Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment. Our retrospective and exploratory results indicate that a majority of patients are not recommended the best surviving therapy once they require a new therapy. This investigation lays the groundwork for treatment recommendation using analytics, but more investigation is required to analyze patient outcomes beyond survival

Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia

Background: Medication nonadherence can compound into severe medical problems for patients. Identifying patients who are likely to become nonadherent may help reduce these problems. Data-driven machine learning models can predict medication adherence by using selected indicators from patients’ past health records. Sources of data for these models traditionally fall under two main categories: (1) proprietary data from insurance claims, pharmacy prescriptions, or electronic medical records and (2) survey data collected from representative groups of patients. Models developed using these data sources often are limited because they are proprietary, subject to high cost, have limited scalability, or lack timely accessibility. These limitations suggest that social health forums might be an alternate source of data for adherence prediction. Indeed, these data are accessible, affordable, timely, and available at scale. However, they can be inaccurate. Objective: This paper proposes a medication adherence machine learning model for fibromyalgia therapies that can mitigate the inaccuracy of social health forum data. Methods: Transfer learning is a machine learning technique that allows knowledge acquired from one dataset to be transferred to another dataset. In this study, predictive adherence models for the target disease were first developed by using accurate but limited survey data. These models were then used to predict medication adherence from health social forum data. Random forest, an ensemble machine learning technique, was used to develop the predictive models. This transfer learning methodology is demonstrated in this study by examining data from the Medical Expenditure Panel Survey and the PatientsLikeMe social health forum. Results: When the models are carefully designed, less than a 5% difference in accuracy is observed between the Medical Expenditure Panel Survey and the PatientsLikeMe medication adherence predictions for fibromyalgia treatments. This design must take into consideration the mapping between the predictors and the outcomes in the two datasets. Conclusions: This study exemplifies the potential and limitations of transfer learning in medication adherence–predictive models based on survey data and social health forum data. The proposed approach can make timely medication adherence monitoring cost-effective and widely accessible. Additional investigation is needed to improve the robustness of the approach and extend its applicability to other therapies and other sources of data. [JMIR Med Inform 2019;7(2):e12561

Transfer learning for medication adherence prediction from social forums self-reported data

Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence and non-compliance left unaddressed can compound into severe medical problems for patients. Identifying patients that are likely to become non-adherent can help reduce these problems. Despite these benefits, monitoring adherence at scale is cost-prohibitive. Social forums offer an easily accessible, affordable, and timely alternative to the traditional methods based on claims data. This study investigates the potential of medication adherence prediction based on social forum data for diabetes and fibromyalgia therapies by using transfer learning from the Medical Expenditure Panel Survey (MEPS). Predictive adherence models are developed by using both survey and social forums data and different random forest (RF) techniques. The first of these implementations uses binned inputs from k-means clustering. The second technique is based on ternary trees instead of the widely used binary decision trees. These techniques are able to handle missing data, a prevalent characteristic of social forums data. The results of this study show that transfer learning between survey models and social forum models is possible. Using MEPS survey data and the techniques listed above to derive RF models, less than 5% difference in accuracy was observed between the MEPS test dataset and the social forum test dataset. Along with these RF techniques, another RF implementation with imputed means for the missing values was developed and shown to predict adherence for social forum patients with an accuracy >70%. This thesis shows that a model trained with verified survey data can be used to complement traditional medical adherence models by predicting adherence from unverified, self-reported data in a dynamic and timely manner. Furthermore, this model provides a method for discovering objective insights from subjective social reports. Additional investigation is needed to improve the prediction accuracy of the proposed model and to assess biases that may be inherent to self-reported adherence measures in social health networks

Automated Quantitative Analysis of Nerve Fiber Conduction Velocity

poster abstractThe baroreflex (BRX) is essential for reliable autonomic control of arterial blood pressure. Central to BRX function is a rapid, negative feedback control of heart rate. Arterial pressure sensors known as baroreceptors (BR) encode heart rate and blood pressure information into patterns of neural discharge that is conveyed to the central nervous system via a network of sensory afferent nerve fibers. These BR fibers are broadly classified as myelinated A-fibers with diameters in the range of 1-10 μm and unmyelinated Cfibers with diameters typically less than 1 μm. Fiber diameter and conduction velocity are related with the large A-fibers being much faster (> 10 m/sec) than the smaller diameter C-fibers (< 1 m/sec). Recently, our lab has documented an additional phenotype of myelinated BR afferents termed Ah-fibers that are notably present in female; but only rarely observed in male rats. In response to an electrical stimulus, the nerve fibers produce a compound action potential (CAP) that propagates away from the stimulation site. The CAP of each fiber type is observable in the evoked waveform on account of the differing conduction velocities. As Ah-fibers have conduction velocities in the range of 10 m/sec - 2 m/sec, the resulting CAP is clearly separated in time from the faster A-fibers and much slower C-fibers. Root-mean-square analysis of these distinct time segments provides a quantitative measure of the total signal energy from each of the A-, Ah-, and C-type fibers. This project sought to create MATLAB scripts that would import nerve recording files from both male and female rats and automate the energy analysis in an efficient and reliable manner. Doing so not only facilitates the analysis of these large data files, but also reduces the possibility for biases and errors that can occur during a manual measurement of nerve activity

Same Book, Different Bookmarks: The Development and Preliminary Validation of the Bible Verse Selection Task as a Measure of Christian Fundamentalism

The development and preliminary validation of a new measure of Christian fundamentalism required a multi-stage process. In an initial exploratory study, participants indicated which of a set of Bible verses were most central to their faith, and factor analysis was used to identify verses that appeared to tap a latent dimension of religious fundamentalism (Study 1). These relationships were re-tested with a new method in a new sample (Study 2), and the items that predicted fundamentalism in both samples were incorporated into a new measure of Christian fundamentalism, the Bible Verse Selection Task (BVST). Importantly, the forced-choice format of the BVST may be less impacted by social desirability response styles that may affect scores on existing fundamentalism scales (Studies 3 & 4) while preserving useful levels of criterion-related validity (Study 5) and convergent evidence of construct validity (Study 6). These studies provide initial psychometric evidence for the BVST as an internally consistent measure of Christian fundamentalism that predicts scores on other fundamentalism scales and related constructs including traditionalism, authoritarianism, and political conservativism

Predicting Dementia With Routine Care EMR Data

Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions. The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis

Disinfection of Ebola Virus in Sterilized Municipal Wastewater

Concerns have been raised regarding handling of Ebola virus contaminated wastewater, as well as the adequacy of proposed disinfection approaches. In the current study, we investigate the inactivation of Ebola virus in sterilized domestic wastewater utilizing sodium hypochlorite addition and pH adjustment. No viral inactivation was observed in the one-hour tests without sodium hypochlorite addition or pH adjustment. No virus was recovered after 20 seconds (i.e. 4.2 log10 unit inactivation to detection limit) following the addition of 5 and 10 mg L-1 sodium hypochlorite, which resulted in immediate free chlorine residuals of 0.52 and 1.11 mg L-1, respectively. The addition of 1 mg L-1 sodium hypochlorite resulted in an immediate free chlorine residual of 0.16 mg L-1, which inactivated 3.5 log10 units of Ebola virus in 20 seconds. Further inactivation was not evident due to the rapid consumption of the chlorine residual. Elevating the pH to 11.2 was found to significantly increase viral decay over ambient conditions. These results indicate the high susceptibility of the enveloped Ebola virus to disinfection in the presence of free chlorine in municipal wastewater; however, we caution that extension to more complex matrices (e.g. bodily fluids) will require additional verification

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software