Shirou Manabe

Hand manipulation of virtual objects in wearable augmented reality

This paper proposes an interface for wearable computers using augmented reality (AR) environment which allows the user to handle virtual objects with his/her own hands intuitively. The proposed system is constructed with a wearable computer and a head mounted display (HMD). A pair of cameras is attached to HMD positioned as a pair of eyes. Using the stereo measurement, the fingers postures are captured and displayed together with virtual objects on a video-see-though HMD. The system tracks the fingertips motion of the index finger and the thumb so as the user can pick up, move and rotate the virtual object as if treating a real object with the hands in the real world. While the stereo measurement, range information is added to the region of the hand Visual occlusion is synthesized between the users hand and the virtual objects including 2D based graphical user interfaces, 3D computer graphics models and so on.

An extraction of medical knowledge on text mining for ubiquitous medicine

Just now, ubiquitous computing that means anyone, anyplace, and anytime we are able to use computer and those technologies exist unconsciously, is beginning. The important point concerning ubiquitous computing is interface between human and information. So, we would like to focus attention on natural language that is kind of human interface in medicine because natural language is an essential tool for healthcare professionals and patients to communicate. If natural language processing is realized, it is easy that various information is recorded anytime and anywhere and anyone with mobile, in addition, we can use data of text, speech and so on, and we are able to acquire medical knowledge from it. We have tried to extract medical knowledge with methods of text mining. To put it concretely, we have developed a new tool and medical dictionary called co-occurrence relation illustration system with medical terminology including basic property to every word. This system is able to show one of the expression and meaning of medical language. Consequently, natural language data as convenience media for input information but inconvenient media for output information to computing are useful actually and we can use computer system for medicine as talking with it ubiquitously.

Screening of anticancer drugs to detect drug‐induced interstitial pneumonia using the accumulated data in the electronic medical record

Because drug‐induced interstitial pneumonia (DIP) is a serious adverse drug reaction, its quantitative risk with individual medications should be taken into due consideration when selecting a medicine. We developed an algorithm to detect DIP using medical record data accumulated in a hospital. Chest computed tomography (CT) is mainly used for the diagnosis of IP, and chest X‐ray reports, KL‐6, and SP‐D values are used to support the diagnosis. The presence of IP in the reports was assessed by a method using natural language‐processing, in which IP was estimated according to the product of the likelihood ratio of characteristic keywords in each report. The sensitivity and the specificity of the method for chest CT reports were 0.92 and 0.97, while those for chest X‐ray reports were 0.83 and 1, respectively. The occurrence of DIP was estimated by the patterns of presence of IP before, during, and after the administration of the target medicine. The occurrence rate of DIP in cases administered Gefitinib; Methotrexate (MTX); Tegafur, Gimeracil, and Oteracil potassium (TS‐1); and Tegafur and Uracil (UTF) was 6.0%, 2.3%, 1.4%, and 0.7%, respectively. The estimated DIP cases were checked by having the medical records independently reviewed by medical doctors. By chart review, the positive predictive values of DIP against Gefitinib, MTX, TS‐1, and UFT were 69.2%, 44.4%, 58.6%, and 77.8%, respectively. Although the cases extracted by this method included some that did not have DIP, this method can estimate the relative risk of DIP between medicines.

Method for Detecting Drug-Induced Interstitial Pneumonia from Accumulated Medical Record Data at a Hospital

Drug-induced interstitial pneumonia (DIP) is a serious adverse drug reaction. The occurrence rete of DIP was evaluated by clinical trial before available in the market. However, due to limited number of cases in clinical trials, it may be inapplicable to the real market. We aimed to seek a method to evaluate the occurrence rate of DIP using clinical data warehouse at a hospital. Initially we developed a method that assesses whether presence of IP was written in reports by natural language processing. Next we detected DIP by estimating IP before, during and after the drug administration. Presence of IP was determined according to the reports of CT if CT was performed, otherwise it was determined based on the changes in the results of chest X-ray, level of KL-6 or SP-D. DIP was determined according to the pattern of presence of IP in each phase. In this study we chose amiodarone as a target drug. The number of patients who suffered from IP caused by amiodarone was 16 (3.9 %), including one definitively diagnosed and 15 strong doubt cases. Most of them could be validated by medical record chart. Using this method, we were able to successfully detect occurrence of DIP from accumulated data in a hospital information system.

ICF-based community mental health care

This system is designed as a tool which solves some challenges imposed in community mental health care. Using a computer network, the electronic-bulletin-board-like system which uses ICF-based data input is applied, for the purpose of sharing community mental health care consumer information. Such system is expected to facilitate health care consumer rehabilitation and in the prevention of a recurrence of an illness. It will become a vital necessity in the future of community mental health care.