloxone and buprenorphine. To maximize the efficiency of such efforts, we also sought to determine where these underserved regions overlap with the largest numbers of actual patients who have experienced opioid overdose.

METHODS

We used address data to assess the spatial distribution of naloxone pharmacies and buprenorphine prescribers. Using the home addresses of patients who had an opioid overdose, we identified geographic locations of resource deficits. We report findings at the high spatial granularity of census tracts, with some neighboring census tracts merged to preserve privacy.

RESULTS

We identified several hot spots, where many patients live far from the nearest resource of each type. The highest density of patients in areas far from naloxone pharmacies was found in eastern Broome county. For areas far from buprenorphine prescribers, we identified subregions of Oswego county and Wayne county as having a high number of potentially underserved patients.

CONCLUSIONS

Although NYS is home to thousands of naloxone pharmacies and potential buprenorphine prescribers, access is not uniform. Spatial analysis revealed census tract areas that are far from resources, yet contain the residences of many patients who have experienced opioid overdose. Our findings have implications for public health decision support in NYS. Our methods for privacy can also be applied to other spatial supply-demand problems involving sensitive data.

BayesGaze: A Bayesian Approach to Eye-Gaze Based Target Selection

Selecting targets accurately and quickly with eye-gaze input remains an open research question. In this paper, we introduce BayesGaze, a Bayesian approach of determining the selected target given an eye-gaze trajectory. This approach views each sampling point in an eye-gaze trajectory as a signal for selecting a target. It then uses the Bayes' theorem to calculate the posterior probability of selecting a target given a sampling point, and accumulates the posterior probabilities weighted by sampling interval to determine the selected target. The selection results are fed back to update the prior distribution of targets, which is modeled by a categorical distribution. Our investigation shows that BayesGaze improves target selection accuracy and speed over a dwell-based selection method, and the Center of Gravity Mapping (CM) method. Our research shows that both accumulating posterior and incorporating the prior are effective in improving the performance of eye-gaze based target selection.

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning

OBJECTIVE

The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions.

METHODS

Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve.

RESULTS

The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN).

CONCLUSIONS

LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.

Association of Opioid Use Disorder With 2016 Presidential Voting Patterns: Cross-sectional Study in New York State at Census Tract Level

Background

Opioid overdose-related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP) and opioid use disorder (OUD).

Objective

We aimed to discover geospatial patterns in nonmedical opioid use and its correlations with demographic features related to despair and economic hardship, most notably the US presidential voting patterns in 2016 at census tract level in New York State.

Methods

This cross-sectional analysis used data from New York Statewide Planning and Research Cooperative System claims data and the presidential voting results of 2016 in New York State from the Harvard Election Data Archive. We included 63,958 patients who had at least one OUD diagnosis between 2010 and 2016 and 36,004 patients with at least one OP diagnosis between 2012 and 2016. Geospatial mappings were created to compare areas of New York in OUD rates and presidential voting patterns. A multiple regression model examines the extent that certain factors explain OUD rate variation.

Results

Several areas shared similar patterns of OUD rates and Republican vote: census tracts in western New York, central New York, and Suffolk County. The correlation between OUD rates and the Republican vote was .38 (P<.001). The regression model with census tract level of demographic and socioeconomic factors explains 30% of the variance in OUD rates, with disability and Republican vote as the most significant predictors.

Conclusions

At the census tract level, OUD rates were positively correlated with Republican support in the 2016 presidential election, disability, unemployment, and unmarried status. Socioeconomic and demographic despair-related features explain a large portion of the association between the Republican vote and OUD. Together, these findings underscore the importance of socioeconomic interventions in combating the opioid epidemic.

Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning

The US is experiencing an opioid epidemic, and opioid overdose is causing more than 100 deaths per day. Early identification of patients at high risk of Opioid Overdose (OD) can help to make targeted preventative interventions. We aim to build a deep learning model that can predict the patients at high risk for opioid overdose and identify most relevant features. The study included the information of 5,231,614 patients from the Health Facts database with at least one opioid prescription between January 1, 2008 and December 31, 2017. Potential predictors (n = 1185) were extracted to build a feature matrix for prediction. Long Short-Term Memory (LSTM) based models were built to predict overdose risk in the next hospital visit. Prediction performance was compared with other machine learning methods assessed using machine learning metrics. Our sequential deep learning models built upon LSTM outperformed the other methods on opioid overdose prediction. LSTM with attention mechanism achieved the highest F-1 score (F-1 score: 0.7815, AUCROC: 0.8449). The model is also able to reveal top ranked predictive features by permutation important method, including medications and vital signs. This study demonstrates that a temporal deep learning based predictive model can achieve promising results on identifying risk of opioid overdose of patients using the history of electronic health records. It provides an alternative informatics-based approach to improving clinical decision support for possible early detection and intervention to reduce opioid overdose.

