Introduction: Big Data

We find ourselves enveloped in an ever-expanding universe of data. The pulse of life—whether through the rhythmic beat of a heart monitor, the ATCG sequences of our genetic code, or the detailed chronicles of our healthcare encounters—is being continually recorded as a seemingly endless stream of 1s and 0s. A mere decade ago, the healthcare sector managed vast, but conceivable amounts of data. Yet, with an exponential surge in digital touchpoints, the volume of data has exploded at a rate far outpacing traditional methods of interpretation.

This brings us to “Big Data” and the pivotal role of computational approaches, be it clinical informatics or advanced genomics, that aid in making sense of this information overload. By leveraging big data with these techniques, we have the potential to discern patterns that can revolutionize clinical decision-making and enhance patient care. But the path to harnessing this potential is riddled with complexities. It is not a matter of a singular solution, but rather a nuanced array of ever-developing techniques, many of which are spotlighted in this issue. Our aim in this issue is to illuminate the multifaceted ways in which researchers are harnessing big data to pave the way for medical advancements, whether through genetic discovery, rigorous clinical informatics, or innovative applications of artificial intelligence.

We begin our exploration within—with our genetic code. The field of genetics has long been at the forefront of big data research, from landmark efforts like the Human Genome Project to vast collections of single nucleotide polymorphism array data underlying genome wide association studies. Advances in DNA sequencing have resulted in groundbreaking discoveries in human genetics, some of which are featured in this issue. Our issue includes research ranging from an in-depth genetic analysis of spondylocarpotarsal synostosis syndrome revealing the extensive influence of the FLNB gene, to the pinpointing of a genetic variant causing Laron syndrome in a Pakistani family, to the intricate exploration of intellectual disability from congenital hypothyroidism (Shahid et al., Shabbir et al., and Naqvi et al., respectively). In these studies, the authors made use of cutting-edge sequencing and variant detection technologies to turn billions of bytes of data detailing As, Ts, Cs, and Gs into new genetic discoveries which enhance our understanding of human biology and provide direct benefits for patient care.

Moving beyond genetics, we come to clinical informatics, a discipline which involves aggregating and synthesizing data across hundreds to thousands of patients to deliver actionable insights. In this issue, two articles exemplify this promise. One highlights the indispensable role of clinical informatics in critical care, illustrating how data-rich environments like intensive care units have emerged as hubs for innovations in data visualization and clinical decision support tools, particularly with the integration of techniques such as machine learning (Nadkarni and Sakhuja). Another article provides perspective on the Aortic Institute’s clinical database, exploring how both traditional and innovative analytical methods can be deployed to redefine our understanding and approach to thoracic aortic aneurysm treatments (Zafar et al.). These articles highlight the pivotal influence of clinical informatics in improving healthcare processes.

Beyond optimizing clinical processes, big data can also help us uncover important health disparities which may be overlooked in routine interactions. Multiple articles in our issue highlight the impact of sociodemographic factors on healthcare delivery and outcomes. Mohammed et al. show that some racial-ethnic groups receive fewer life-sustaining treatments in the intensive care unit setting, underscoring the urgency for improved equity in healthcare delivery. Moreover, Courchesne-Krak et al. utilized a large dataset to demonstrate that demographic factors predicted the principal reason for clinical visits related to substance use diagnoses in the primary and psychiatric care settings.

Moving to some of the most recent developments in big data, we focus on artificial intelligence (AI). The field of AI harnesses techniques such as neural networks and deep learning to develop representations of data and solve problems which are intractable for traditional mathematical or data-driven models. Recent large language models, like ChatGPT, offer promising prospects, especially in deciphering, structuring, and generating huge volumes of complex clinical text. This issue features an exploration into the potential of ChatGPT in rendering radiology reports comprehensible for patients, providing early evidence that AI models could improve patient-doctor communication (Amin et al.). Furthermore, Knapp et al. present the perspective that the field of ophthalmology can be a beacon for big data applications, with significant strides made using the IRIS Registry Database, setting a precedent for other specialties. Finally, deep learning techniques provide pivotal insights, showcased through a study comparing lumpectomy and mastectomy in breast cancer treatment, indicating the comparable efficacy of both interventions (Wang et al.).

Interestingly, the revolution does not stop at clinical patient care. The intersection of big data and AI also brings potential innovations to the academic realm. In our issue, Biswas et al. provide an illustration of the potential for ChatGPT in streamlining the academic journal review process. While still in nascent stages, AI-assisted reviews present a promising glimpse into the future of academic publishing, demonstrating the capacity of AI models to aid human reviews by automating or improving the identification of shortcomings in research articles.

