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MIT Mathematician Develops an Algorithm to Help Treat Diabetes

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  • Jose Oletta
    MIT Mathematician Develops an Algorithm to Help Treat Diabetes The key to managing the disease, which afflicts 29 million people in the U.S., might be in big
    Mensaje 1 de 3 , 14 nov

      MIT Mathematician Develops an Algorithm to Help Treat Diabetes

      The key to managing the disease, which afflicts 29 million people in the U.S., might be in big data

      https://www.smithsonianmag.com/innovation/mit-mathematician-has-developed-algorithm-help-treat-diabetes-180962698/?utm_source=twitter.com&utm_medium=socialmedia
      www.smithsonianmag.com
      When people ask me why I, an applied mathematician, study diabetes, I tell them that I am motivated for both scientific and human reasons. Type 2 diabetes runs in my ...


      image: https://thumbs-prod.si-cdn.com/CCFwLYMANLwWEA4fqI06hwAFo9A=/800x600/filters:no_upscale()/https://public-media.smithsonianmag.com/filer/f2/1c/f21cf4b4-4c5e-41fb-a865-fd1e5ece5574/image-20170320-9114-4eriq8.jpg

      Tools of diabetes
      Tools of diabetes treatment almost always include improved diet and regular exercise. (From www.shutterstock.com)
      By Dimitris Bertsimas, The Conversation
      SMITHSONIAN.COM 
      MARCH 28, 2017

      When people ask me why I, an applied mathematician, study diabetes, I tell them that I am motivated for both scientific and human reasons.

      Type 2 diabetes runs in my family. My grandfather died of complications related to the condition. My mother was diagnosed with the disease when I was 10 years old, and my Aunt Zacharoula suffered from it. I myself am pre-diabetic.

      As a teen, I remember being struck by the fact that my mother and her sister received different treatments from their respective doctors. My mother never took insulin, a hormone that regulates blood sugar levels; instead, she ate a limited diet and took other oral drugs. Aunt Zacharoula, on the other hand, took several injections of insulin each day.

      Though they had the same heritage, the same parental DNA and the same disease, their medical trajectories diverged. My mother died in 2009 at the age of 75 and my aunt died the same year at the age of 78, but over the course of her life dealt with many more serious side effects.

      When they were diagnosed back in the 1970s, there were no data to show which medicine was most effective for a specific patient population.

      Today, 29 million Americans are living with diabetes. And now, in an emerging era of precision medicine, things are different.

      Increased access to troves of genomic information and the rising use of electronic medical records, combined with new methods of machine learning, allow researchers to process large amounts data. This is accelerating efforts to understand genetic differences within diseases – including diabetes – and to develop treatments for them. The scientist in me feels a powerful desire to take part.

      Using big data to optimize treatment

      My students and I have developed a data-driven algorithm for personalized diabetes management that we believe has the potential to improve the health of the millions of Americans living with the illness.

      It works like this: The algorithm mines patient and drug data, finds what is most relevant to a particular patient based on his or her medical history and then makes a recommendation on whether another treatment or medicine would be more effective. Human expertise provides a critical third piece of the puzzle.

      After all, it is the doctors who have the education, skills and relationships with patients who make informed judgments about potential courses of treatment.

      We conducted our research through a partnership with Boston Medical Center, the largest safety net hospital in New England that provides care for people of lower income and uninsured people. And we used a data set that involved the electronic medical records from 1999 to 2014 of about 11,000 patients who were anonymous to us.

      These patients had three or more glucose level tests on record, a prescription for at least one blood glucose regulation drug, and no recorded diagnosis of type 1 diabetes, which usually begins in childhood. We also had access to each patient’s demographic data, as well their height, weight, body mass index, and prescription drug history.

      Next, we developed an algorithm to mark precisely when each line of therapy ended and the next one began, according to when the combination of drugs prescribed to the patients changed in the electronic medical record data. All told, the algorithm considered 13 possible drug regimens.

      For each patient, the algorithm processed the menu of available treatment options. This included the patient’s current treatment, as well as the treatment of his or her 30 “nearest neighbors” in terms of the similarity of their demographic and medical history to predict potential effects of each drug regimen. The algorithm assumed the patient would inherit the average outcome of his or her nearest neighbors.

