Delirium occurs in about 80% of cases in the Intensive Care Unit (ICU) and is associated with an extended hospital stay, increased mortality, and other complications. Delirium lacks biomarker-based diagnosis and is frequently treated with antipsychotic drugs (APD), despite numerous studies debating its efficacy. Since randomized controlled trials (RCT) are expensive and time-consuming, we approach the research question of estimating the efficacy and safety outcomes of APD in treating delirium through retrospective cohort analysis. We employed the Causal inference framework to explore the underlying causal model for Delirium patient cohort. We focus on building a structural causal model for delirium in the ICU using large observational data sets linking various delirium-related covariates. We utilized an extensive electronic health records (EHR) dataset (MIMIC-III) to curate delirium data cohort. Our null hypothesis examines any significant differences in outcomes (30-day mortality and ICU length of stay) among delirium patients under different drug-groups (Haloperidol, other drugs, and no drugs). Our causal exploration presents a specialized pipeline through causal model generation, expert knowledge augmentation and average treatment effect estimation. Through our exploratory, machine learning driven, and causal analysis, we estimate and compare effects of antipsychotic drug groups on patients’ survival timeline and ICU length-of-stay.

Causal inference involving Structural causal models (SCMs) provides a principled approach to identifying causation from observational and experimental data in disciplines ranging from economics to medicine. However, to estimate the underlying causal structure, SCMs need to rely on domain knowledge in addition to available data. Clinical research has a vast collection of well-explored hypotheses, experiments, and publications, rich with underused causal information. A key challenge in this context is the absence (or acceptance) of a systematic and methodological framework for encoding priors (background knowledge) into causal models. We propose an abstraction called causal knowledge hierarchy (CKH) for encoding priors into causal models. Our approach is based on the foundation of "levels of evidence" in medicine, with a focus on confidence in causal information. Using CKH, we present a standardized framework for encoding causal priors from various information sources and combining them to derive an SCM. We evaluate our approach on multiple (simulated and real-world) benchmark datasets and demonstrate overall performance compared to the ground truth causal model.

Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip mini-video images and the hemoglobin level by laboratory "gold standard." We also discussed other non-invasive methods and compared with our model. Finally, we described our findings and discussed future works.

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Timely and accurate information about patients' symptoms is important for clinical decision making such as adjustment of medication. Due to the limitations of self-reported symptom such as pain, we investigated whether facial images can be used for detecting pain level accurately using existing algorithms and infrastructure for cancer patients. For low cost and better pain management solution, we present a smart phone based system for pain expression recognition from facial images. To the best of our knowledge, this is the first study for mobile based chronic pain intensity detection. The proposed algorithms classify faces, represented as a weighted combination of Eigenfaces, using an angular distance, and support vector machines (SVMs). A pain score was assigned to each image by the subject. The study was done in two phases. In the first phase, data were collected as a part of a six month long longitudinal study in Bangladesh. In the second phase, pain images were collected for a cross-sectional study in three different countries: Bangladesh, Nepal and the United States. The study shows that a personalized model for pain assessment performs better for automatic pain assessment and the training set should contain varying levels of pain representing the application scenario.

Breast cancer (BC) patients need traditional treatment as well as long term monitoring through an adaptive feedback-oriented treatment mechanism. Here, we present the findings of our 31-week long field study and deployment of e-ESAS - the first mobile-based remote symptom monitoring system (RSMS) developed for rural BC patients where patients are the prime users rather than just the source of data collection at some point of time. We have also shown how 'motivation' and 'automation' have been integrated in e-ESAS and creating a unique motivation-persuasion-motivation cycle where the motivated patients become proactive change agents by persuading others. Though in its early deployment stages (2 months), e-ESAS demonstrates the potential to positively impact the cancer care by (1) helping the doctors with graphical charts of long symptom history (automation), (2) facilitating timely interventions through alert generation (automation) and (3) improving three way communications (doctor-patient-attendant) for a better decision making process (motivation) and thereby improving the quality of life of BC patients.