Machine learning to predict rare diseases in the population

Rare diseases are being the main and major problem that i want to discuss, specifically in women, due to the fact that they are being neglected and misdiagnosed in the medical field, hence which affects their fertility, childbirth and childbearing capabilities. 

My solution is based on machine learning techniques and technology which applies and uses an algorithm based on several parameters and factors including genetical, demographical, geographical, laboratory and other imaging methods ( prenatal screening) that helps identifying which patients are suffering or being subjects of rare diseases. By adapting this technology, there will be a limitation for this type of doseases to progress.

This solution is mainly addressed to patients mainly women suffering from rare diseases and not receiving the proper treatment due to lack of knowledge, medical experience and skills. This solution will address their needs by giving them a chance to be treated and knowing their symptoms better hence cooperating with the new challenges that they might face in the near future. 

By adapting this technology, patients as well as the physicians will have a better look and overview on rare diseases, they would be able to understand the changes they will be suffering from and will be able to deal with it, from one hand, while on the other hand, physicians will have the ability to participate in the diagnosis odyssey and be able to take part in decision making in order to help their patients properly.

My new technology is facing so many financial problems. Due to the fact that I am coming from Lebanon,  and due to current economic crisis, there is a lack of financial support, patients with rare diseases are not being supported from the state, as well as this technology requires a lot of reimbursement. Additional to the financial problems, there is a medical problem, with the lack of clinical researches, skills ans knowledge, many doctors are misdiagnosing their patients with other diseases, hence patients would receive different type of treatments, which at the end will put their lives under risk and increase the mortality rate. Not to forget the cultural barriers, where the community is not being informed about the problems of patients with rare diseases, their families and doctors in order to increase the tolerance, volunteer participation and promoting public participation in different social activities. 

My solution will help medicine over all, by using this predictors, patients with rare diseases would be highly recognized, hence the proper treatments would be given. Machine Learning techniques would specify through genetical predictors if the new generation of women would suffer from any rare diseases or if they would transmit any of these rare disease to their children. Knowing well the Predictors would target the populations under risk, and decrease the risk of their hospitalizations in the future. Machine learning will peovide for each new predictor what is the best treatment, solution or procedure to limit the progression of the problem and to stabilise the disease from their evolution.

My impact goals as future Obstetrician to help women with rare diseases who are being neglected and mistreated, in making good decisions for example take part in childbirth decision, adoption, preparing women with rare diseases for future pregnancies, avoid risk of neonatal mortality and intrauterine growth retardation and defect, and neonatal ICU admission.

Goal 1: specifying the patients or women woth rare diseases. Collecting medical data and records is an essential step to specify the population under risk of having rare diseases, as well as selecting the Predictors would be the best solution to highlight the families or races that sudder from rare diseases hence supporting them.

Goal 2: provide good doagnosis for this target population by selecting the main symtpoms.

Goal 3: provide the proper treatment and help in developing and producing new drugs that limit from the disease evolution and make rare diseases more understandable and easy to deal with.

Goal 4: protect women with rare diseases and provide healthy environment for them and their families.

Theory of changes: 

Increase awareness and support early and accurate doagnosis. 

Support drug development and clinical trials.

Provide links to resources and Create digital applications to raise awareness.

The long term outputs: 

Integrate rare diseases in the universities curriculum.

Mandatory teaching program about RDs.

Train medical students on the diagnostic pathways of RDs as well as on the orphan drugs.

Include questions referring to rare diseases in their examinations and focus on student’s clinical and critical reasoning.

Postgraduate specialization of RDs and emphasize lectures and e learning on rare diseases. 

My solution is based on machine learning techniques or AI that helps identifying Predictors and parameters for rare diseases.

I don't have members in my team, as this topic is not highly discussed in Lebanon, and physicians usually try to avoid it as they don't own the proper knowledge and information and due to the fact that they are lacking of proper skills and dont have the eagerness or enthusiasm to do any researches.

My technology requires a lot of reimbursement and researches as the target population is quite big, the resources would be financially dependent, culturally and economically. I am trying to reach out my patients through media, by providing good sources and information through videos and presentations that would inspire both patients and physicians as well as through lectures and organisations that discuss mainly women's health and life.

I will try to bring money from social media, hospitals, health organisations and NGOs, and through public donations that actually will help in supporting financially this technology.

I haven't received any financial help yet, however this technology has reached out so many students, medic residents researches, doctors, patients and their families and i think that it helped them in some way by providing help to each other as well as teaching each other the importance of knowing this type of diseases.