Personalized, precision or layered medicine is understood as a medical approach in which patients are classified according to disease subtypes, risks, prognosis, or treatment response using specific diagnostic tests. The terms above are used interchangeably in many publications, but some researchers differentiate between them to highlight particular nuances. The main idea is to base medical decisions on individual patient characteristics (including biomarkers) and not on averages across the entire population. In agreement with the US Food and Drug Administration, the term biomarker was used for the measurable amount or score that could be used as a basis for classifying patients (e.g., genomic changes, molecular markers, disease severity scores, etc.). The basic idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, not population averages.
Personalized Medicine and Machine Learning
Personalized medicine is deeply rooted in data science, particularly with Machine Learning (ML) (often referred to as Artificial Intelligence (AI) in the mainstream media). However, while there has been great enthusiasm for the potential of solutions based on big data and Machine Learning (ML) in recent years, there are only a few examples that have impacted current clinical practice. The lack of impact on clinical training can be attributed mainly to the poor performance of predictive models, difficulty interpreting complex model predictions, and lack of validation through prospective clinical trials that show a clear benefit compared to standard of care. An interdisciplinary effort was identified, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science–based solutions seem to need to be better managed. Parallel to this situation, computational methods must advance further to benefit clinical practice directly.
Drugs or molecules that inhibit specific molecular targets responsible for cancer growth, progression, and spread are used in targeted cancer treatments. These treatments are sometimes referred to as molecularly targeted drugs, targeted molecular therapies, precision medicine. However, the precision medicine paradigm is not limited to targeted treatments, and its real strength lies in identifying and eliminating possible risks before diseases occur. In addition, a focus on maintaining health and healthy–being can significantly reduce health expenditures.
The precision medicine (or personalized medicine) approach is not just about evaluating genomic elements. Of course, the genomic structure is an essential component in precision medicine, but it also has other details such as environment, lifestyle, personal preferences, clinical data. Therefore, the precision medicine approach requires processing a large number and variety of traditional and new health data types. In this way, new health service models can be developed.
A complete change is required to realize the precision medicine approach in today’s medical understanding. First of all, with the abnormal increase in the data collected in health, it may be possible to conduct data–based research and make medicine data–based.
In this respect, data science and extensive data analytics development will be critical to implementing the precision medicine paradigm. At this point, concepts such as data security, privacy, ownership, fluidity, and mutual workability will occupy the plan more. In addition, physicians will need to have specific knowledge of data science and their traditional education. Physicians will increasingly have to become data specialists and perhaps genomics specialists, and they will make decisions based on the patient’s characteristics. However, this is not enough either.
For the precision medicine paradigm to become widespread, it is also necessary to empower individuals, share their data, and contribute to the mass production of science. Today, with the development of digital health devices and online medical websites more than ever, people can manage their health effectively and demand more consumer-friendly experiences.
Artificial Intelligence (AI) approaches that excel at exploring complex relationships between multiple factors provide opportunities. A study from Vanderbilt offered early examples of combining HIMS and genetic data with positive results in cardiovascular disease prediction. Activating phenotype features with Artificial Intelligence (AI) through HIMS or images and matching these features with genetic variants provide a faster diagnosis of genetic diseases. For example, for severely ill infants suspected of having a congenital illness, accurate and rapid diagnosis can be achieved using rapid whole-genome sequencing, and automated phenotyping with NLP enabled.
The power of precision medicine to customize care, particularly the emergence of genotyping, the global use of electronic health records, has created an opportunity to generate new phenotypes. Combined with information from the IHC, these phenotypes will improve the diagnosis and treatment of diseases.
Machine Learning (ML) algorithms need to be developed to predict which patients will need which drug in the light of genomic information. However, genotyping must be done first to customize medicines and dosages. It was among the first examples of convergence between Artificial Intelligence (AI) and precision medicine, as Artificial Intelligence (AI) techniques proved useful for efficient genome interpretation.
Clinicians have used genotype information as a guide to help determine the correct dose of Warfarin. Precision medicine perhaps first demonstrated its power in prescriptions written based on genome information. Likewise, precision oncology treatments rely heavily on patient genomic data to make treatment decisions. Machine Learning (ML); Unprecedented molecular detail combined with next–generation drug development has enabled high–throughput targeted therapy.
