Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks

Neural networks (NNs) have become the state of the art in many machine learning applications, especially in image and sound processing [1]. The same, although to a lesser extent [2,3], could be said in natural language processing (NLP) tasks, such as named entity recognition. However, the success of NNs remains dependent on the availability of large labelled datasets, which is a significant hurdle in many important applications. One such case are electronic health records (EHRs), which are arguably the largest source of medical data, most of which lies hidden in natural text [4,5]. Data access is difficult due to data privacy concerns, and therefore annotated datasets are scarce. With scarce data, NNs will likely not be able to extract this hidden information with practical accuracy. In our study, we develop an approach that solves these problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009 Medical Extraction Challenge [6], 4.3 above the architecture that won the competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on extracting relationships between medical terms. To reach this state-of-the-art accuracy, our approach applies transfer learning to leverage on datasets annotated for other I2B2 tasks, and designs and trains embeddings that specially benefit from such transfer.Comment: 11 pages, 4 figures, 8 table

Development of an Automated Physician Review Classification System: A hybrid Machine Learning Approach

Patients are increasingly turning to physician rating websites to help them make important healthcare decisions, such as selecting primary care doctors, specialists, and supplementary medical care providers. Previous research has identified a variety of topics and themes that emerge on these review platforms. However, there is little or no work that has been done to create an automated classifier that automatically categorizes these reviews into distinct topics after they have been explored in this context. Building such an automated classifier could assist IS developers and other stakeholders in automatically classifying patient reviews and understanding patient needs. Furthermore, using design science research we strategize how such machine learning systems can be built using design guidelines in turn having the potential to be generalized to other specific contextual problem spaces. Our work focuses on laying the foundation to design guidelines that need to be followed while building automated systems in specific contexts

Facial Paralysis Grading Based on Dynamic and Static Features

Peripheral facial nerve palsy, also known as facial paralysis (FP), is a common clinical disease, which requires subjective judgment and scoring based on the FP scale. There exists some automatic facial paralysis grading methods, but the current methods mostly only consider either static or dynamic features, resulting in a low accuracy rate of FP grading. This thesis proposes an automatic facial paralysis assessment method including both static and dynamic characteristics. The first step of the method performs preprocessing on the collected facial expression videos of the subjects, including rough video interception, video stabilization, keyframe extraction, image geometric normalization and gray-scale normalization. Next, the method selects as keyframes no facial expression state and maximum facial expression state in the image data to build the the research data set. Data preprocessing reduces errors, noise, redundancy and even errors in the original data. The basis for extracting static and dynamic features of an image is to use Ensemble of Regression Trees algorithm to determine 68 facial landmarks. Based on landmark points, image regions of image are formed. According to the Horn-Schunck optical flow method, the optical flow information of parts of the face are extracted, and the dynamic characteristics of the optical flow difference between the left and right parts are calculated. Finally, the results of dynamic and static feature classification are weighted and analyzed to obtain FP ratings of subjects. A 32-dimensional static feature is fed into the support vector machine for classification. A 60-dimensional feature vector of dynamical aspects is fed into a long and short-term memory network for classification. Videos of 30 subjects are used to extract 1419 keyframes to test the algorithm. The accuracy, precision, recall and f1 of the best classifier reach 93.33%, 94.29%, 91.33% and 91.87%, respectively.Perifeerinen kasvojen hermohalvaus, joka tunnetaan myös nimellä kasvojen halvaus (FP), on yleinen kliininen sairaus, joka vaatii subjektiivista arviointia ja FP -asteikon pisteytystä. Joitakin automaattisia kasvohalvauksen luokittelumenetelmiä on olemassa, mutta yleensä ottaen ne punnitsevat vain joko staattisia tai dynaamisia piirteitä. Tässä tutkielmassa ehdotetaan automaattista kasvojen halvaantumisen arviointimenetelmää, joka kattaa sekä staattiset että dynaamiset ominaisuudet. Menetelmän ensimmäinen vaihe suorittaa ensin esikäsittelyn kohteiden kerätyille kasvojen ilmevideoille, mukaan lukien karkea videon sieppaus, videon vakautus, avainruudun poiminta, kuvan geometrinen normalisointi ja harmaasävyjen normalisointi. Seuraavaksi menetelmä valitsee avainruuduiksi ilmeettömän tilan ja kasvojen ilmeiden maksimitilan kuvadatasta kerryttäen tutkimuksen data-aineiston. Tietojen esikäsittely vähentää virheitä, kohinaa, redundanssia ja jopa virheitä alkuperäisestä datasta. Kuvan staattisten ja dynaamisten piirteiden poimimisen perusta on käyttää Ensemble of Regression Trees -algoritmia 68 kasvojen merkkipisteiden määrittämiseen. Merkkipisteiden perusteella määritellään kuvan kiinnostavat alueet. Horn-Schunckin optisen virtausmenetelmän mukaisesti poimitaan optisen virtauksen tiedot joistakin kasvojen osista, ja dynaaminen luonnehdinta lasketaan vasempien ja oikeiden osien välille. Lopuksi dynaamisen ja staattisen piirteiden luokittelun tulokset painotetaan ja analysoidaan kattavasti koehenkilöiden FP-luokitusten saamiseksi. 32- ulotteinen staattisten piirteiden vektori syötetään tukivektorikoneeseen luokittelua varten. 60-ulotteinen dynaamisten piirteiden ominaisuusvektori syötetään pitkän ja lyhyen aikavälin muistiverkkoon luokittelua varten. Parhaan luokittelijan tarkkuus, täsmällisyys, palautustaso ja f1 saavuttavat arvot 93,33%, 94,29%, 91,33% ja 91,87%