Volter Longo has some interesting new ideas regarding how we look at life expectancy, lifespan. He recommends thinking about spans in terms of youthspan (peak health 20-60) and Healthspan (oriented to disease free stage and the older phase (65-120).
check – Longo VD. Programmed longevity, youthspan, and juventology. Aging Cell. 2019;18:e12843. https://doi.org/10.1111/acel.12843
also check David Sinclair’s new book Lifespan: Why We Age―and Why We Don’t Have To — is a really great book! Highly recommended …. packed with information … some should be taken with a grain of salt or maybe with a Sirtuin activator like NMN 🙂
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.
Just finished looking at Matt Hagen’s 2014 “Biological and clinical data integration and its applications in healthcare.” PhD dissertation. This is a great piece of work … You can find it here.
While its around 5 years old, the insights and discussion are excellent. I like the detailed breakdown of how different ontologies and vocabularies align (and how things fall through the cracks). I liked the discussion of using Neo4j to analyze relationships and simplify searches and relationship mappings.
Particularly liked the discussion of using ontologies. to” facilitate improved prioritization of intensive care admissions and accurate clustering of multimorbidity conditions”. THIS IS BIG! with enormous potential.
Discussion of his BioSPIDA relational database translator and its contrast with the separate Entrez Gene, Pubmed, CDD, Refseq, MMDB, and Biosystems NCBI databases.
His Table 7.2: Descriptions of patient clusters is rather illuminating, as his discussion and analysis of ICU Electronic Health Records and findings associated with morbidity outcomes.
For example Cluster 1 contains the following Most Prevalent Conditions: Coronary arteriosclerosis, Hypercholesterolemia, Diabetes, Gastroesophageal reflux disease, Atrial fibrillation, Hyperlipidemia, Tobacco dependence. Which led to the following Most Prevalent Procedures: Catheterization of left heart, Cardiopulmonary bypass operation, Angiocardiography of left heart,.
I am surprised this work is not cited as much as it should be!. IMHO, this work definitely should be used as blueprint for additional investigations.
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