開源日報 每天推薦一個 GitHub 優質開源項目和一篇精選英文科技或編程文章原文,堅持閱讀《開源日報》,保持每日學習的好習慣。
今日推薦開源項目:《中文OCR:chineseocr_lite》
今日推薦英文原文:《Meet Your Match: AI Finds the Right Clinical Trial for Cancer Patients》
開源日報第713期:《中文OCR:chineseocr_lite》
今日推薦開源項目:《中文OCR:chineseocr_lite》傳送門:GitHub鏈接
推薦理由:超輕量級中文ocr,支持豎排文字識別, 支持ncnn推理 , psenet(8.5M) + crnn(6.3M) + anglenet(1.5M) 總模型僅17M. 輕量,有效解決痛點.
今日推薦英文原文:《Meet Your Match: AI Finds the Right Clinical Trial for Cancer Patients》作者:ISHA SALIAN
原文鏈接:https://blogs.nvidia.com/blog/2020/03/08/intrepida-ai-cancer-clinical-trials/
推薦理由: 這篇文章為我們簡單的介紹了人工智慧為癌症患者找到正確的臨床試驗的過程,可以讓我們更好的了解這些前沿技術的具體應用.

Meet Your Match: AI Finds the Right Clinical Trial for Cancer Patients

Clinical trials need a matchmaker.

Healthcare researchers and pharmaceutical companies rely on trials to validate new, potentially life-saving therapies for cancer and other serious conditions. But fewer than 10 percent of cancer patients participate in clinical trials, and four out of five studies are delayed due to the challenges involved in recruiting participants.

For patients interested in participating in trials, there』s no easy way to determine which they』re eligible for. AI tool Ancora aims to improve the matchmaking process, using natural language processing models to pair patients with potential studies.

「This all started because my friend』s parent was diagnosed with stage 3 cancer,」 said Danielle Ralic, founder and CEO of Intrepida, the Switzerland-based startup behind Ancora. 「I knew there were trials out there, but when I tried to help them find options, it was so hard.」

The U.S. National Institutes of Health maintains a database of hundreds of thousands of clinical trials. Each study lists a detailed series of text-based requirements, known as inclusion and exclusion criteria, for trial participants.

While users can sort by condition and basic demographics, there may still be hundreds of studies to manually sort through — a time-consuming process of weeding through complex medical terminology.

Intrepida』s customized natural language processing models do the painstaking work of interpreting these text-heavy criteria for patients and physicians, processing new studies on NVIDIA GPUs. The studies listed in the Ancora tool are updated weekly, and users can fill out a simple, targeted questionnaire to shortlist suitable clinical trials, and receive alerts for new potential studies.

「We assessed what 20 questions we could ask that can most effectively knock a patient』s list down from, for example, 250 possible trials to 10,」 Ralic said. The platform also shows patients useful information to help decide on a trial, such as how the treatment will be administered, and if it』s been approved in the past to treat other conditions.

Intrepida』s tool is currently available for breast and lung cancer patients. A physician version will soon be available to help doctors find trials for their patients. The company is a member of the NVIDIA Inception virtual accelerator program, which provides go-to-market support for AI startups — including NVIDIA Deep Learning Institute credits, marketing support and preferred pricing on hardware.

Finding the Perfect Match

Intrepida founder Danielle Ralic

Though the primary way patients hear about clinical trials is from their physicians, less than a quarter of patients hear about trials as an option from their doctors, who have limited time and resources to keep track of existing trials.

Ralic recalls being surprised to meet a stage 4 cancer survivor while hiking in Patagonia, and finding out the man had participated in a clinical trial for a new breakthrough drug.

「I asked him, how did you know about the trial? And he said he found out through a relative of his wife』s friend. That』s not how this should work,」 Ralic said.

For physicians and patients, a better and more democratized way to discover clinical trials could lead to life-saving results. It could also speed up the research cycle by improving trial enrollment rates, helping pharmaceutical companies more quickly validate new drugs and bring them to market.

As members of the NVIDIA Inception program, Ralic says she and the Intrepida team were able to meet with other AI startups and with NVIDIA developers at the GPU Technology Conference held in Munich in 2018.

「We joined the program because, as a company that was working with NVIDIA GPUs already, we wanted to develop more sophisticated natural language models,」 she said. 「There』s been a lot to learn from NVIDIA team members and other Inception startups.」

Using NVIDIA GPUs has enabled Intrepida to shrink training times for one epoch from 20 minutes to just 12 seconds.

Diversifying the Data

A female startup founder in an industry that to date has been dominated by men, Ralic says more diversity is key to improving the healthcare industry as a whole — and especially clinical trials.

「Healthcare is holistic. It involves so many different types of people and knowledge,」 she said. 「Without a diversity of perspectives, we can never address the problems the healthcare industry has.」

The data backs her up. Clinical trial participants in the United States skew overwhelmingly white and male. The lack of diversity in trials can lead to critical errors in drug dosage.

For example, in 2013, the U.S. Food and Drug Administration mandated doses for several sleeping aids to be cut in half for women. Because females metabolize the drug differently, it increased their risk of getting in a car accident the morning after taking a sleeping pill.

「If we don』t have a diverse trial population, we won』t know whether a patient of a different gender or ethnicity will react differently to a new drug,」 Ralic said. 「If we did it right from the start, we could improve how we prescribe medicine to people, because we』re all different.」


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