Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence.

With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era.

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.

Clinical and Genomic Evaluation of 207 Genetic Myopathies in the Indian Subcontinent.

Objective: Inherited myopathies comprise more than 200 different individually rare disease-subtypes, but when combined together they have a high prevalence of 1 in 6,000 individuals across the world. Our goal was to determine for the first time the clinical- and gene-variant spectrum of genetic myopathies in a substantial cohort study of the Indian subcontinent. Methods: In this cohort study, we performed the first large clinical exome sequencing (ES) study with phenotype correlation on 207 clinically well-characterized inherited myopathy-suspected patients from the Indian subcontinent with diverse ethnicities. Results: Clinical-correlation driven definitive molecular diagnosis was established in 49% (101 cases; 95% CI, 42-56%) of patients with the major contributing pathogenicity in either of three genes, GNE (28%; GNE-myopathy), DYSF (25%; Dysferlinopathy), and CAPN3 (19%; Calpainopathy). We identified 65 variant alleles comprising 37 unique variants in these three major genes. Seventy-eight percent of the DYSF patients were homozygous for the detected pathogenic variant, suggesting the need for carrier-testing for autosomal-recessive disorders like Dysferlinopathy that are common in India. We describe the observed clinical spectrum of myopathies including uncommon and rare subtypes in India: Sarcoglycanopathies (SGCA/B/D/G), Collagenopathy (COL6A1/2/3), Anoctaminopathy (ANO5), telethoninopathy (TCAP), Pompe-disease (GAA), Myoadenylate-deaminase-deficiency-myopathy (AMPD1), myotilinopathy (MYOT), laminopathy (LMNA), HSP40-proteinopathy (DNAJB6), Emery-Dreifuss-muscular-dystrophy (EMD), Filaminopathy (FLNC), TRIM32-proteinopathy (TRIM32), POMT1-proteinopathy (POMT1), and Merosin-deficiency-congenital-muscular-dystrophy-type-1 (LAMA2). Thirteen patients harbored pathogenic variants in >1 gene and had unusual clinical features suggesting a possible role of synergistic-heterozygosity/digenic-contribution to disease presentation and progression. Conclusions: Application of clinically correlated ES to myopathy diagnosis has improved our understanding of the clinical and genetic spectrum of different subtypes and their overlaps in Indian patients. This, in turn, will enhance the global gene-variant-disease databases by including data from developing countries/continents for more efficient clinically driven molecular diagnostics.