Healthcare data analytics. Da-Wei Wang Institute of Information Science

Transcription

1 Healthcare data analytics Da-Wei Wang Institute of Information Science

2 Outline Data Science Enabling technologies Grand goals Issues Google flu trend Privacy Conclusion

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5 Analytics Statistics Machine learning decision tree, artificial neural network, support vector machine, Bayesian network Deep learning Graph analytics Natural language processing

6 Map-Reduce Programming model for large-scale computing problems Parallel and distributed computing

7 1. Distribute data to machine (mapper) 2. Map: computing something you want from each data item (key, value) pair 3. Shuffle and Sort (according to key) 4. Reduce: aggregate, summarize, filter, or transform (reducer) 5. Output

8 Compute word frequency 1. Distribute web pages to machines (mapper) 2. Map: for each word, w, create (w, c) pair where c is the number of occurrence of w in the document 3. Shuffle and Sort (according to key) 4. Reduce: add all c_i in pair (w, c_i) 5. Output

9 Visualization main goal of data visualization is to communicate information clearly and effectively through graphical means To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way Example: Hans Rosling, gapminder

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11 Heterogeneity in Healthcare Multiple forms insurance claims, physician notes, images conversations about health in social media data from wearables and other monitoring devices. Multiple agencies: Providers, payers, employers, personalizedgenetic-testing companies (23andme), social media, and patients

12 The Learning Healthcare System Series

13 The goal of a learning healthcare system is to deliver the best care every time, and to learn and improve with each care experience Each care experience counts implies massive data Need analytics

14 Precision Medicine Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. Electronic health records have been widely adopted, genomic analysis costs have dropped significantly, data science has become increasingly sophisticated

15 Precision medicine initiative Mr. Obama called for $215 million in fiscal year 2016 to support the Initiative(2015/1) $130 million was allocated to NIH to build a national, large-scale research participant group, called a cohort $70 million was allocated to the National Cancer Institute to lead efforts in cancer genomics

16 Not only for profit

17 Issues with big data analytics Over fitting Model complexity Association (correlation) v.s. causality Understanding, explanation v.s. predicting Parametric to non-parametric Equational model to algorithmic model Wolfgang Pietsch Big Data The New Science of Complexity

18 Cautious notes Google flu trend Detecting influenza epidemics using search engine query data Nature 2009 (letters) When google got flu wrong Nature 2013 (news) The parable of google flu: traps in big data analysis Science 2014 (policy forum)

19 Google Flu Trend Early detection -> rapid response -> reduced impact Monitor health-seeking behavior in the form of online web search queries Relative frequency of certain queries is highly correlated with the percentage of physician visits Estimate current level of weekly influenza activity

20 Data: hundreds of billions of individual searches logs (03-08), time series of weekly counts for 50 million most common search queries normalized by dividing total number of queries Percentage of ILI-related physician visit data from CDC Goal: to estimate the percentage of influenza like illness (ILI)

21 Estimate the probability, P, that a random physician visit is influenza-like illness related Key insight: the probability, Q, that a random search query is ILI-related can approximate P Next steps: Pick a model to relate P with Q Determine ILI-related query

22 Logit(P)= a + b*logit(q)+e, Logit(x)= ln(x/1-x) P, Q? Training step: select the set of ILI-related queries (Q) that fits the model best

23 Single query as Q, try 50 millions one by one. Favor those performed well for all 9 regions. (9 regions) Produce a sorted list of highest scoring queries. Decide how many queries to be included in Q. N=45

24 results Training Meaning correlation 0.9 (min=0.8, max=0.96, 9 regions) Validating: 42 points for each region ( ) 0.97 (min=0.92, max=0.99)

25 When Google got flu wrong Not doing well for 2012 season 2009 flu trend badly underestimated ILI in the US at the start of the H1N1 pandemic Attributed to changes in people s search behaviors as a result of the exceptional nature of the pandemic

26 The most big data that have received popular attention are not the output of instruments designed to produce valid and reliable data amenable for scientific analysis 50 million search terms to fit 1152 data points Remedy: combining multiple sources and dynamically recalibrating GFT

27 Algorithms dynamics All empirical research stands on a foundation of measurement. Is the instrumentation actually capturing the theoretical construct of interest? In the measurement stable and comparable across cases and over time? Are measurement errors systematic? GFT was an unstable reflection of the prevalence of the flu because of algorithm dynamics affecting google s search algorithm

28 Algorithm dynamics Algorithm dynamics are the changes made by engineers to improve the commercial service and by consumers in using that service The google search algorithm is not a static entity Providing suggested additional search terms (2011) Returning potential diagnoses for searches including physical symptoms (2012)

29 GFT assumes that relative search volume for certain terms is statically related to external events, but search behavior changes dynamically Research subjects attempt to manipulate the data generating process to meet their own goals. (google bomb) Ironically, the more successful we become at monitoring the behavior of people using these open sources of information, the more tempting it will be to manipulate those signals.

30 lessons Transparency and replicability Use big data to understand the unknown GFT for finer granularity Study the algorithms Robust patterns? Replicate across time, with other data source Study evolution of social-technical system embedded in our society. It s not just about size of the data all data

31 健 康 存 摺 與 電 子 病 歷 交 換 中 心 已 經 站 上 了 learning healthcare system 的 起 跑 點

32 防 疫 雲 開 始 嘗 試 machine to machine 自 動 資 料 交 換 使 傳 染 病 監 控 更 即 時 更 經 濟

33 健 康 雲 跨 領 域 研 究 法 律 經 濟 生 醫 公 衛 統 計 資 訊 希 望 創 造 更 尊 重 個 人 且 有 善 的 研 究 環 境

34 Privacy Dispute about National Health Insurance data:not only personal privacy, also autonomy. The right to opt-out? Data de-identified, opt-out reduces the quality of data, it s for public good, administration cost too high It s my decision! IT brings administration cost down What if 30% opt-out, data quality down. But

35 Releasing data Data -> User De-identification (cellsecu system) Data enclave( 資 料 中 心 ) User -> Data Link unlinkable data sets Secure multiparty computation

36 Dataset Linkage problem Linking several dataset can be very useful Linkage is prohibited by law in many places due to privacy concerns Secure multiparty computation (SMC) protocols might remedy the situation We built a prototype system

37 Conclusions Data science has tremendous potential Healthcare analytics can have profound impact on healthcare systems Autonomy and privacy issues have to be addressed 主 動 參 與 是 可 能 的 選 項