      If the algorithm spotted substantial potential for improvement, it offered a change in treatment; if not, the algorithm suggested the patient remain on his or her existing regimen. In two-thirds of the patient sample, the algorithm did not propose a change.

      The patients who did receive new treatments as a result of the algorithm saw dramatic results. When the system’s suggestion was different from the standard of care, an average beneficial change in the hemoglobin of 0.44 percent at each doctor’s visit was observed, compared to historical data. This is a meaningful, medically material improvement.

      Based on the success of our study, we are organizing a clinical trial with Massachusetts General Hospital. We believe our algorithm could be applicable to other diseases, including cancer, Alzheimer’s, and cardiovascular disease.

      It is professionally satisfying and personally gratifying to work on a breakthrough project like this one. By reading a person’s medical history, we are able to tailor specific treatments to specific patients and provide them with more effective therapeutic and preventive strategies. Our goal is to give everyone the greatest possible opportunity for a healthier life.

      Best of all, I know my mom would be proud.


      This article was originally published on The Conversation.

      image: https://counter.theconversation.edu.au/content/69606/count.gif?distributor=republish-lightbox-advanced

      The Conversation

      Read more: http://www.smithsonianmag.com/innovation/mit-mathematician-has-developed-algorithm-help-treat-diabetes-180962698/#M11sEUJdXjojY0ub.99
      Give the gift of Smithsonian magazine for only $12! http://bit.ly/1cGUiGv
      Follow us: @SmithsonianMag on Twitter


    • Antonio clemente heimerdinger
      Muchas gracias José es un razonamiento amirablemente actualizado, esto no es mi campo pero los resultados que refieren son admirables, saludos antonio
      Mensaje 2 de 3 , 15 nov
        Muchas gracias José es un razonamiento amirablemente actualizado, esto no es mi campo pero los resultados que refieren son admirables, saludos antonio clemente h

        2017-11-14 18:22 GMT-04:00 Jose Oletta jofeole2@... [medicosdevenezuela] <medicosdevenezuela@yahoogroups.com>:
         

        When people ask me why I, an applied mathematician, study diabetes, I tell them that I am motivated for both scientific and human reasons.

        Type 2 diabetes runs in my family. My grandfather died of complications related to the condition. My mother was diagnosed with the disease when I was 10 years old, and my Aunt Zacharoula suffered from it. I myself am pre-diabetic.

        As a teen, I remember being struck by the fact that my mother and her sister received different treatments from their respective doctors. My mother never took insulin, a hormone that regulates blood sugar levels; instead, she ate a limited diet and took other oral drugs. Aunt Zacharoula, on the other hand, took several injections of insulin each day.

        Though they had the same heritage, the same parental DNA and the same disease, their medical trajectories diverged. My mother died in 2009 at the age of 75 and my aunt died the same year at the age of 78, but over the course of her life dealt with many more serious side effects.

        When they were diagnosed back in the 1970s, there were no data to show which medicine was most effective for a specific patient population.

        Today, 29 million Americans are living with diabetes. And now, in an emerging era of precision medicine, things are different.

        Increased access to troves of genomic information and the rising use of electronic medical records, combined with new methods of machine learning, allow researchers to process large amounts data. This is accelerating efforts to understand genetic differences within diseases – including diabetes – and to develop treatments for them. The scientist in me feels a powerful desire to take part.

        Using big data to optimize treatment

        My students and I have developed a data-driven algorithm for personalized diabetes management that we believe has the potential to improve the health of the millions of Americans living with the illness.

        It works like this: The algorithm mines patient and drug data, finds what is most relevant to a particular patient based on his or her medical history and then makes a recommendation on whether another treatment or medicine would be more effective. Human expertise provides a critical third piece of the puzzle.

        After all, it is the doctors who have the education, skills and relationships with patients who make informed judgments about potential courses of treatment.

        We conducted our research through a partnership with Boston Medical Center, the largest safety net hospital in New England that provides care for people of lower income and uninsured people. And we used a data set that involved the electronic medical records from 1999 to 2014 of about 11,000 patients who were anonymous to us.

        These patients had three or more glucose level tests on record, a prescription for at least one blood glucose regulation drug, and no recorded diagnosis of type 1 diabetes, which usually begins in childhood. We also had access to each patient’s demographic data, as well their height, weight, body mass index, and prescription drug history.

        Next, we developed an algorithm to mark precisely when each line of therapy ended and the next one began, according to when the combination of drugs prescribed to the patients changed in the electronic medical record data. All told, the algorithm considered 13 possible drug regimens.

        For each patient, the algorithm processed the menu of available treatment options. This included the patient’s current treatment, as well as the treatment of his or her 30 “nearest neighbors” in terms of the similarity of their demographic and medical history to predict potential effects of each drug regimen. The algorithm assumed the patient would inherit the average outcome of his or her nearest neighbors.

        If the algorithm spotted substantial potential for improvement, it offered a change in treatment; if not, the algorithm suggested the patient remain on his or her existing regimen. In two-thirds of the patient sample, the algorithm did not propose a change.

        The patients who did receive new treatments as a result of the algorithm saw dramatic results. When the system’s suggestion was different from the standard of care, an average beneficial change in the hemoglobin of 0.44 percent at each doctor’s visit was observed, compared to historical data. This is a meaningful, medically material improvement.

        Based on the success of our study, we are organizing a clinical trial with Massachusetts General Hospital. We believe our algorithm could be applicable to other diseases, including cancer, Alzheimer’s, and cardiovascular disease.

        It is professionally satisfying and personally gratifying to work on a breakthrough project like this one. By reading a person’s medical history, we are able to tailor specific treatments to specific patients and provide them with more effective therapeutic and preventive strategies. Our goal is to give everyone the greatest possible opportunity for a healthier life.

        Best of all, I know my mom would be proud.


        This article was originally published on The Conversation.

        image: https://counter. theconversation.edu.au/ content/69606/count.gif? distributor=republish- lightbox-advanced

        The Conversation

        Read more: http://www.smithsonianmag.com/ innovation/mit-mathematician- has-developed-algorithm-help- treat-diabetes-180962698/# M11sEUJdXjojY0ub.99
        Give the gift of Smithsonian magazine for only $12! http://bit.ly/1cGUiGv
        Follow us: @SmithsonianMag on Twitter



      • Ada Klein
        Dr. Oletta, Researchers from the Institute for Health Metrics and Evaluation tracked the costs associated with 155 diseases for 18 years. Only 20 of those
        Mensaje 3 de 3 , 15 nov

          Dr. Oletta,

          Researchers from the Institute for Health Metrics and Evaluation
          tracked the costs associated with 155 diseases for 18 years.
          Only 20 of those diseases were found responsible for over half of
          all medical expenditures. The top three ones:


          1
          Diabetes is the most expensive condition in terms of total
          dollars spent nationwide, costing $101 billion in diagnosis
          and treatment in 2013. Diabetes-related costs have grown
          36-times faster than those for ischemic heart disease, which
          kills more people than any other condition.

          2
          Ischemic heart disease or coronary artery disease, the
          second-largest source of expenses, cost a total of $88 billion.


          Low back and neck pain together are the third biggest
          source of medical spending in the U.S. And unlike the
          diabetes or heart disease, which tend to affect Americans
          in their late-60s or older, neck and back pain often afflicts
          working adults.
          http://www.cnbc.com/2016/12/27 /diabetes-costing-americans-mo re-than-any-other-disease.html

          Conventional medicine maintains there is no cure for Diabetes,
          because treating diabetics is just too darned profitable.

          As a matter of fact Diabetes is not a difficult disease to prevent
          or reverse
          because it's not really an affliction that "strikes" you
          randomly. It is merely the biological effect of following certain lifestyle
          (bad foods, no exercise) that can be reversed in virtually anyone,
          sometimes in just a few days
          . Many people have done it.

          How to Treat Diabetes - Dr. Michael Greger - Nutritionfacts.org
          Click on transcript

          Dietary Approach to Diabetes - Dr. Neal Barnard
          Dr. Barnard recommends a plant-based diet to reverse diabetes.solo
          (Una dieta derivada de plantas.)



          On Tue, Nov 14, 2017 at 5:22 PM, Jose Oletta jofeole2@... [medicosdevenezuela] <medicosdevenezuela@ yahoogroups.com> wrote:
           

          When people ask me why I, an applied mathematician, study diabetes, I tell them that I am motivated for both scientific and human reasons.

