Biomedical and Health Data Analysis

Summary

The effort to improve healthcare delivery by making medicine more personalized, precise and efficient, presents many challenges that require the acquisition, processing and analysis of a large variety of healthcare data. One example is how patients with a trauma injury are dispatched to trauma centers distributed in large geographic areas (Bardes et al., 2022). Another example is the identification of patients with certain cardiovascular diseases, such as coronary artery disease, which often remain undetected (Joseph et al., 2021). Using protein sequence and gene expression data coupled with state-of-the-art deep learning techniques for making predictions enables a large set of applications in digital health, like predicting human age (Ashiqur Rahman et al., 2020) (Mohamadi et al., 2021), or detecting adverse drug-drug interactions (Islam et al., 2021), which is a fundamental drug safety and surveillance problem.

References

1. Emergency Medical Services Shock Index is the Most Accurate Predictor of Patient Outcomes After Blunt Torso Trauma Bardes, J. M., Price, B. S., Adjeroh, D. A., Doretto, G., and Wilson, A. Journal of Trauma and Acute Care Surgery, 2022. abstract bibTeX html doi

   INTRODUCTION: Shock index (SI) and delta shock index (∆SI) predict mortality and blood transfusion in trauma patients. This study aimed to evaluate the predictive ability of SI and ∆SI in a rural environment with prolonged transport times and transfers from critical access hospitals or level IV trauma centers. METHODS: We completed a retrospective database review at an American College of Surgeons verified level 1 trauma center for 2 years. Adult subjects analyzed sustained torso trauma. Subjects with missing data or severe head trauma were excluded. For analysis, poisson regression and binomial logistic regression were used to study the effect of time in transport and SI/∆SI on resource utilization and outcomes. p < 0.05 was considered significant. RESULTS: Complete data were available on 549 scene patients and 127 transfers. Mean Injury Severity Score was 11 (interquartile range, 9.0) for scene and 13 (interquartile range, 6.5) for transfers. Initial emergency medical services SI was the most significant predictor for blood transfusion and intensive care unit care in both scene and transferred patients (p < 0.0001) compared with trauma center arrival SI or transferring center SI. A negative ∆SI was significantly associated with the need for transfusion and the number of units transfused. Longer transport time also had a significant relationship with increasing intensive care unit length of stay. Cohorts were analyzed separately. CONCLUSION: Providers must maintain a high level of clinical suspicion for patients who had an initially elevated SI. Emergency medical services SI was the greatest predictor of injury and need for resources. Enroute SI and ∆SI were less predictive as time from injury increased. This highlights the improvements in en route care but does not eliminate the need for high-level trauma intervention. LEVEL OF EVIDENCE: Therapeutic/care management, level IV.

   @article{bardesPADW2021,
     author = {Bardes, J. M. and Price, B. S. and Adjeroh, D. A. and Doretto, G. and Wilson, A.},
     journal = {Journal of Trauma and Acute Care Surgery},
     title = {Emergency Medical Services Shock Index is the Most Accurate Predictor of Patient Outcomes After Blunt Torso Trauma},
     year = {2022},
     month = mar,
     number = {3},
     pages = {499--503},
     volume = {92},
     doi = {10.1097/ta.0000000000003483},
     publisher = {Ovid Technologies (Wolters Kluwer Health)},
     url = {https://doi.org/10.1097/ta.0000000000003483},
     bib2html_pubtype = {Journals}
   }
   

2. BIBM

   Detecting Drug-Drug Interactions using Protein Sequence-Structure Similarity Networks Islam, S., Abbasi, A., Agarwal, N., Zheng, W., Doretto, G., and Adjeroh, D. A. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021. Oral abstract bibTeX pdf

   Adverse drug events represent a key challenge in public health, especially with respect to drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to this problem have used different types of data, such as drug chemical structure, information about protein targets, side effects, pathways, etc to predict potential interactions between drugs. In this work, we study the question of whether using just genetic information about the drugs can provide significant information about the potential safety profile for a given drug. We propose a novel neural network model to predict adverse drug events using only data about the protein sequence and protein structure associated with the drug targets. We compare the results with those from the state-of-the-art methods on this problem. Our results show that the proposed method is quite competitive, at times outperforming the state-of-the-art.

