Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.Siirretty Doriast

Passiivitalovaatimusten saavuttaminen pientalolle : Case Käpy

Rakentamisen energiamääräysten tiukentuminen tulevaisuudessa on luonut tarpeen passiivi- ja matalaenergiatalojen kehittämiseen. Myös kuluttajat ovat entistä kiinnostuneempia energiatehokkaasta asumisesta. Teijo-Talot Oy on työn tilaajana kiinnostunut omien talomalliensa kehittämisestä passiivitasoisiksi. Opinnäytetyössä keskityttiin löytämään Käpy-talomallille rakenne- ja talotekniikkaratkaisut, joilla saavutetaan passiivitaloille määritellyt tasot, ja käsiteltiin yleisesti Suomessa voimassa olevia energiamääräyksiä. Työssä tutkitun rakennuksen ulkomittoja ei voitu kasvattaa, jotta rakennus olisi vielä kuljetettavissa. Koska passiivirakenteet usein ovat paksumpia kuin normienmukaiset rakenteet, rakennuksen kerrosala pienenee. Käpy-talomalli on kompakti 2-3 makuuhuoneen asuinrakennus eikä siinä ole erillistä teknistä tilaa, joten lämmitys- ja ilmanvaihtokoneet eivät saa olla tilaa vieviä. Rakennus mallinnettiin ArchiCAD-ohjelmalla, jolloin rakennuksen laajuustiedot olivat helposti saatavilla. Rakennuksen energiankulutuksen arviointiin hyödynnettiin Archi-CAD Eco Designer-lisäosaa ja Laskentapalvelut.fi:n E-lukulaskuria. Alapohjarakenne pidettiin Teijo-Talojen EPS-eristeellä kevennettynä teräsbetonipalkistona. Yläpohja ja ulkoseinärakenteet valittiin Isoverin rakennekirjastosta, koska talotehtailla on käytetty Isoverin tuotteita aiemminkin. Passiivitaloissa on huomioitava erityisesti rakenteiden ja liitosten tiiveys, jotta saavutetaan asetettu tiiviys 0,6 1/h. Valituilla toimenpiteillä ja rakennevaihtoehdoilla saavutettiin matalaenergiataso, jolla lämmitysenergiantarve bruttoalaa kohden on noin 30 kWh/m2a. Jotta lämmitysenergiantarve olisi <20 kWh/m2a, rakennuksen vaipparakenteiden U-arvoja parannettiin ja tiivistettiin edelleen. Työssä on esitetty laskelmat kaukolämmöllä, maalämpöpumpulla ja sähkölämmityksellä lämmitettävistä rakennuksista. Passiivivaatimukset saatiin täytettyä kaikille kolmelle tutkitulle lämmitysmuodolle. Alapohjarakenteen U-arvoa ei voida tuotannollisista ja kuljetusteknisistä syistä parantaa nykytasosta juurikaan, ja sen osuus rakennuksen lämpöhäviöistä on ikkunoiden jälkeen suurin. Koska alapohjan lämpöhäviöitä joudutaan muilla tavoin kompensoimaan, johtaa se tiukkoihin vaatimuksiin muiden rakenteiden, rakennusosien ja laitteiden osalta.The purpose of this thesis was to collect information on prefabricated passive and lowenergy houses. The energy regulations are tightening and it creates a need to develop passive and low-energy houses. Teijo-Talot Oy is interested in developing their prefabricated houses towards passive house standards. The theoretical section explores the Finnish energy regulations. The subject of this thesis was one of the models in range of Teijo-Talot, called Käpy. Total energy consumption was evaluated by modeling the house with ArchiCAD and then using Eco Designer addition and E-number calculator of Laskentapalvelut.fi. The results suggest that it is possible to achieve passive house standards on Käpy. Achieving standards demands very efficient heat recovery unit and low thermal conductivity from structures. The house must be also air tight so the warm air does not leak out and the structures work physically as designed. Benefits of building a passive house should be evaluated for whole life span of the house so upkeep costs can be included in calculations with building costs

A systematic review of speech recognition technology in health care

BACKGROUND To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. METHODS A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. RESULTS The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. CONCLUSIONS SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.Funding for this study was provided by the University of Western Sydney. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. NICTA is also funded and supported by the Australian Capital Territory, the New South Wales, Queensland and Victorian Governments, the Australian National University, the University of New South Wales, the University of Melbourne, the University of Queensland, the University of Sydney, Griffith University, Queensland University of Technology, Monash University and other university partners

