What Big Data Means. Naoki Nakashima, M.D., Ph.D.

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1 What Big Data Means Naoki Nakashima, M.D., Ph.D. Medical Information Center, Kyushu University Hospital Huge amounts of digital data have been accumulated in the medical field by the recent acceleration of medical informatization. Several nationwide projects with medical big data are already running in Japan, and are expected to improve social issues. Medical big data are often complicated and diversified as there are so many sources of individual data items which can be connected to privacy data. Thus, it is necessary to introduce mining technology such as machine learning and also to develop innovative methods of analysis by collaboration between information science and medical professionals. After the My Number law will be enforced in 2016, with the adoption of a unique medical ID in Japan, and privacy laws will be revised in the medical field, the era of medical big data will truly begin in Japan. Received March 9, 2015 accepted April 8, 2015 Key words big data, machine learning, data mining, data base Jpn J Neurosurg Tokyo , Big Data Research and Development Initiative Address reprint requests to Naoki Nakashima, M.D., Ph.D., Medical Information Center, Kyushu University Hospital, Maidashi, Higashi ku, Fukuoka shi, Fukuoka , Japan

2 3 V 1 Volume 2 Velocity 3 Variety 2 2 Table 1 Fig DPC Table 1 Aspects of medical big data Big number of data categories linked with individuals deep data Accumulated for a long time by time series Intricately change of data velocity and granularity e.g. by hospital admission Variety of data categories with structured and unstructured Structured data Diagnosis, reimbursement claim, health check up, DPC, NCD, etc Unstructured data Free text medical record, summary, report, etc, medical image, sound voice, sensor data Sensitive privacy information, difficulty to access data General Big Data n Medical Big Data Explanatory variables number, diversity, etc Fig. 1 Difference between general and medical big data Medical big data has many explanatory variables, and accumulation continues for a long period. Jpn J Neurosurg VOL. 24 NO

3 DPC 1,800 1,000 7 DPC ResDac ,400 4 NDB MID NET 2 NDB NDB 5 MID NET 10 MID NET NCD , NCD A B C D E F G IT

4 3 Table 2 1 DPC 100 Fig. 1 CI 100 CI Table 2 New medical progress can cause an information explosion Newly developed medicine, generic medicine Clinical practice guideline for each disease Medical information sharing by electronic medical record and medical network system High performance medical image e.g. CT images with a 320 row Vital sensor devices at home Tailor made medicine by genomic information ADL CI DPC NCD 1, ADL RF Area Under Curve AUC AUC 0.90 Fig PCI Jpn J Neurosurg VOL. 24 NO

True positive rate or sensitivity 0.0 0.2 0.4 0.6 0.8 1.

5 True positive rate or sensitivity A AUC TPR FPR False positive rate or 1 Specificity JCS 4 th day JCS 3 rd day JCS 2 nd day Age JCS 1 st day D-Dimer 1 st day Wash Room self Paralysis of upper left 2 nd day NIHSS 48H A/G ratio 1 st day Paralysis of lower left 3 rd day Body temperature 3 rd day A/G ratio 3 rd day NIHSS 24H Paralysis of lower left 4 th day Alb 3 rd day Paralysis of lower left 2 nd day Urination management mrs before Stroke Paralysis of upper left 4 th day Paralysis of upper left 3 rd day Paralysis of lower right 3 rd day Alb 1 st day NIHSS at admission Myoglobin 1st day JCS at admission RBC 3 rd day TG 3 rd day ADL cosmetic at admission BUN 3dr day B Small Importance Big Fig. 2 A Predictor of ADL at discharge by random forest method. B Top items which contribute to ADL at discharge extracted from 1,835 explanatory variables in stroke patients. Max ST level at 2 nd day Anthropometric measurements at 2 nd day Absence of arrhythmia alert at 3 rd day Average of ST level at 3 rd day Bed transfer at 2 nd day Wound care at 2 nd day A Importance SD of ST level at 2 nd day Number of tachycardia alert at 3 rd day Number of heart rate alert at 3 rd day Median of ST level at 3 rd day B Importance Fig. 3 A Top items which contribute to elongation of admission from vital data 40 thousand hours. B Top items which contribute elongation of admission from sensed nurse actions, in patients with ischemic heart disease. PCI CI Fig CI CI PDCA DPC NCD

6 PC PC 4 Skype 2 16, RF 15, AUC Fig. 4 1 AUC Fig Balanced Error Rate ,344 15,705 1, True positive rate or sensitivity AUC TPR FPR False positive rate or 1 Specificity Fig. 4 Predictor of blood sugar level by random forest method in Bangladesh CD Jpn J Neurosurg VOL. 24 NO

7 8 20 Twitter ID IT ID COI PACS Beyer M Gartner Says Solving Big Data Challenge Involves More Than Just Managing Volumes of Data. http: it page.jsp?id accessed September 2, IT https: kaken.nii.ac.jp pdf 2011 chukan_jigo_hyoka jigo_hyoka.pdf accessed September 2, http: bunya shakaihosho iryouseido01 info03_h24.html accessed September 2, NDB http: file 05 Shingikai Hokenkyoku Soumuka pdf accessed September 2, Kurita N, Miyata H, Gotoh M, Shimada M, Imura S, Kimura W, Tomita N, Baba H, Kitagawa Y, Sugihara K, Mori M Risk model for distal gastrectomy when treating gastric cancer on the basis of data from 33,917 Japanese patients collected using a nationwide web based data entry system. Ann Surg , http: jp singi it2 senm on_bunka number dai8 siryou2.pdf accessed September http: jp tpp pdf _5.pdf accessed September 2, Nakashima N, Kobayashi K, Inoguchi T, Nishida D, Tanaka N, Nakazono H, Hoshino A, Soejima H, Takayanagi R, Nawata H A Japanese model of disease management. Stud Health Technol Inform 129 Pt , IHE http: PDI pdi2.pdf accessed September 2, Nohara Y, Kai E, Ghosh P, Islam R, Ahmed A, Kuroda M, Inoue S, Hiramatsu T, Kimura M, Shimizu S, Kobayashi K, Baba Y, Kashima H, Tsuda K, Sugiyama M, Blondel M, Ueda N, Kitsuregawa M, Nakashima N A health checkup and tele medical intervention program for preventive medicine in developing countries a verification study. J Med Internet Res 17 e2, Office of Science and Technology Policy Executive Office of the President Obama Administration Unveils Big Data Initiative Announces $ 200 Million in New R & D Investments. http: sites default files microsites ostp big_data_press_release_final_2.pdf accessed September 2, Snijders C, Matzat U, Reips U D Big Data Big gaps of knowledge in the field of Internet Science. International Journal of Internet Science 7 1 5, Wakata Y, Nakashima N, Taketomi A, Shirabe K, Maehara Y, Hagihara A Factors associated with the postoperative status of donor patients for living donor liver transplantation. Liver Transpl , DPC Diagnosis Procedure Combination J UOEH ,

8 2016 ID Jpn J Neurosurg VOL. 24 NO

Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records

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