No abstract available.

No abstract available.

Recently, many hospitals have been adopting clinical data warehouses (CDW) as well as electronic medical records. These new hospital information systems are inevitably introducing very large amounts of clinical data that might be useful for further analysis. However, the electronic clinical data in the CDW are usually byproducts of clinical practice rather than the product of research. Therefore, they include inconsistent and sometimes erroneous information that might not have the specific context of the clinical situations. Data miners usually have various academic backgrounds such as electronics, informatics, statistics, biomedicine, and public health. If the complex situations surrounding the clinical data are not well understood, investigators performing data mining in clinical fields may have problems assessing the information they are confronted with. Here, we would like to introduce some basic concepts on the principles of data mining in clinical fields including legal and ethical considerations as well as technical concerns.
Machine Learning ; Data Mining ; Electronic Health Records ; Electronics ; Electrons ; Hospital Information Systems ; Humans ; Informatics ; Public Health ; Research Personnel

Adoption of electronic health records (EHRs) is increasing worldwide. The worldwide EHR adoption rate is estimated to be around 9% to 12%. Thus, the accumulation of medical records in electronic form is also sharply increasing and is expected to be a precious asset for clinical research. Longitudinal observational studies based on EHRs are also increasing. Observational studies covering more than a million people are not rare at present. However, much of the current EHR data are equivalent in form to those of paper records, but are just stored in electronic stor-age devices, rather than as electronic data that can be transferred and shared without loss of clinical semantics. Current EHR systems must be improved in many ways to be used for anal-yses to yield important clinical knowledge. These improvements, which are addressed in this review, include the adoption of clinical data warehouses, use of controlled vocabulary, avoidance of personal/departmental research databases, a standardized interface of many diagnostic devices with the EHR system, control of time-stamp granularity, preparedness for whole-genome sequencing of every patient, confederation or consolidation of multi-institutional EHR data, protection of privacy and confidentiality, and an education system for clinical informaticians.
Adoption ; Confidentiality ; Electronic Health Records ; Electronics ; Electrons ; Humans ; Medical Informatics ; Medical Records ; Privacy ; Semantics ; Vocabulary, Controlled

Varieties of telepathology system had been developed and in use, but their functional capability and diagnostic accuracy are considered to be inferior to those of conventional optical microscope. This study is intended to find out: 1) the diagnostic accuracy and reproducibility rate according to the input devices and the video signals; 2) any potential technical problems of the telepathology system; 3) any possible physical and psychological impacts. We devised a virtual telepathology system using our existing microscope equipped with CCD camera unit that has no restriction of network speed. Total fifty-five surgical pathology cases from 11 different organs were selected. Three pathologists were involved in making diagnoses. The resulting diagnostic accuracies were: 1 CCD camera with composite video signal was 86.2%; 3 CCD camera with composite video signal was 93.1%; 3 CCD camera with component video signal was 95.0%. The 3 CCD camera with component video signal resulted in 95.0% diagnostic accuracy and was superior to 1 CCD camera with composite video signal. Some technical problems noted during this study were: the visual field of the virtual telepathology system was smaller by 43% than that of microscope; the difference of cell sizes between microscope and monitor; low resolution of image. Some physical and psychological symptoms were noted.
Cell Size ; Diagnosis ; Pathology, Surgical ; Telepathology* ; Visual Fields

No abstract available.
Confidentiality* ; Informatics*

No abstract available.
Genitalia* ; Humans ; Male*

No abstract available.
Informatics

Electronic Health Records (EHRs)-based surveillance systems are being actively developed for detecting adverse drug reactions (ADRs), but this is being hindered by the difficulty of extracting data from unstructured records. This study performed the analysis of ADRs from nursing notes for drug safety surveillance using the temporal difference method in reinforcement learning (TD learning).