Does the systematic use of stimulus reduction shorten hospitalization in acute mania? A pilot study

Peer reviewe

A Machine Learning Analysis of the Non- academic Employment Opportunities for Ph.D. Graduates in Australia

Can Australia's PhD graduates be better utilised in the non-academic workforce? There has been a historic structural decline in the ability of PhD graduates to find work within academia for the last couple of decades (Forsyth 2014). Around 60% of PhD graduates in Australia now find jobs outside the academy, and the number is growing year on year (McGagh et al. 2016). The PhD is a degree designed in the early 20th century to credential the academic workforce. How to make it fit contemporary needs, when many if not most graduates do not work in academia, is a question that must be addressed by higher education managers and policymakers. Progress has been slow, partly because of the lack of reliable data-driven evidence to inform this work. This paper puts forward a novel hybrid quantitative/qualitative approach to the problem of analysing PhD employability. We report on a project using machine learning (ML) and natural language processing to perform a 'big data' analysis on the text content of non-academic job advertisements. This paper discusses the use of ML in this context and its future utility for researchers. Using these methods, we performed an analysis of the extent of demand for PhD student skills and capabilities in the Australian employment market. We show how these new methods allow us to handle large, complex datasets, which are often left unexplored because of human labour costs. This analysis could be reproduced outside of the Australian context, given an equivalent dataset. We give an outline of our approach and discuss some of the advantages and limitations. This paper will be of interest for those involved in re-shaping PhD programs and anyone interested in exploring new machine learning methods to inform education policy work.The project team would like to thank the Australian Department of Industry and Seek.com.au for their generous support of this project

Transfer Learning for Hate Speech Detection in Social Media

In today's society more and more people are connected to the Internet, and its information and communication technologies have become an essential part of our everyday life. Unfortunately, the flip side of this increased connectivity to social media and other online contents is cyber-bullying and -hatred, among other harmful and anti-social behaviors. Models based on machine learning and natural language processing provide a way to detect this hate speech in web text in order to make discussion forums and other media and platforms safer. The main difficulty, however, is annotating a sufficiently large number of examples to train these models. In this paper, we report on developing automated text analytics methods, capable of jointly learning a single representation of hate from several smaller, unrelated data sets. We train and test our methods on the total of $37,520$ English tweets that have been annotated for differentiating harmless messages from racist or sexists contexts in the first detection task, and hateful or offensive contents in the second detection task. Our most sophisticated method combines a deep neural network architecture with transfer learning. It is capable of creating word and sentence embeddings that are specific to these tasks while also embedding the meaning of generic hate speech. Its prediction correctness is the macro-averaged F1 of $78\%$ and $72\%$ in the first and second task, respectively. This method enables generating an interpretable two-dimensional text visualization --- called the Map of Hate --- that is capable of separating different types of hate speech and explaining what makes text harmful. These methods and insights hold a potential for not only safer social media, but also reduced need to expose human moderators and annotators to distressing online~messaging

Behavior of C-reactive protein in association with surgery of facial fracture and the influence of dexamethasone

To clarify pre- and postoperative C-reactive protein (CRP) levels in patients with facial fractures and to investigate the influence of perioperatively administered dexamethasone on postoperative CRP levels. Facial fracture patients were randomized to receive perioperatively a total dose of 30 mg of dexamethasone (OradexonA (R)), whereas patients in the control group received no glucocorticoid. The analysis included patients who had CRP measured pre- and postoperatively. A total of 73 adult patients with facial fractures were included in the final analysis. Mean CRP level was elevated preoperatively and the level increased further after surgery. However, postoperative CRP rise was significantly impeded by dexamethasone (p <0.001), regardless of gender, age, treatment delay, site of fracture, surgical approach, and duration of surgery. CRP rise halved on the 1st postoperative day when dexamethasone was used. In addition, dexamethasone resulted in a CRP decrease on the 2nd postoperative day, whereas the CRP rise continued in the control group. CRP rise is a normal body response after facial fracture and surgery that can be markedly reduced with dexamethasone. CRP changes should be considered with caution if perioperative dexamethasone is used.Peer reviewe