A large-scale retrospective study of opioid poisoning in New York State with implications for targeted interventions

Opioid overdose related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP). To discover trends and patterns of opioid poisoning and the demographic and regional disparities, we analyzed large scale patient visits data in New York State (NYS). Demographic, spatial, temporal and correlation analyses were performed for all OP patients extracted from the claims data in the New York Statewide Planning and Research Cooperative System (SPARCS) from 2010 to 2016, along with Decennial US Census and American Community Survey zip code level data. 58,481 patients with at least one OP diagnosis and a valid NYS zip code address were included. Main outcome and measures include OP patient counts and rates per 100,000 population, patient level factors (gender, age, race and ethnicity, residential zip code), and zip code level social demographic factors. The results showed that the OP rate increased by 364.6%, and by 741.5% for the age group > 65 years. There were wide disparities among groups by race and ethnicity on rates and age distributions of OP. Heroin and non-heroin based OP rates demonstrated distinct temporal trends as well as major geospatial variation. The findings highlighted strong demographic disparity of OP patients, evolving patterns and substantial geospatial variation.

Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach

BACKGROUND

Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the "gold standard" reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate.

OBJECTIVE

This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems.

METHODS

We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population.

RESULTS

When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve-receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis.

CONCLUSIONS

This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.

Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach

BACKGROUND

In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns.

OBJECTIVE

This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic.

METHODS

Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data.

RESULTS

Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments.

CONCLUSIONS

Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.

Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records

Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This depends on critical knowledge to understand the risk of opioid overdose of patients. In this paper, we present our work on building machine learning based prediction models to predict opioid overdose of patients based on the history of patients' electronic health records (EHR). We performed two studies using New York State claims data (SPARCS) with 440,000 patients and Cerner's Health Facts database with 110,000 patients. Our experiments demonstrated that EHR based prediction can achieve best recall with random forest method (precision: 95.3%, recall: 85.7%, F1 score: 90.3%), best precision with deep learning (precision: 99.2%, recall: 77.8%, F1 score: 87.2%). We also discovered that clinical events are among critical features for the predictions.

Deep Learning on Electronic Health Records to Improve Disease Coding Accuracy

Characterization of a patient's clinical phenotype is central to biomedical informatics. ICD codes, assigned to inpatient encounters by coders, is important for population health and cohort discovery when clinical information is limited. While ICD codes are assigned to patients by professionals trained and certified in coding there is substantial variability in coding. We present a methodology that uses deep learning methods to model coder decision making and that predicts ICD codes. Our approach predicts codes based on demographics, lab results, and medications, as well as codes from previous encounters. We are able to predict existing codes with high accuracy for all three of the test cases we investigated: diabetes, acute renal failure, and chronic kidney disease. We employed a panel of clinicians, in a blinded manner, to assess ground truth and compared the predictions of coders, model and clinicians. When disparities between the model prediction and coder assigned codes were reviewed, our model outperformed coder assigned ICD codes.

Comparison of neoadjuvant and adjuvant therapy for resectable pancreatic cancer using Markov decision modeling

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Background: Meta-analysis of smaller studies have shown that neoadjuvant chemotherapy is more beneficial for patients with resectable pancreatic cancer than upfront surgery by comparing life expectancy (LE) and quality-adjusted life expectancy (QALE) computed from Markov models. The study results utilized literature data using several small clinical trials but no individual patient data was used and only gemzar based therapy was studied. Methods: Markov model was used to calculate the LE and QALE for adjuvant and neoadjuvant chemotherapy and individual patient parameters was used in the model to refine certain clinical outcome datapoints. We used 278 patients pancreatic cancer data from 2008 to 2017 from Stony Brook University and used the literature data from randomized clinical trials studying gemzar (GEM), gemzar and capecitabine (GEM+CAP) and modified FOLFIRINOX (mFOL). The median OS for each model was obtained by computer simulation. Results: Intensive adjuvant chemotherapy using mFOL had best simulation outcome with median OS (52.5 months), LE (81.5 months), and QALE (65.0 quality-adjusted life months) compared to using GEM (40.5, 66.5, and 52.9 months for median OS, LE, and QALE), GEM+CAP (16.5, 28.0, and 21.9 months for median OS, LE, and QALE), and 5-FU (16.5, 26.9, and 21.1 months for median OS, LE, and QALE). The neoadjuvant chemotherapy approach improved LE and QALE but not in median OS when compared to adjuvant therapy. Conclusions: Mathematical modeling confirms the improved clinical outcome for modified FOLFIRINOX in resectable pancreatic cancer. The benefit of neoadjuvant chemotherapy approach suggest further clinical trials are needed to determine the better treatment strategy for pancreatic cancer patients.