While the horizons of big data in medicine seem limitless, challenges persist. There remains a need for standardized data collection and integration approaches to better connect information across research and hospital networks. Moreover, as we continue to embrace this data-driven era, the ethical nuances of data sharing and patient privacy cannot be overlooked. Nevertheless, the exciting convergence of genomics, clinical informatics, and AI provides unparalleled opportunities, be it in predictive modeling, refined diagnostics, or targeted treatments. As we stand at this juncture, the trajectory of big data promises a transformative reshaping of medicine’s landscape. We cordially invite our readers to embark on this journey with us—to dive into these articles, and through critical discourse, contribute to the inspiring intersection of big data, biology, and medicine.

Glossary

AI

artificial intelligence

References

Schoenmakers, N; Alatzoglou, KS; Chatterjee, VK; Dattani, MT.Recent advances in central congenital hypothyroidism.. J Endocrinol. .2015.Dec;;227((3):):R51–71.10.1530/JOE-15-03411479-680526416826

Rastogi, MV; LaFranchi, SH.Congenital hypothyroidism.. Orphanet J Rare Dis. .2010.Jun;;5((1):):17.10.1186/1750-1172-5-171750-117220537182

Waller, DK; Anderson, JL; Lorey, F; Cunningham, GC.Risk factors for congenital hypothyroidism: an investigation of infant’s birth weight, ethnicity, and gender in California, 1990-1998.. Teratology. .2000.Jul;;62((1):):36–41.10.1002/1096-9926(200007)62:1<36::AID-TERA8>3.0.CO; 2-W0040-370910861631

Grosse, SD; Van Vliet, G.Prevention of intellectual disability through screening for congenital hypothyroidism: how much and at what level?. Arch Dis Child. .2011.Apr;;96((4):):374–9.10.1136/adc.2010.1902801468-204421242230

Iqbal, Z; Neveling, K; Razzaq, A; Shahzad, M; Zahoor, MY; Qasim, M; et al.Targeted next generation sequencing reveals a novel intragenic deletion of the TPO gene in a family with intellectual disability.. Arch Med Res. .2012.May;;43((4):):312–6.10.1016/j.arcmed.2012.01.0111873-548722387573

Cangul, H; Aycan, Z; Olivera-Nappa, A; Saglam, H; Schoenmakers, NA; Boelaert, K; et al.Thyroid dyshormonogenesis is mainly caused by TPO mutations in consanguineous community.. Clin Endocrinol (Oxf). .2013.Aug;;79((2):):275–81.10.1111/cen.121271365-226523236987

Park, SM; Chatterjee, VK.Genetics of congenital hypothyroidism.. J Med Genet. .2005.May;;42((5):):379–89.10.1136/jmg.2004.0241581468-624415863666

Bakker, B; Bikker, H; Vulsma, T; de Randamie, JS; Wiedijk, BM; De Vijlder, JJ.Two decades of screening for congenital hypothyroidism in The Netherlands: TPO gene mutations in total iodide organification defects (an update).. J Clin Endocrinol Metab. .2000.Oct;;85((10):):3708–12.10.1210/jcem.85.10.68780021-972X11061528

Castanet, M; Polak, M; Bonaïti-Pellié, C; Lyonnet, S; Czernichow, P; Léger, J; AFDPHE (Association Française pour le Dépistage et la Prévention des Handicaps de l’Enfant).Nineteen years of national screening for congenital hypothyroidism: familial cases with thyroid dysgenesis suggest the involvement of genetic factors.. J Clin Endocrinol Metab. .2001.May;;86((5):):2009–14.10.1210/jcem.86.5.75010021-972X11344199

Mittal, K; Rafiq, MA; Rafiullah, R; Harripaul, R; Ali, H; Ayaz, M; et al.Mutations in the genes for thyroglobulin and thyroid peroxidase cause thyroid dyshormonogenesis and autosomal-recessive intellectual disability.. J Hum Genet. .2016.Oct;;61((10):):867–72.10.1038/jhg.2016.621435-232X27305979

Khan, A; Umair, M; Sharaf, RA; Khan, MI; Ullah, A; Abbas, S; et al.Novel homozygous variant in the TPO gene associated with congenital hypothyroidism and mild-intellectual disability.. Hum Genome Var. .2020.Nov;;7((1):):41.10.1038/s41439-020-00129-32054-345X33298898

APA.American Psychiatric Association, Diagnostic and statistical manual of mental disorders. 5th Ed., Washington, DC. .2013..