   @inproceedings{mohamadiDNAbibm,
     abbr = {BIBM},
     author = {Islam, S. and Abbasi, A. and Agarwal, N. and Zheng, W. and Doretto, G. and Adjeroh, D. A.},
     title = {{Detecting Drug-Drug Interactions using Protein Sequence-Structure Similarity Networks}},
     booktitle = {Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine},
     year = {2021},
     bib2html_pubtype = {Conferences},
     wwwnote = {Oral}
   }
   

3. BIBM

   Human Age Estimation from Gene Expression Data using Artificial Neural Networks Mohamadi, S., Doretto, G., Nasrabadi, N. M., and Adjeroh, D. A. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021. Oral abstract bibTeX arXiv

   The study of signatures of aging in terms of genomic biomarkers can be uniquely helpful in understanding the mechanisms of aging and developing models to accurately predict the age. Prior studies have employed gene expression and DNA methylation data aiming at accurate prediction of age. In this line, we propose a new framework for human age estimation using information from human dermal fibroblast gene expression data. First, we propose a new spatial representation as well as a data augmentation approach for gene expression data. Next in order to predict the age, we design an architecture of neural network and apply it to this new representation of the original and augmented data, as an ensemble classification approach. Our experimental results suggest the superiority of the proposed framework over state-of-the-art age estimation methods using DNA methylation and gene expression data.

   @inproceedings{mohamadiDNAbibn,
     abbr = {BIBM},
     author = {Mohamadi, S. and Doretto, G. and Nasrabadi, N. M. and Adjeroh, D. A.},
     title = {{Human Age Estimation from Gene Expression Data using Artificial Neural Networks}},
     booktitle = {Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine},
     year = {2021},
     wwwnote = {Oral},
     arxiv = {2111.02692},
     bib2html_pubtype = {Conferences}
   }
   

4. Coronary artery disease phenotype detection in an academic hospital system setting Joseph, A., Mullett, C., Lilly, C., Armistead, M., Cox, H. J., Denney, M., Varma, M., Rich, D., Adjeroh, D. A., Doretto, G., Neal, W., and Pyles, L. A. Applied Clinical Informatics, 2021. abstract bibTeX doi

   Background - The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH. Objective - We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository. Methods - We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification. Results - CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as “coronary angiography through a vein graft” were more useful than generic terms. Conclusion - We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.

   @article{josephMLACDVRADNP2021aci,
     title = {Coronary artery disease phenotype detection in an academic hospital system setting},
     author = {Joseph, A. and Mullett, C. and Lilly, C. and Armistead, M. and Cox, H. J. and Denney, M. and Varma, M. and Rich, D. and Adjeroh, D. A. and Doretto, G. and Neal, W. and Pyles, L. A.},
     journal = {Applied Clinical Informatics},
     year = {2021},
     number = {1},
     volume = {12},
     pages = {10--16},
     doi = {10.1055/s-0040-1721012},
     bib2html_pubtype = {Journals}
   }
   

5. BB

   Deep learning for biological age estimation Ashiqur Rahman, S., Giacobbi, P., Pyles, L., Mullett, C., Doretto, G., and Adjeroh, D. A. Briefings in Bioinformatics, 2020. abstract bibTeX doi

   Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.

   @article{ashiqurrahmanGPMDA2020bb,
     abbr = {BB},
     title = {Deep learning for biological age estimation},
     author = {Ashiqur Rahman, S. and Giacobbi, P. and Pyles, L. and Mullett, C. and Doretto, G. and Adjeroh, D. A.},
     journal = {Briefings in Bioinformatics},
     month = may,
     year = {2020},
     issn = {1477-4054},
     doi = {10.1093/bib/bbaa021},
     bib2html_pubtype = {Journals}
   }