Table of content
- Personalized, precision or layered medicine is understood as a medical approach in which patients are classified according to disease subtypes, risks, prognosis, or treatment response using specific diagnostic tests. The terms above are used interchangeably in many publications, but some researchers differentiate between them to highlight particular nuances. The main idea is to base medical decisions on individual patient characteristics (including biomarkers) and not on averages across the entire population. In agreement with the US Food and Drug Administration, the term biomarker was used for the measurable amount or score that could be used as a basis for classifying patients (e.g., genomic changes, molecular markers, disease severity scores, etc.). The basic idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, not population averages.
- Personalized Medicine and Machine Learning
- Personalized medicine is deeply rooted in data science, particularly with Machine Learning (ML) (often referred to as Artificial Intelligence (AI) in the mainstream media). However, while there has been great enthusiasm for the potential of solutions based on big data and Machine Learning (ML) in recent years, there are only a few examples that have impacted current clinical practice. The lack of impact on clinical training can be attributed mainly to the poor performance of predictive models, difficulty interpreting complex model predictions, and lack of validation through prospective clinical trials that show a clear benefit compared to standard of care. An interdisciplinary effort was identified, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science–based solutions seem to need to be better managed. Parallel to this situation, computational methods must advance further to benefit clinical practice directly.
- Drugs or molecules that inhibit specific molecular targets responsible for cancer growth, progression, and spread are used in targeted cancer treatments. These treatments are sometimes referred to as molecularly targeted drugs, targeted molecular therapies, precision medicine. However, the precision medicine paradigm is not limited to targeted treatments, and its real strength lies in identifying and eliminating possible risks before diseases occur. In addition, a focus on maintaining health and healthy–being can significantly reduce health expenditures.
- The precision medicine (or personalized medicine) approach is not just about evaluating genomic elements. Of course, the genomic structure is an essential component in precision medicine, but it also has other details such as environment, lifestyle, personal preferences, clinical data. Therefore, the precision medicine approach requires processing a large number and variety of traditional and new health data types. In this way, new health service models can be developed.
- A complete change is required to realize the precision medicine approach in today’s medical understanding. First of all, with the abnormal increase in the data collected in health, it may be possible to conduct data–based research and make medicine data–based.
- In this respect, data science and extensive data analytics development will be critical to implementing the precision medicine paradigm. At this point, concepts such as data security, privacy, ownership, fluidity, and mutual workability will occupy the plan more. In addition, physicians will need to have specific knowledge of data science and their traditional education. Physicians will increasingly have to become data specialists and perhaps genomics specialists, and they will make decisions based on the patient’s characteristics. However, this is not enough either.
- For the precision medicine paradigm to become widespread, it is also necessary to empower individuals, share their data, and contribute to the mass production of science. Today, with the development of digital health devices and online medical websites more than ever, people can manage their health effectively and demand more consumer-friendly experiences.
- Artificial Intelligence (AI) approaches that excel at exploring complex relationships between multiple factors provide opportunities. A study from Vanderbilt offered early examples of combining HIMS and genetic data with positive results in cardiovascular disease prediction. Activating phenotype features with Artificial Intelligence (AI) through HIMS or images and matching these features with genetic variants provide a faster diagnosis of genetic diseases. For example, for severely ill infants suspected of having a congenital illness, accurate and rapid diagnosis can be achieved using rapid whole-genome sequencing, and automated phenotyping with NLP enabled.
- The power of precision medicine to customize care, particularly the emergence of genotyping, the global use of electronic health records, has created an opportunity to generate new phenotypes. Combined with information from the IHC, these phenotypes will improve the diagnosis and treatment of diseases.
- Machine Learning (ML) algorithms need to be developed to predict which patients will need which drug in the light of genomic information. However, genotyping must be done first to customize medicines and dosages. It was among the first examples of convergence between Artificial Intelligence (AI) and precision medicine, as Artificial Intelligence (AI) techniques proved useful for efficient genome interpretation.
- Clinicians have used genotype information as a guide to help determine the correct dose of Warfarin. Precision medicine perhaps first demonstrated its power in prescriptions written based on genome information. Likewise, precision oncology treatments rely heavily on patient genomic data to make treatment decisions. Machine Learning (ML); Unprecedented molecular detail combined with next–generation drug development has enabled high–throughput targeted therapy.
