志望動機書_ネイティブチェック①

さて、TOEFL受験も終わり、出願で残すはSoPの仕上げのみとなった。具体的にやるべきは以下

  • SoPの英語のみならずコンテンツも見て下さるプロの方を探して、チェックを依頼
  • 英語表現をネイティブの方に修正してもらう
  • 志望大学8校に応じた、SoPの作成(なぜその大学院なのか?をアレンジ)

まず、英語に関しては、今留学の書類に関してアドバイスをくださっている慶應の教授に依頼したところ、学生課 国際連携担当という部署にネイティブの方がいると教えていただいたので、早速メール

すぐに、以下のコメントとともに返信がきた。

Your statement is very good. I made some English corrections, and made a comment about one section in the text. I’ve attached my revised version.

Finally, I have one suggestion for the overall content of your statement:
I believe you are a unique candidate because you are a practicing Japanese physician, and I guess there will probably not be many other students like you in the program. It might be a good idea to discuss some aspects of this, such as:
– Do you plan to work in Japan or the US (or both) in the future?
– Why is Stanford the best option for you from the perspective of a Japanese physician and data scientist?
– How is data science studied and applied in the Japanese health care system, and how will studying at Stanford broaden your perspective/knowledge?

This was the thought I had when reading your letter, but it is very good as it is now. If you would like to add some content, I will check it again.

Please let me know if anything is unclear, and feel free to contact me whenever.

Unique candidateという点が、評価されたのは嬉しかった。今回の大学院受験では、分野が統計とデータ解析で今までの医学とは全く異なるので、originalityでいかに勝負できるかが鍵だと思っているので。

また、なぜStanfordなのかの理由がまだ弱いことも分かった。さらに修正を加えようと思う。

というわけで、最新のSoPはバージョン8で下記



また、「それでもあきらめない ハーバードが私に教えてくれたこと」の本の著書で有名な、ハーバード公衆衛生大学院在籍中の林英恵さんは、自分が学生の時に主催したセミナーの講師としてきてくれたこともあり、SoPのチェックを依頼。

すると、ご自身もAffinity英語学院という英語学院で、SoPのチェックを受けて修士課程、博士課程ともに合格されたとの返事をいただき、早速メールでSoPのチェックを問い合わせ。こちらは返信待ち

Personal Statement

  Enhancing people’s wellbeing through individualized healthcare data analysis is the ultimate goal I aim for in my professional career. By accumulating and analyzing a vast number of individual healthcare data, I believe that optimal and personalized healthcare information feedback is possible. I seek to attain this goal through working as a data scientist with a medical background.
  During medical school, I taught myself computer programming and developed an information-sharing and community-making web service for medical students. Since then, I have been fascinated with programming because I can create something new and useful from nothing. I kept teaching myself programming, such as C++, JavaScript, Ruby and other languages. I also developed a software application called Flixy, which aims to raise patients’ drug adherence. It synchronizes a drug case with a mobile app via Bluetooth, and automatically records drug intake of the patient (web site: http://flixy.co/). I also taught myself the R language, and showed my competency through an internship in a health insurance company. I analyzed the health checkup data and medical expenditure data, and improved the efficacy of the company’s prevention program. From these experiences, I came to believe that IT has strong potential to improve the quality of healthcare, and I myself want to engage in this field.
  After university, I started working as a clinical physician. Working as a physician was rewarding, and at first I felt proud just by prescribing treatments recommended in research papers. I searched for papers whenever I encountered clinical problems, and I came to believe undoubtedly that having a medical paper supporting a clinical decision is the most important thing in clinical practice. One day, I encountered a case of a young male with intraventricular thrombus having developed acute cardioembolic stroke. The role of immediate anticoagulation in this case is controversial. As usual, I did some research and found papers on this matter. However, as I critically read these papers, I noticed that the randomized control trials described in these papers were not applicable to the patient at hand. This patient was significantly younger than the studied population, and his race was different. Moreover, his past medical history did not meet inclusion criteria of these studies. I became perplexed. How on earth can I decide optimal treatments for this patient? Medical papers I had relied on so far did not back up me in this case. I am still not sure whether I provided optimal treatments for this patient. During my residency, I often encountered similar cases. Physicians nowadays cannot always provide personally-tailored, optimal, and evidence-based treatment for each patient—and I found this very upsetting. Treatments recommended by randomized control trials are mostly one-size-fits-all models. External validities such as race, age, or preexisting conditions are often not brought in to consideration; thus, evidence provided by clinical trials does not always prove a treatment’s effectiveness on all patients.
  Coincidentally, I came across the concept of big data driven medicine in a TV program. It described a big data driven system that predicts baby infection in a neonatal ICU. I was struck by this introduction to big data medicine, and became increasingly interested through researching more about the emerging medical revolutions made possible by big data analysis. I have also read that big data analysis, combined with machine learning methodologies, enables personalized, actionable predictions. Therefore, I now strongly believe that big data analysis can play a crucial role in reducing medical uncertainty. My acquiring more profound knowledge on data analysis is imperative in achieving my goal. I also believe that a medical professional engaging in the data analysis field would be remarkably valuable for the field.
  I became deeply interested in Stanford’s program because there are several laboratories that share similar interests with me, particularly the labs of professors Wong, Hastie, and Tibishirani. Furthermore, this program offers not only general statistics courses, but also domain-specialized course, which is unique to this graduate school. The courses “Analytics for Big Data”, and “Data Driven Medicine” are especially relevant to my interests. Furthermore, the internationally high reputation of the department attracts diverse and competent students, and I believe studying in this environment will be very stimulating and exciting.
  After completing my degree, I plan to engage in the healthcare IT industry, and create a system which automatically evaluates disease risk from patient data. This system calculates the disease risks, optimal treatment, and predictive prognosis from patient data such as age, sex, physical data, and preexisting conditions. It would also extract similar cases from previously accumulated data and predict what physicians would want to evaluate in the new case.
  I look forward to joining Stanford University as a graduate student in your esteemed department, which would be a great step towards my ultimate goal.