01-Oct-2021 | Market Research Store
Medical records play a crucial role in determining precise treatment methods for a person suffering from a specific disease. These records comprise basic information about a patient, his or her health history, and all the medically examined findings, which include treated as well as non-treated allergies. Sometimes, it is hard to search for a crucial medical report at the time of need. Hence, alternatives are always looked for.
A group of scientists from Hasso Plattner Institute for Digital Health at Mount Sinai (HPIMS) has formulated an algorithm with the technical support of the Alzheimer’s Drug Discovery Foundation. The latter organization helps to perform a deep scan of medical records to extract the important medical history of a patient. This algorithm is based on the phenotype Knowledgebase (phekB) that trains the machine to spot disease and associated symptoms, thereby determining the appropriate treatment course. This software operates manually to provide the in-detailed medical history of the patient in one sheet only. Medical professionals and doctors can access the sheet to examine the health of the patient.
The researchers divided the entire process of designing the software into two steps. First, they designed a computer algorithm to search for patients, who have been diagnosed with a particular disease, by specifying symptoms to the system. Later, they created a self-learning algorithm, known as Phe2Vec, to automatically spot the disease phenotype. All the extracted phenotypes were compared based on their effectiveness in identifying and diagnosing disease in a patient. To conclude, the software is based on Phe2Vec algorithm for digitally analyzing the extensive medical records in a health center without any manual labor required by the medical experts. The approach is quick, easy to use, and most accurate that increases the possibility of early diagnosis of a disease, thereby ultimately increasing the survival rate of a person.
The researchers specified that there is a need for further analysis and improvement in the software in order to reduce the pre-treatment steps and that spared time could be utilized in advancing the predictive modeling of the disease.
Read Also:
https://www.marketresearchstore.com/market-insights/chronic-disease-management-market-824731