Yıldız Bölükbaşı, E; Afzal, M; Mumtaz, S; Ahmad, N; Malik, S; Tolun, A.Progressive SCAR14 with unclear speech, developmental delay, tremor, and behavioral problems caused by a homozygous deletion of the SPTBN2 pleckstrin homology domain.. Am J Med Genet A. .2017.Sep;;173((9):):2494–9.10.1002/ajmg.a.383321552-483328636205

Shabbir, RM; Nalbant, G; Ahmad, N; Malik, S; Tolun, A.HomozygousCHST11mutation in chondrodysplasia, brachydactyly, overriding digits, clino-symphalangism and synpolydactyly.. J Med Genet. .2018.Jul;;55((7):):489–96.10.1136/jmedgenet-2017-1050031468-624429514872

Afroze, B; Humayun, KN; Qadir, M.Newborn screening in Pakistan—lessons from a hospital-based congenital hypothyroidism screening programme.. Ann Acad Med Singapore. .2008.;37:114e123.

Huo, K; Zhang, Z; Zhao, D; Li, H; Wang, J; Wang, X; et al.Risk factors for neurodevelopmental deficits in congenital hypothyroidism after early substitution treatment.. Endocr J. .2011.;;58((5):):355–61.10.1507/endocrj.k10e-38410.1507/endocrj.K10E-3841348-454021467693

Seo, MK; Yoon, JS; So, CH; Lee, HS; Hwang, JS.Intellectual development in preschool children with early treated congenital hypothyroidism.. Ann Pediatr Endocrinol Metab. .2017.Jun;;22((2):):102–7.10.6065/apem.2017.22.2.1022287-101228690988

Riazuddin, S; Hussain, M; Razzaq, A; Iqbal, Z; Shahzad, M; Polla, DL; et al.UK10K.Exome sequencing of Pakistani consanguineous families identifies 30 novel candidate genes for recessive intellectual disability.. Mol Psychiatry. .2017.Nov;;22((11):):1604–14.10.1038/mp.2016.1091476-557827457812

Fu, C; Xie, B; Zhang, S; Wang, J; Luo, S; Zheng, H; et al.Mutation screening of the TPO gene in a cohort of 192 Chinese patients with congenital hypothyroidism.. BMJ Open. .2016.May;;6((5):):e010719.10.1136/bmjopen-2015-0107192044-605527173810

Wang, H; Wang, W; Chen, X; Shi, H; Shi, Y; Ding, G.Screening and functional analysis of TPO gene mutations in a cohort of Chinese patients with congenital hypothyroidism.. Front Endocrinol (Lausanne). .2021.Dec;;12(:774941.10.3389/fendo.2021.7749411664-239235002963

Nemescu, D; Tanasa, IA; Stoian, DL; Navolan, DB; Vinturache, AE.Conservativein uterotreatment of fetal dyshormonogenetic goiter with levothyroxine, a systematic literature review.. Exp Ther Med. .2020.Sep;;20((3):):2434–8.10.3892/etm.2020.87941792-098132765729

van Trotsenburg, P; Stoupa, A; Léger, J; Rohrer, T; Peters, C; Fugazzola, L; et al.Congenital Hypothyroidism: A 2020-2021 Consensus Guidelines Update-An ENDO-European Reference Network Initiative Endorsed by the European Society for Pediatric Endocrinology and the European Society for Endocrinology.. Thyroid. .2021.Mar;;31((3):):387–419.10.1089/thy.2020.03331557-907733272083

Alofi, RM; Alrohaily, LS; Jan, RA; Alsaedi, SL; Mahrous, FA; Alreefi, MM.Adherence to Levothyroxine Treatment Among Patients With Hypothyroidism in Madinah, Saudi Arabia: A Cross-Sectional Study.. Cureus. .2023.Jun;;15((6):):e40686.10.7759/cureus.406862168-818437485132

Kumar, R; Shaukat, F.Adherence to levothyroxine tablet in patients with hypothyroidism.. Cureus. .2019.May;;11((5):):e4624.10.7759/cureus.46242168-818431312550

El Helou, S; Hallit, S; Awada, S; Al-Hajje, A; Rachidi, S; Bawab, W; et al.Adherence to levothyroxine among patients with hypothyroidism in Lebanon.. East Mediterr Health J. .2019.Apr;;25((3):):149–59.10.26719/emhj.18.0221687-163431054225