Improving Palliative Care with Deep Learning

Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.Comment: IEEE International Conference on Bioinformatics and Biomedicine 201

The Bionic Radiologist: avoiding blurry pictures and providing greater insights

Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care. The Bionic Radiologist is a concept that combines humanity and digitalization for better health care integration of radiology. At a practical level, this concept will achieve critical goals: (1) testing decisions being made scientifically on the basis of disease probabilities and patient preferences; (2) image analysis done consistently at any time and at any site; and (3) treatment suggestions that are closely linked to imaging results and are seamlessly integrated with other information. The Bionic Radiologist will thus help avoiding missed care opportunities, will provide continuous learning in the work process, and will also allow more time for radiologists’ primary roles: interacting with patients and referring physicians. To achieve that potential, one has to cope with many implementation barriers at both the individual and institutional levels. These include: reluctance to delegate decision making, a possible decrease in image interpretation knowledge and the perception that patient safety and trust are at stake. To facilitate implementation of the Bionic Radiologist the following will be helpful: uncertainty quantifications for suggestions, shared decision making, changes in organizational culture and leadership style, maintained expertise through continuous learning systems for training, and role development of the involved experts. With the support of the Bionic Radiologist, disparities are reduced and the delivery of care is provided in a humane and personalized fashion

Special Libraries, August 1980

Volume 71, Issue 8https://scholarworks.sjsu.edu/sla_sl_1980/1006/thumbnail.jp

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Get yourself connected: conceptualising the role of digital technologies in Norwegian career guidance

This report outlines the role of digital technologies in the provision of career guidance. It was commissioned by the c ommittee on career guidance which is advising the Norwegian Government following a review of the countries skills system by the OECD. In this report we argue that career guidance and online career guidance in particular can support the development of Norwa y’s skills system to help meet the economic challenges that it faces.The expert committee advising Norway’s Career Guidance Initiativ

Special Libraries, March 1968

Volume 59, Issue 3https://scholarworks.sjsu.edu/sla_sl_1968/1002/thumbnail.jp

Improving the matching of registered unemployed to job offers through machine learning algorithms

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceDue to the existence of a double-sided asymmetric information problem on the labour market characterized by a mutual lack of trust by employers and unemployed people, not enough job matches are facilitated by public employment services (PES), which seem to be caught in a low-end equilibrium. In order to act as a reliable third party, PES need to build a good and solid reputation among their main clients by offering better and less time consuming pre-selection services. The use of machine-learning, data-driven relevancy algorithms that calculate the viability of a specific candidate for a particular job opening is becoming increasingly popular in this field. Based on the Portuguese PES databases (CVs, vacancies, pre-selection and matching results), complemented by relevant external data published by Statistics Portugal and the European Classification of Skills/Competences, Qualifications and Occupations (ESCO), the current thesis evaluates the potential application of models such as Random Forests, Gradient Boosting, Support Vector Machines, Neural Networks Ensembles and other tree-based ensembles to the job matching activities that are carried out by the Portuguese PES, in order to understand the extent to which the latter can be improved through the adoption of automated processes. The obtained results seem promising and point to the possible use of robust algorithms such as Random Forests within the pre-selection of suitable candidates, due to their advantages at various levels, namely in terms of accuracy, capacity to handle large datasets with thousands of variables, including badly unbalanced ones, as well as extensive missing values and many-valued categorical variables

Promotional Campaigns in the Era of Social Platforms

The rise of social media has facilitated the diffusion of information to more easily reach millions of users. While some users connect with friends and organically share information and opinions on social media, others have exploited these platforms to gain influence and profit through promotional campaigns and advertising. The existence of promotional campaigns contributes to the spread of misleading information, spam, and fake news. Thus, these campaigns affect the trustworthiness and reliability of social media and render it as a crowd advertising platform. This dissertation studies the existence of promotional campaigns in social media and explores different ways users and bots (i.e. automated accounts) engage in such campaigns. In this dissertation, we design a suite of detection, ranking, and mining techniques. We study user-generated reviews in online e-commerce sites, such as Google Play, to extract campaigns. We identify cooperating sets of bots and classify their interactions in social networks such as Twitter, and rank the bots based on the degree of their malevolence. Our study shows that modern online social interactions are largely modulated by promotional campaigns such as political campaigns, advertisement campaigns, and incentive-driven campaigns. We measure how these campaigns can potentially impact information consumption of millions of social media users

Predictive modeling of housing instability and homelessness in the Veterans Health Administration

OBJECTIVE: To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA). DATA SOURCES/STUDY SETTING: Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015. STUDY DESIGN: We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry. PRINCIPAL FINDINGS: Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk. CONCLUSIONS: Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Grant/Award Number: IIR 13-334 (IIR 13-334 - U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSRD))Accepted manuscrip