A survey of the etiological agents of scalp and nail dermatophytosis in Yazd, Iran in 2014-2015

Background and Purpose:

Tinea capitis and tinea unguium are regarded as global public health concerns. The purpose of the present study was to identify the etiological agents of tinea capitis and tinea unguium in patients, referring to the Central Laboratory of Yazd University of Medical Sciences, Yazd, Iran.

Materials and Methods:

This study was conducted during 2014-2015. Skin scraping, scalp hair, and nail clipping specimens were collected from 134 patients (80 males and 54 females) with clinical features suggesting fungal involvement. Direct microscopic examinations were carried out, using potassium hydroxide 10%, while culture studies were performed on Sabouraud dextrose agar, containing chloramphenicol and cycloheximide at 28^°C for four weeks. Fungal colonies were identified based on their macroscopic and microscopic characteristics, as well as supplementary diagnostic tests.

Results:

Among 134 patients, 12 cases showed positive results on direct examination and culture studies. The frequency of infections was equal among male and female subjects. Among 12 affected cases, the frequency of tinea capitis and tinea unguium was 91.6% and 8.4%, respectively. Microsporum canis (50%) was the most prevalent species, followed by Trichophyton verrucosum (25%) and Trichophyton mentagrophytes (25%). Also, tinea unguium, caused by T. mentagrophytes, was found in a female patient.

Conclusion:

The etiological agents of scalp and nail dermatophytosis have changed in Yazd over the past 13 years. In the present study, replacement of anthropophilic dermatophytes by zoophilic species was noteworthy, highlighting the necessity of efficient surveillance for the management and prevention of infections.

A study on etiologic agents and clinical manifestations of dermatophytosis in Yazd, Iran

BACKGROUND AND PURPOSE

Dermatophytosis is one of the most common infections of skin, hair, and nails, caused by a group of keratinophilic fungi known as dermatophytes. Species identification of these fungi is of great significance from epidemiological and therapeutic points of view. The objective of the present study was to investigate dermatophytosis and its causative agents in patients, referring to the Central Mycology Laboratory of Yazd University of Medical Sciences, Yazd, Iran.

MATERIALS AND METHODS

In total, 139 clinically suspected cases of dermatophytosis were examined during 12 months from February 2014 to February 2015. Skin scrapings were assessed through direct microscopic examinations and culture studies. Dermatophyte isolates were identified based on colony morphology on potato dextrose agar and dermatophyte test medium, nutritional requirements, urease and hair perforation tests, and microscopic characteristics on slide cultures.

RESULTS

Dermatophytosis was mycologically confirmed in 26 (18.70%) out of 139 cases. Although there was a statistically insignificant difference between male and female subjects, men were dominantly affected. Infection was significantly common in the age group of ≤ 29 years (P<0.043). The most common clinical manifestation of dermatophytosis was tinea corporis (69.2%), followed by tinea cruris (15.4%), tinea manuum (11.5%), and tinea pedis (3.8%). Trichophyton mentagrophytes complex was the main etiologic agent (38.5%), followed by T. rubrum (23%), T. violaceum (15.5%), T. verrucosum (11.5%), Microsporum canis (7.7%), and Epidermophyton floccosum (3.8%).

CONCLUSION

In comparison with previous research, epidemiology of dermatophytosis has changed in Yazd over the past decades. Therefore, periodical investigations on the epidemiological aspects of this infection are required for efficient control and prevention of this cutaneous dermatophytic disease.

Distance metric learning for complex networks: towards size-independent comparison of network structures

Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these applications, an effective similarity metric is needed which, given two complex networks of possibly different sizes, evaluates the amount of similarity between the structural features of the two networks. Traditional graph comparison approaches, such as isomorphism-based methods, are not only too time consuming but also inappropriate to compare networks with different sizes. In this paper, we propose an intelligent method based on the genetic algorithms for integrating, selecting, and weighting the network features in order to develop an effective similarity measure for complex networks. The proposed similarity metric outperforms state of the art methods with respect to different evaluation criteria.