Causal Inference in Healthcare: Approaches to Causal Modeling and Reasoning through Graphical Causal Models

In the era of big data, researchers have access to large healthcare datasets collected over a long period. These datasets hold valuable information, frequently investigated using traditional Machine Learning algorithms or Neural Networks. These algorithms perform great in finding patterns out of datasets (as a predictive machine); however, the models lack extensive interpretability to be used in the healthcare sector (as an explainable machine). Without exploring underlying causal relationships, the algorithms fail to explain their reasoning. Causal Inference, a relatively newer branch of Artificial Intelligence, deals with interpretability and portrays causal relationships in data through graphical models. It explores the issue of causality and works towards an explainability of underlying causal models deeply buried in data. For this dissertation work, the research goal is to use Causal Inference to build an applied framework that lets researchers leverage observational datasets in understanding causal relationships between features. To achieve that, we focus on specific objectives such as (a) the addition of background knowledge to causal structure learning algorithms, (b) the proposal of new causal inference methodologies, (c) generation of theories connecting causality to standard statistical analyses (e.g., Odds Ratio, Survival Analysis), and (d) application of proposed approaches in real-world healthcare problems. This dissertation encapsulates the tasks mentioned above, through various new methodologies and experiments under the rubric of Structural Theory of Causation. We discuss the common research theme in causal inference, historical development, the structural theory of causation, and underlying assumptions. Finally, we explore the impact of these proposed methodologies in real-world treatment controversy of Delirium patients, by examining the efficacy of antipsychotic drugs prescribed in treating Delirium in the ICU, from a curated observational healthcare dataset

An mHealth App-Based Self-management Intervention for Family Members of Pediatric Transplant Recipients (myFAMI): Framework Design and Development Study

Background Solid-organ transplantation is the treatment of choice for children with end-stage organ failure. Ongoing recovery and medical management at home after transplant are important for recovery and transition to daily life. Smartphones are widely used and hold the potential for aiding in the establishment of mobile health (mHealth) protocols. Health care providers, nurses, and computer scientists collaboratively designed and developed mHealth family self-management intervention (myFAMI), a smartphone-based intervention app to promote a family self-management intervention for pediatric transplant patients’ families. Objective This paper presents outcomes of the design stages and development actions of the myFAMI app framework, along with key challenges, limitations, and strengths. Methods The myFAMI app framework is built upon a theory-based intervention for pediatric transplant patients, with aid from the action research (AR) methodology. Based on initially defined design motivation, the team of researchers collaboratively explored 4 research stages (research discussions, feedback and motivations, alpha testing, and deployment and release improvements) and developed features required for successful inauguration of the app in the real-world setting. Results Deriving from app users and their functionalities, the myFAMI app framework is built with 2 primary components: the web app (for nurses’ and superadmin usage) and the smartphone app (for participant/family member usage). The web app stores survey responses and triggers alerts to nurses, when required, based on the family members’ response. The smartphone app presents the notifications sent from the server to the participants and captures survey responses. Both the web app and the smartphone app were built upon industry-standard software development frameworks and demonstrate great performance when deployed and used by study participants. Conclusions The paper summarizes a successful and efficient mHealth app-building process using a theory-based intervention in nursing and the AR methodology in computer science. Focusing on factors to improve efficiency enabled easy navigation of the app and collection of data. This work lays the foundation for researchers to carefully integrate necessary information (from the literature or experienced clinicians) to provide a robust and efficient solution and evaluate the acceptability, utility, and usability for similar studies in the future

Analyzing Happiness: Investigation on Happy Moments using a Bag-of-Words Approach and Related Ethical Discussions

In this research paper, we analyzed what moments and activities make people happy, based on a collection of happy moments. We are focusing on specific happy moments from a collection of text responses that people have shared through the crowd-sourcing platform: Amazon Mechanical Turk (MTurk). Using crowd-sourcing to collect our data allows us to advance our understanding of the cause of happiness, by focusing on words and real human experiences. Workers of MTurk were asked to reflect on what makes them happy in a given period and share three specific moments in complete sentences. Through text-based analysis, we will look to see what other components have a role in making a specific event happy and further analyze how we can classify such words. Also, we dive deeper into specific subcategories of classifiers in an attempt to form insights about their happiness level based on specific factors. With the goal to extract features from the text in HappyDB, in this study we used the bag of words approach. Through doing so, our results were successful at predicting the happiness category, concerning both accuracy and context. Our models were able to accomplish the goal of understanding a happy moment and fit such a moment into one of the seven ground truth happiness categories we set at the beginning of this study. We finished the article with the ethical perspective of such research works and related social implications

SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network

Blood hemoglobin level (Hgb) measurement has a vital role in the diagnosis, evaluation, and management of numerous diseases. We describe the use of smartphone video imaging and an artificial neural network (ANN) system to estimate Hgb levels non-invasively. We recorded 10 second-300 frame fingertip videos using a smartphone in 75 adults. Red, green, and blue pixel intensities were estimated for each of 100 area blocks in each frame and the patterns across the 300 frames were described. ANN was then used to develop a model using the extracted video features to predict hemoglobin levels. In our study sample, with patients 20-56 years of age, and gold standard hemoglobin levels of 7.6 to 13.5 g/dL., we observed a 0.93 rank order of correlation between model and gold standard hemoglobin levels. Moreover, we identified specific regions of interest in the video images which reduced the required feature space

Pilot Study Protocol of a Mhealth Self‐Management Intervention for Family Members of Pediatric Transplant Recipients

Solid‐organ transplantation is the treatment of choice for end‐stage organ failure. Parents of pediatric transplant recipients who reported a lack of readiness for discharge had more difficulty coping and managing their child\u27s medically complex care at home. In this paper, we describe the protocol for the pilot study of a mHealth intervention (myFAMI). The myFAMI intervention is based on the Individual and Family Self‐Management Theory and focuses on family self‐management of pediatric transplant recipients at home. The purpose of the pilot study is to test the feasibility of the myFAMI intervention with family members of pediatric transplant recipients and to test the preliminary efficacy on postdischarge coping through a randomized controlled trial. The sample will include 40 family units, 20 in each arm of the study, from three pediatric transplant centers in the United States. Results from this study may advance nursing science by providing insight for the use of mHealth to facilitate patient/family–nurse communication and family self‐management behaviors for family members of pediatric transplant recipients

A Culturally Tailored Intervention System for Cancer Survivors to Motivate Physical Activity

It is necessary for a cancer survivor to have good health behavior. Essential exercise and proper diet are helpful to decrease the risk of recurrence of the disease and the development of a new cancer type. People from low socioeconomic status are more likely to participate in risky health behaviors and have a higher chance of recurrence of cancer. It is important to have a motivational system for cancer survivors that motivates them to perform regular physical activities. In this article, we discuss the development of an mHealth system, which aims to increase physical activity in Native American populations with culturally appropriate motivational text and video messages. The system also includes an e-journal to monitor and maintain proper healthcare. We will also analyze the pilot data to evaluate the usability and the effectiveness of the system

One Size Does Not Fit All: Discharge Teaching and Child Challenging Behaviors

This study compares quality of discharge teaching and care coordination for parents of children with challenging behaviors participating in a nursing implementation project, which used an interactive iPad application, to usual discharge care. Unlike parents in the larger quasi-experimental longitudinal project, parents of children with challenging behaviors receiving the discharge teaching application (n = 14) reported lower mean scores on the quality of discharge teaching scale–delivery subscale (M = 8.2, SD = 3.1) than parents receiving usual care (n = 11) (M = 9.6, SD = 4.7) and lower scores on the Care Transition Measure (M = 2.44, SD = 1.09) than parents receiving usual care (M = 3.02, SD = 0.37), with moderate to large effects (0.554–0.775). The discharge teaching approach was less effective with this subset, suggesting other approaches might be considered for this group of parents. Further study with a larger sample specific to parents of children with challenging behaviors is needed to assess their unique needs and to optimize their discharge experience

Using the Engaging Parents in Education for Discharge (\u3cem\u3ee\u3c/em\u3ePED) iPad Application to Improve Parent Discharge Experience

Purpose The purpose of this study was to evaluate the use of the Engaging Parents in Education for Discharge (ePED) iPad application on parent experiences of hospital discharge teaching and care coordination. Hypotheses were: parents exposed to discharge teaching using ePED will have 1) higher quality of discharge teaching and 2) better care coordination than parents exposed to usual discharge teaching. The secondary purpose examined group differences in the discharge teaching, care coordination, and 30-day readmissions for parents of children with and without a chronic condition. Design/Methods Using a quasi-experimental design, ePED was implemented on one inpatient unit (n = 211) and comparison group (n = 184) from a separate unit at a pediatric academic medical center. Patient experience outcome measures collected on day of discharge included Quality of Discharge Teaching Scale-Delivery (QDTS-D) and care coordination measured by Care Transition Measure (CTM). Thirty-day readmission was abstracted from records. Results Parents taught using ePED reported higher QDTS-D scores than parents without ePED (p = .002). No differences in CTM were found between groups. Correlations between QDTS-D and CTM were small for ePED (r = 0.14, p 0.03) and non-ePED (r = 0.29, p \u3c .001) parent groups. CTM was weakly associated with 30-day readmissions in the ePED group. Conclusion The use of ePED by the discharging nurse enhances parent-reported quality of discharge teaching. Practice implications The ePED app is a theory-based structured conversation guide to engage parents in discharge preparation. Nursing implementation of ePED contributes to optimizing the patient/family healthcare experience