藤原進之介です。このページでは自治医科大学2018年度の数学過去問を解答・解説つきで無料公開しています。
英語 問題
英語 解答
問題PDFをテキストでも確認
自治医科大学 2018年度 数学入試問題の文字起こし・出題分析
公開中の問題PDFを検索・復習しやすいよう文字データ化し、読み取れた語句から出題分野を整理しました。
- 図形・三角関数
- 指数・対数
- 微分・積分
- 確率・場合の数
この年度の出題範囲と傾向
2018年度の問題PDFでは、図形・三角関数、指数・対数、微分・積分、確率・場合の数に関する語句や設問を確認できました。自治医科大学の公開PDFを年度横断で調べると、図形・三角関数は19年度、指数・対数は19年度、微分・積分は19年度で検出されており、複数年を比較して対策したい分野です。
分野判定はPDFから読み取れた語句に基づく集計です。数式・記号・図表は文字変換で崩れる場合があるため、正確な条件は必ず原本PDFで確認してください。
問題文の文字起こし
問1画像OCR
検出分野: 図形・三角関数、指数・対数、微分・積分、確率・場合の数
A 学 試 BR A Bw
外 。 国 詩
ne
平成30年1月22 日 13 時10 分一14 時10分
注 意 事項
a
a 1 試験開始の合図があるまで, この問題氏子を開かないこと。
2 この問題國子は表紙・白紙を除き 13 ページである。落丁, AT, 印刷不鮮明の
箇所等があった場合は申し出ること。
3 解答には必ず黒鉛筆(またはシャープペンシル) を使用すること。
4 解答は, 各設問どとに一つだけ選び, 解答用紙の所定の解答欄の該当する記号を
座りつぶすこと。
5 MATES SBA, 消しゴムできれいに消すこと。
6 忠督員の指示に従って, 問題田子の表紙の指定欄に受験番号を記入し, 解答用紙
の指定欄に受験番号. 受験番号のマーク, 氏名を記入すること。
7 この問題骨子の余折は, 章季用に使用してよい。 ただし, 切り離してはならない。
8 解答用紙およびこの問題違子は, 持ち帰ってはならない。
el
上の柏内に受験番号を記入しなさい。
やMI3(078一50)
] . 次の英文を続み, 1~ 8 の問いに答えなさい。
Paul Tang was with his wife in the hospital just after her knee replacement
surgery, a procedure performed on about 700, 000 people in the U.S. every year.
The surgeon came by, and Tang, who is himself a primary-care physician, asked
when he expected her to be back at her normal routines, (give) his experience
with patients like her. The surgeon kept giving vague ams “Finally it
hit me,” says Tang, “He didn’t know.” Tang would soon learn that most.
physicians don’t know how their patients do in the ordinary measures of life back
at home and at work — the measures that most matter to patients.
Tang still sees patients as a pe but he’s also chief health
transformation officer for IBM’s Watson Health. That’s the business group
developing health-care applications for Watson, the machine-learning system that
IBM is essentially betting its future on. It could tell a doctor, for instance, how
long it took for patients similar to Tang’s wife to be walking without pain, or
climbing stairs. It could even help analyze images and tissue samples and
determine the best treatments for any given patient.
A major cancer center and IBM partnered in 2012. The goal was for Watson
to read data about any patient’s symptoms, gene sequence, and pathology
reports, combine it with physicians’ notes on the patient and relevant journal
articles, and then help doctors | come up with diagnoses and treatments. But
they overinflated expectations to the technology. IBM claimed in 2033 that “a
new era of computing has emerged” and gave Forbes magazine the impression
that Watson would be in use with patients in just a matter of months. In 2015,
however, Watson was still busy establishing a “collective intelligence model
between machine and man.”
To understand what’s slowing the progress, you have to understand how
* machinelearning systems like Watson are trained. Watson “learns” by
continually *rejiggering its internal processing routines in order to produce the
ee on M3 (078-81)
（続きは問題PDFで確認できます）
外 。 国 詩
ne
平成30年1月22 日 13 時10 分一14 時10分
注 意 事項
a
a 1 試験開始の合図があるまで, この問題氏子を開かないこと。
2 この問題國子は表紙・白紙を除き 13 ページである。落丁, AT, 印刷不鮮明の
箇所等があった場合は申し出ること。
3 解答には必ず黒鉛筆(またはシャープペンシル) を使用すること。
4 解答は, 各設問どとに一つだけ選び, 解答用紙の所定の解答欄の該当する記号を
座りつぶすこと。
5 MATES SBA, 消しゴムできれいに消すこと。
6 忠督員の指示に従って, 問題田子の表紙の指定欄に受験番号を記入し, 解答用紙
の指定欄に受験番号. 受験番号のマーク, 氏名を記入すること。
7 この問題骨子の余折は, 章季用に使用してよい。 ただし, 切り離してはならない。
8 解答用紙およびこの問題違子は, 持ち帰ってはならない。
el
上の柏内に受験番号を記入しなさい。
やMI3(078一50)
] . 次の英文を続み, 1~ 8 の問いに答えなさい。
Paul Tang was with his wife in the hospital just after her knee replacement
surgery, a procedure performed on about 700, 000 people in the U.S. every year.
The surgeon came by, and Tang, who is himself a primary-care physician, asked
when he expected her to be back at her normal routines, (give) his experience
with patients like her. The surgeon kept giving vague ams “Finally it
hit me,” says Tang, “He didn’t know.” Tang would soon learn that most.
physicians don’t know how their patients do in the ordinary measures of life back
at home and at work — the measures that most matter to patients.
Tang still sees patients as a pe but he’s also chief health
transformation officer for IBM’s Watson Health. That’s the business group
developing health-care applications for Watson, the machine-learning system that
IBM is essentially betting its future on. It could tell a doctor, for instance, how
long it took for patients similar to Tang’s wife to be walking without pain, or
climbing stairs. It could even help analyze images and tissue samples and
determine the best treatments for any given patient.
A major cancer center and IBM partnered in 2012. The goal was for Watson
to read data about any patient’s symptoms, gene sequence, and pathology
reports, combine it with physicians’ notes on the patient and relevant journal
articles, and then help doctors | come up with diagnoses and treatments. But
they overinflated expectations to the technology. IBM claimed in 2033 that “a
new era of computing has emerged” and gave Forbes magazine the impression
that Watson would be in use with patients in just a matter of months. In 2015,
however, Watson was still busy establishing a “collective intelligence model
between machine and man.”
To understand what’s slowing the progress, you have to understand how
* machinelearning systems like Watson are trained. Watson “learns” by
continually *rejiggering its internal processing routines in order to produce the
ee on M3 (078-81)
（続きは問題PDFで確認できます）