title: Experiments in Predicting Biodegradability

authors: Blockeel, Hendrik

subjects: computer science

abstract: This paper is concerned with the use of AI techniques in ecology. More specifically, we present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). The activity we want to predict is the biodegradability of chemical compounds in water. In particular, the target variable is the half-life for aerobic aqueous biodegradation. Structural descriptions of chemicals in terms of atoms and bonds are derived from the chemicals' SMILES encodings. The definition of substructures is used as background knowledge. Predicting biodegradability is essentially a regression problem, but we also consider a discretized version of the target variable. We thus employ a number of relational classification and regression methods on the relational representation and compare these to propositional methods applied to different propositionalizations of the problem. We also experiment with a prediction technique that consists of merging upper and lower bound predictions into one prediction. Some conclusions are drawn concerning the applicability of machine learning systems and the merging technique in this domain and the evaluation of hypotheses.

text: Experiments in Predicting Biodegradability This paper is concerned with the use of AI techniques in ecology. More specifically, we present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). The activity we want to predict is the biodegradability of chemical compounds in water. In particular, the target variable is the half-life for aerobic aqueous biodegradation. Structural descriptions of chemicals in terms of atoms and bonds are derived from the chemicals' SMILES encodings. The definition of substructures is used as background knowledge. Predicting biodegradability is essentially a regression problem, but we also consider a discretized version of the target variable. We thus employ a number of relational classification and regression methods on the relational representation and compare these to propositional methods applied to different propositionalizations of the problem. We also experiment with a prediction technique that consists of merging upper and lower bound predictions into one prediction. Some conclusions are drawn concerning the applicability of machine learning systems and the merging technique in this domain and the evaluation of hypotheses.

year: 2004

title: A goodness of fit measure related to r² for model performance assessment

authors: Bardsley, W. Earl

subjects: fit

abstract: Checking the predictive worth of an environmental model inevitably includes a goodness of fit metric to quantify the degree of matching to recorded data, thereby giving a measure of model performance. Considerable analysis and discussion have taken place over fit indices in hydrology, but a neglected aspect is the degree of communicability to other disciplines. It is suggested that a fit index is best communicated to colleagues via reference to models giving unbiased predictions, because unbiased environmental models are a desirable goal across disciplines. That is, broad recognition of a fit index is aided if it simplifies in the unbiased case to a familiar and logical expression. This does not hold for the Nash–Sutcliffe Efficiency E which reduces to the somewhat awkward unbiased expression E = 2 – 1/r², where r² is the coefficient of determination. A new goodness of fit index V is proposed for model validation as V =  r²/(2-E), which simplifies to the easily communicated V = r4 in the unbiased case. The index is defined over the range 0 ≤ V ≤ 1, and it happens that V < E for larger values of E. Some synthetic and recorded data sets are used to illustrate characteristics of V in comparison to E

text: A goodness of fit measure related to r² for model performance assessment Checking the predictive worth of an environmental model inevitably includes a goodness of fit metric to quantify the degree of matching to recorded data, thereby giving a measure of model performance. Considerable analysis and discussion have taken place over fit indices in hydrology, but a neglected aspect is the degree of communicability to other disciplines. It is suggested that a fit index is best communicated to colleagues via reference to models giving unbiased predictions, because unbiased environmental models are a desirable goal across disciplines. That is, broad recognition of a fit index is aided if it simplifies in the unbiased case to a familiar and logical expression. This does not hold for the Nash–Sutcliffe Efficiency E which reduces to the somewhat awkward unbiased expression E = 2 – 1/r², where r² is the coefficient of determination. A new goodness of fit index V is proposed for model validation as V =  r²/(2-E), which simplifies to the easily communicated V = r4 in the unbiased case. The index is defined over the range 0 ≤ V ≤ 1, and it happens that V < E for larger values of E. Some synthetic and recorded data sets are used to illustrate characteristics of V in comparison to E

year: 2013

title: Whanau Maori explain how the Harti Hauora Tool assists with better access to health services (vol 25, pg 471, 2019)

authors: Masters-Awatere, Bridgette

subjects: Science & Technology

abstract: In this paper, whānau Māori highlight how a Kaupapa Māori-centred intervention (the Harti Hauora Tamariki tool, hereafter Harti tool) has improved interactions with health services. The Harti tool is undergoing a randomised control trial (RCT) at Waikato Hospital in New Zealand. As part of the RCT, the authors engaged in a series of qualitative interviews with whānau members of tamariki Māori (children aged 0–5 years) admitted to Waikato Hospital’s paediatric ward. Whānau who met at least one criteria for New Zealand’s domains of deprivation were included. Using a Kaupapa Māori approach to the study, participants shared their views on barriers and facilitators to accessing health resources and primary care services. The interviews conducted highlight how the Harti tool, when administered in a culturally appropriate and respectful manner that prioritised relationship-building, enabled better connection to healthcare services. Prevalent in our analysis were connections to wider determinants of health and ways to reduce existing health inequities. To conclude the paper, how the Harti tool has enhanced feelings of being in control of health, with the potential to reduce the likelihood of a hospital readmission, is highlighted.

text: Whanau Maori explain how the Harti Hauora Tool assists with better access to health services (vol 25, pg 471, 2019) In this paper, whānau Māori highlight how a Kaupapa Māori-centred intervention (the Harti Hauora Tamariki tool, hereafter Harti tool) has improved interactions with health services. The Harti tool is undergoing a randomised control trial (RCT) at Waikato Hospital in New Zealand. As part of the RCT, the authors engaged in a series of qualitative interviews with whānau members of tamariki Māori (children aged 0–5 years) admitted to Waikato Hospital’s paediatric ward. Whānau who met at least one criteria for New Zealand’s domains of deprivation were included. Using a Kaupapa Māori approach to the study, participants shared their views on barriers and facilitators to accessing health resources and primary care services. The interviews conducted highlight how the Harti tool, when administered in a culturally appropriate and respectful manner that prioritised relationship-building, enabled better connection to healthcare services. Prevalent in our analysis were connections to wider determinants of health and ways to reduce existing health inequities. To conclude the paper, how the Harti tool has enhanced feelings of being in control of health, with the potential to reduce the likelihood of a hospital readmission, is highlighted.

year: 2019