“Conclusion In this large study, greater adherence to the Mediterranean diet was associated with longer telomeres. These results further support the benefits of adherence to the Mediterranean diet for promoting health and longevity.”
So, that seems promising … the question….. can you ‘stick with the diet’? Of course, there are other studies that provide other results. How does one resolve the discrepancies?
Stat tuned … Ideas coming soon 🙂
in the meanwhile some people like these dish ideas
Back to Watson for Oncology (WFO). … so today was deep dive day to look at what papers were written specifically re WFO.
So, on Sunday, June 23, 2019, using Google Scholar … the list below is of the main useful things I could find.
- Shows promise
- Not ready for solo flight (i.e. needs clinicians to work with it).
- Benefits from adding diagnostic tests liken GEA (Gene expression assays)
- Keep working on improving WFO, and understand specifics better.
Literature I looked at, will look at it again in more detail, and provide further insights.
- Choi, Y. I., Chung, J. W., Kim, K. O., Kwon, K. A., Kim, Y. J., Park, D. K., … & Sung, K. H. (2019). Concordance Rate between Clinicians and Watson for Oncology among Patients with Advanced Gastric Cancer: Early, Real-World Experience in Korea. Canadian Journal of Gastroenterology and Hepatology, 2019.
- Kim, Y. Y., Oh, S. J., Chun, Y. S., Lee, W. K., & Park, H. K. (2018). Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. PloS one, 13(7), e0200100.
- Schmidt, C. (2017). MD Anderson breaks with IBM Watson, raising questions about artificial intelligence in oncology. JNCI: Journal of the National Cancer Institute, 109(5).
- Zhang, X. C., Zhou, N., Zhang, C. T., Lv, H. Y., Li, T. J., Zhu, J. J., … & Liu, G. (2017). 544P Concordance study between IBM Watson for Oncology (WFO) and clinical practice for breast and lung cancer patients in China. Annals of Oncology, 28(suppl_10), mdx678-001.
- Zou, F., Liu, C. Y., Liu, X. H., Tang, Y. F., Ma, J. A., & Hu, C. H. (2018). Concordance Study between IBM Watson for Oncology and Real Clinical Practice for Cervical Cancer Patients in China: A Retrospective Analysis. Available at SSRN 3287513.
- Somashekhar, S. P., Sepúlveda, M. J., Puglielli, S., Norden, A. D., Shortliffe, E. H., Rohit Kumar, C., … & Ramya, Y. (2018). Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of Oncology, 29(2), 418-423.
- Somashekhar, S. P., Sepúlveda, M. J., Norden, A. D., Rauthan, A., Arun, K., Patil, P., … & Kumar, R. C. (2017). Early experience with IBM Watson for Oncology (WFO) cognitive computing system for lung and colorectal cancer treatment.
- Somashekhar, S. P., Kumarc, R., Rauthan, A., Arun, K. R., Patil, P., & Ramya, Y. E. (2017). Abstract S6-07: Double blinded validation study to assess performance of IBM artificial intelligence platform, Watson for oncology in comparison with Manipal multidisciplinary tumour board–First study of 638 breast cancer cases.
- Liu, C., Liu, X., Wu, F., Xie, M., Feng, Y., & Hu, C. (2018). Using artificial intelligence (Watson for oncology) for treatment recommendations amongst Chinese patients with lung cancer: Feasibility study. Journal of medical Internet research, 20(9), e11087.
- Ross, C., & Swetlitz, I. (2017). IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. STAT News.
- Zauderer, M. G., Gucalp, A., Epstein, A. S., Seidman, A. D., Caroline, A., Granovsky, S., … & Petri, J. (2014). Piloting IBM Watson Oncology within Memorial Sloan Kettering’s regional network.
- Herath, D. H., Wilson-Ing, D., Ramos, E., & Morstyn, G. (2016). Assessing the natural language processing capabilities of IBM Watson for oncology using real Australian lung cancer cases.
