@article{10.11648/j.ajcem.20200806.13,
  author = {Shailendra Vaishampayan and Joshua Joshi and Amruta Vaishampayan and Gulnaz Shaikh},
  title = {Confirming merc solubilis as a genus epidemicus in the Evolving Pandemic Using a Mathematical Model Based Upon Machine Learning},
  journal = {American Journal of Clinical and Experimental Medicine},
  volume = {8},
  number = {6},
  pages = {111-120},
  doi = {10.11648/j.ajcem.20200806.13},
  url = {https://doi.org/10.11648/j.ajcem.20200806.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcem.20200806.13},
  abstract = {Background: Advent of a novel pandemic requires development of faster medicine discovery protocol compared to traditional approach. Normally these are placebo controlled clinical trials, such trials usually involve high risk and a lot of time and money and repetitive exposure of the patients involved. In this study we gathered all the pathognomonic features of COVID-19 and translated them to homeopathic clinical features by using the known technique of repertorization, using a software. Top 10 ranked remedies were selected for further exploration. A surrogate model was created for simulation based on real patient data available in which all patients received a random combination of ranked repertorized homeopathic medicine. This output was then fed to a Neural Network. The NN learnt by recognizing patterns that mapped to patients’ initial state to the results of remedies administered, fluctuations were averaged out and different patient features were discovered. Thus, enabling the NN to better predict optimum homeopathy remedies than the traditional method stated before. Method: We designed a mathematical model based upon the principles of machine learning and created a virtual clinical trial first of 200 patients and then updated it to 800 in lieu of a real one. The Results of these Surrogate Digital Clinical trial [SDCT] were fed to a neural network. The Neural Network Clinical Learning [NNCL], clearly gave us a list of drugs and a possible genus epidemicus for this covid 19. These results were compared with actual field results to a data of 130 patients of covid like illnesses, covid or pneumonia treated on OPD basic or through tele medicine. Results: The conclusion was reached by comparing the simulated clinical trials, predictions by the NN and findings in the observational studies. Although the model shows reasonable stability, it is presented as a proof of concept, which should be further rigorously studied and tested by other homeopathic practitioners for further optimization if required. In this study merc sol merged prominently as a genus epidemicus. A further change in the remedy in the reference to a possible second or third wave could be predicted by adding some valuable clinical data to the model. Conclusion: This study could resolve many issues faced by homoeopathic practitioners across the globe and could predict a fairly accurate results making us better prepare in the field.},
 year = {2020}
}

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TY  - JOUR
T1  - Confirming merc solubilis as a genus epidemicus in the Evolving Pandemic Using a Mathematical Model Based Upon Machine Learning
AU  - Shailendra Vaishampayan
AU  - Joshua Joshi
AU  - Amruta Vaishampayan
AU  - Gulnaz Shaikh
Y1  - 2020/12/22
PY  - 2020
N1  - https://doi.org/10.11648/j.ajcem.20200806.13
DO  - 10.11648/j.ajcem.20200806.13
T2  - American Journal of Clinical and Experimental Medicine
JF  - American Journal of Clinical and Experimental Medicine
JO  - American Journal of Clinical and Experimental Medicine
SP  - 111
EP  - 120
PB  - Science Publishing Group
SN  - 2330-8133
UR  - https://doi.org/10.11648/j.ajcem.20200806.13
AB  - Background: Advent of a novel pandemic requires development of faster medicine discovery protocol compared to traditional approach. Normally these are placebo controlled clinical trials, such trials usually involve high risk and a lot of time and money and repetitive exposure of the patients involved. In this study we gathered all the pathognomonic features of COVID-19 and translated them to homeopathic clinical features by using the known technique of repertorization, using a software. Top 10 ranked remedies were selected for further exploration. A surrogate model was created for simulation based on real patient data available in which all patients received a random combination of ranked repertorized homeopathic medicine. This output was then fed to a Neural Network. The NN learnt by recognizing patterns that mapped to patients’ initial state to the results of remedies administered, fluctuations were averaged out and different patient features were discovered. Thus, enabling the NN to better predict optimum homeopathy remedies than the traditional method stated before. Method: We designed a mathematical model based upon the principles of machine learning and created a virtual clinical trial first of 200 patients and then updated it to 800 in lieu of a real one. The Results of these Surrogate Digital Clinical trial [SDCT] were fed to a neural network. The Neural Network Clinical Learning [NNCL], clearly gave us a list of drugs and a possible genus epidemicus for this covid 19. These results were compared with actual field results to a data of 130 patients of covid like illnesses, covid or pneumonia treated on OPD basic or through tele medicine. Results: The conclusion was reached by comparing the simulated clinical trials, predictions by the NN and findings in the observational studies. Although the model shows reasonable stability, it is presented as a proof of concept, which should be further rigorously studied and tested by other homeopathic practitioners for further optimization if required. In this study merc sol merged prominently as a genus epidemicus. A further change in the remedy in the reference to a possible second or third wave could be predicted by adding some valuable clinical data to the model. Conclusion: This study could resolve many issues faced by homoeopathic practitioners across the globe and could predict a fairly accurate results making us better prepare in the field.
VL  - 8
IS  - 6
ER  - 

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