          Type 2 diabetes runs in my family. My grandfather died of complications related to the condition. My mother was diagnosed with the disease when I was 10 years old, and my Aunt Zacharoula suffered from it. I myself am pre-diabetic.

          As a teen, I remember being struck by the fact that my mother and her sister received different treatments from their respective doctors. My mother never took insulin, a hormone that regulates blood sugar levels; instead, she ate a limited diet and took other oral drugs. Aunt Zacharoula, on the other hand, took several injections of insulin each day.

          Though they had the same heritage, the same parental DNA and the same disease, their medical trajectories diverged. My mother died in 2009 at the age of 75 and my aunt died the same year at the age of 78, but over the course of her life dealt with many more serious side effects.

          When they were diagnosed back in the 1970s, there were no data to show which medicine was most effective for a specific patient population.

          Today, 29 million Americans are living with diabetes. And now, in an emerging era of precision medicine, things are different.

          Increased access to troves of genomic information and the rising use of electronic medical records, combined with new methods of machine learning, allow researchers to process large amounts data. This is accelerating efforts to understand genetic differences within diseases – including diabetes – and to develop treatments for them. The scientist in me feels a powerful desire to take part.

          Using big data to optimize treatment

          My students and I have developed a data-driven algorithm for personalized diabetes management that we believe has the potential to improve the health of the millions of Americans living with the illness.

          It works like this: The algorithm mines patient and drug data, finds what is most relevant to a particular patient based on his or her medical history and then makes a recommendation on whether another treatment or medicine would be more effective. Human expertise provides a critical third piece of the puzzle.

          After all, it is the doctors who have the education, skills and relationships with patients who make informed judgments about potential courses of treatment.

          We conducted our research through a partnership with Boston Medical Center, the largest safety net hospital in New England that provides care for people of lower income and uninsured people. And we used a data set that involved the electronic medical records from 1999 to 2014 of about 11,000 patients who were anonymous to us.

          These patients had three or more glucose level tests on record, a prescription for at least one blood glucose regulation drug, and no recorded diagnosis of type 1 diabetes, which usually begins in childhood. We also had access to each patient’s demographic data, as well their height, weight, body mass index, and prescription drug history.

          Next, we developed an algorithm to mark precisely when each line of therapy ended and the next one began, according to when the combination of drugs prescribed to the patients changed in the electronic medical record data. All told, the algorithm considered 13 possible drug regimens.

          For each patient, the algorithm processed the menu of available treatment options. This included the patient’s current treatment, as well as the treatment of his or her 30 “nearest neighbors” in terms of the similarity of their demographic and medical history to predict potential effects of each drug regimen. The algorithm assumed the patient would inherit the average outcome of his or her nearest neighbors.

          If the algorithm spotted substantial potential for improvement, it offered a change in treatment; if not, the algorithm suggested the patient remain on his or her existing regimen. In two-thirds of the patient sample, the algorithm did not propose a change.

          The patients who did receive new treatments as a result of the algorithm saw dramatic results. When the system’s suggestion was different from the standard of care, an average beneficial change in the hemoglobin of 0.44 percent at each doctor’s visit was observed, compared to historical data. This is a meaningful, medically material improvement.

          Based on the success of our study, we are organizing a clinical trial with Massachusetts General Hospital. We believe our algorithm could be applicable to other diseases, including cancer, Alzheimer’s, and cardiovascular disease.

          It is professionally satisfying and personally gratifying to work on a breakthrough project like this one. By reading a person’s medical history, we are able to tailor specific treatments to specific patients and provide them with more effective therapeutic and preventive strategies. Our goal is to give everyone the greatest possible opportunity for a healthier life.

          Best of all, I know my mom would be proud.


          This article was originally published on The Conversation.

          image: https://counter.theconversatio n.edu.au/content/69606/count. gif?distributor=republish-ligh tbox-advanced

          The Conversation

          Read more: http://www.smithsonianmag.com/ innovation/mit-mathematician-h as-developed-algorithm-help-tr eat-diabetes-180962698/#M11sEU JdXjojY0ub.99
          Give the gift of Smithsonian magazine for only $12! http://bit.ly/1cGUiGv
          Follow us: @SmithsonianMag on Twitter



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