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未來醫(yī)生:超級計算機

未來醫(yī)生:超級計算機

Michal Lev-Ram 2012-12-07
科技領域的兩大熱門趨勢,也就是數據分析和移動設備正在走進醫(yī)院。未來,大型計算機系統(tǒng)可以處理包括醫(yī)學文獻到病人體征在內的數據,據此判別癥狀。然后,醫(yī)生就可以借助平板電腦或者智能手機上的智能軟件掌握病情,更及時地提供相應的治療。

????醫(yī)學電視劇的粉絲也許知道敗血性休克有多么可怕。不過幾乎沒人知道,敗血癥這種導致身體抵抗感染時攻擊自身的疾病每年導致多達258,000位美國人死亡。盡管這種疾病易于治療,卻難以診斷。病人們起初可能沒什么癥狀,但卻會在短短幾個小時內休克。

????數據分析和移動設備這兩項高速發(fā)展的科技能夠幫助診斷包括敗血癥和癌癥在內的多種困擾人么的疾病。大型計算機系統(tǒng)正在持續(xù)不斷地處理數據——從醫(yī)學文獻到病人的體征一應俱全,并以此判別癥狀。因此,通過平板電腦或者智能手機上的智能軟件,醫(yī)生就能對即將發(fā)生的問題有所警覺。這樣,救人于旦夕之間的治療將會更加及時。

????荷蘭信息服務公司威科集團(Wolters Kluwer)下屬的醫(yī)療公司正在測驗能夠確診和治療敗血癥的技術。公司最近開始尋找試點醫(yī)院來參與其“敗血癥死亡率降低計劃”。哥倫布市河畔衛(wèi)理公會醫(yī)院(Riverside Methodist Hospital)負責治療質量和病人安全的醫(yī)療總監(jiān)詹姆斯·奧布萊恩說:“如果這個計劃取得成功,敗血癥的死亡率將降低一半?!?/p>

????另一個例子發(fā)生在去年,IBM公司的超級計算機Watson在娛樂節(jié)目《危險邊緣》(Jeopardy!)的智力競賽中擊敗人類,登上了報紙頭條。這臺電冰箱大小的機器正在為其第二職業(yè)進行相關準備,這份職業(yè)也是紐約紀念斯隆-凱特琳癌癥中心(Memorial Sloan-Kettering Cancer Center)試驗計劃的一部分。Watson借助并行處理技術——即同時處理多項任務的技術——每秒鐘最多可以處理500GB的數據。例如,內科醫(yī)師可以輸入切片檢查的數據,而這臺計算機則會據此列出病人的病史、相關的臨床研究和醫(yī)學期刊。接下來還會給出可能的治療方法和相應的“信心水平”,即治療成功率。而最終的決定權依然在醫(yī)生手中。

????這樣的系統(tǒng)究竟有多大價值,目前還無法確定。不過根據市場研究公司Insight Research的數據,接下來六年內,美國衛(wèi)生保健行業(yè)將會在信息產業(yè)投入690億美元。英特爾(Intel)和思愛普(SAP)已經開始與加州大學伯克利分校(University of California at Berkeley)的研究人員通力協(xié)作,開發(fā)具有競爭力的醫(yī)用超級計算機。

????不過在這個項目最終證實能提高效率、減少開支之前,醫(yī)院并不會買賬。加州大學舊金山分校(University of California at San Francisco)的校長兼腫瘤醫(yī)生蘇珊·戴斯蒙德-海爾曼解釋說:“臨床醫(yī)師只相信證據?!?/p>

????還有一個問題在于:全天下的醫(yī)學數據和處理能力都無法教會計算機如何像醫(yī)生那樣對待病人。更不用說一旦用算法取代醫(yī)生,電視劇將變得多么無趣。

????譯者:嚴匡正

????Fans of television medical dramas are probably aware of the grim condition known as septic shock. But few people know that sepsis, a disease that causes the body to attack itself in an attempt to fight off infection, kills 258,000 Americans each year. Though easy to treat, sepsis is difficult to diagnose; hospital patients can go from asymptomatic to a state of shock in just a few hours.

????Two fast-growing technologies,?data analytics?and?mobile devices, could help solve vexing problems ranging from sepsis to cancer diagnosis. Large computer systems are increasingly crunching data -- everything from medical journals to patients' vitals -- to recognize patterns. That has allowed intelligent software to alert doctors of impending problems via tablet or smartphone, making lifesaving treatment more timely.

????Wolters Kluwer Health, a division of the huge Dutch information-services firm, is currently testing such technology to identify and treat sepsis. The company recently began enrolling hospitals as pilot sites in its "sepsis mortality-reduction program." James O'Brien, medical director of quality and patient safety at Riverside Methodist Hospital in Columbus, says, "If this is successful, it could cut the rate of death from sepsis in half."

????Watson, the IBM (IBM) supercomputer that made headlines by drubbing humans on?Jeopardy!last year, is another example. The refrigerator-size machine is prepping for a second career as part of a trial program at New York City's Memorial Sloan-Kettering Cancer Center. Watson relies on parallel processing -- geekspeak for running multiple tasks at once -- to sift through 500 gigabytes of data per second. A physician can enter the results of a biopsy, for example, and Watson pulls relevant bits of a patient's history as well as clinical studies and medical journals. It then lists potential diagnoses and their varying "levels of confidence," or probability. The final call is left up to the doctor.

????It isn't clear how much the market for such systems will eventually be worth. But the U.S. health care industry will spend some $69 billion on IT over the next six years, according to Insight Research Corp. Intel (INTC) and SAP (SAP) are already working with researchers at the University of California at Berkeley to develop a competing breed of medical supercomputer.

????But hospitals won't buy into the idea until it has been proven to boost efficiency and trim costs. "What clinicians respond to is evidence," explains Susan Desmond-Hellmann, an oncologist and the chancellor of the University of California at San Francisco.

????Another problem: All the medical data and processing power in the world can't teach a computer bedside manner. Not to mention that substituting algorithms for doctors would make for really dull TV.

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