- Bach, P., Zauderer, M. G., Gucalp, A., Epstein, A. S., Norton, L., Seidman, A. D., … & Keesing, J. (2013). Beyond Jeopardy!: Harnessing IBM’s Watson to improve oncology decision making.
- Kris, M. G., Gucalp, A., Epstein, A. S., Seidman, A. D., Fu, J., Keesing, J., … & Setnes, M. (2015). Assessing the performance of Watson for oncology, a decision support system, using actual contemporary clinical cases.
looking at citations’ meaningfulness in Tahamtan, Iman, and Lutz Bornmann. “What Do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published between 2006 and 2018.” arXiv preprint arXiv:1906.04588 (2019).
Bertin and Atanassova (2014) showed that in the introduction section, “70 verbs account for 50% of all verb occurrences, and 486 verbs account for 90% of the occurrences”.
Lots and lots of insights and data here ….
note to self. – follow up and look at related work
GitHub’s Octoverse is really providing some serious insight about what’s hot and what’s not with developers. For example: in terms of top projects / contributors:
Top Growing Languages:
Lots more data there … I wish they showed more information, not just the top 10s …
Next on the research reading queue, pointers to applications of AI in Regenerative Medicine. We’ll be including this in discussions.
Wikipedia: Regenerative medicine is a branch of translational research ] in tissue engineering and molecular biology which deals with the “process of replacing, engineering or regenerating human cells, tissues or organs to restore or establish normal function”. This field holds the promise of engineering damaged tissues and organs by stimulating the body’s own repair mechanisms to functionally heal previously irreparable tissues or organs.
Regenerative medicine also includes the possibility of growing tissues and organs in the laboratory and implanting them when the body cannot heal itself. If a regenerated organ’s cells would be derived from the patient’s own tissue or cells, this would potentially solve the problem of the shortage of organs available for donation, and the problem of organ transplant rejection.
Some of the biomedical approaches within the field of regenerative medicine may involve the use of stem cells. Examples include the injection of stem cells or progenitor cells obtained through directed differentiation (cell therapies); the induction of regeneration by biologically active molecules administered alone or as a secretion by infused cells (immunomodulation therapy); and transplantation of in vitro grown organs and tissues (tissue engineering). ]
along these lines, I encountered this interesting title:
Zhavoronkova, Anna A., Polina Mamoshinaa, Quentin Vanhaelena, Morten Scheibye-Knudsene, Alexey Moskalevf and Alex Alipera. “Artificial intelligence for aging and longevity research.” (2018).
Abstract: The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tre- mendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models—extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and re- inforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and ger- oprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker develop- ment, target identification, drug discovery and real world evidence pipeline that may help accelerate and im- prove pharmaceutical research and development practices
Am exploring what really can be said for AI in medicine. There are lots of good things going on … but some reality seems to have set in.
I ran into this conclusion in the paper Deep Learning for Genomics: A Concise Overview by Yue and Wang at Carnegie Mellon. [Yue, Tianwei and Haohan Wang. “Deep Learning for Genomics: A Concise Overview.” CoRR abs/1802.00810 (2018):]
Current applications, however, have not brought about a watershed revolution in genomic research. The predictive performances in most problems have not reach the expec- tation for real-world applications, neither have the interpretations of these abstruse models elucidate insightful knowledge. A plethora of new deep learning methods is constantly being proposed but awaits artful applications in genomics.
I was really hoping we were farther along. Maybe there’s hope … there’s always hope [Elvis: Farther along we’ll know more about it. Farther along we’ll understand why. Cheer up my brother live in the sunshine]. Right now, what I am seeing with Watson for Genomics, and other ‘production systems ‘ suggest lots of work ahead.
Now added a recent search that looked for recent USPTO grants that specified Deep Learning and Medicine …
PATEX 2: Patent Exploration for Artificial Intelligence in Medicine (AIM)
As an amusing element, it picked up this: